Search

We consider a model for the Antarctic Circumpolar Current in rotating spherical coordinates. After establishing global-in-time existence and uniqueness of classical solutions, we turn our attention to the issue of stability of a class of steady zonal solutions (i.e., time-independent solutions that vary only with latitude). By identifying suitable conserved quantities and combining them to construct a Lyapunov function, we prove a stability result.

As a variety of automated collision prevention systems gain presence within personal vehicles, rating and differentiating the automated safety performance of car models has become increasingly important for consumers, manufacturers, and insurers. In 2023, Swiss Re and partners initiated an eight-month long vehicle testing campaign conducted on a recognized UNECE type approval authority and Euro NCAP accredited proving ground in Germany. The campaign exposed twelve mass-produced vehicle models and one prototype vehicle fitted with collision prevention systems to a selection of safety-critical traffic scenarios representative of United States and European Union accident landscape. In this paper, we compare and evaluate the relative safety performance of these thirteen collision prevention systems (hardware and software stack) as demonstrated by this testing campaign. We first introduce a new scoring system which represents a test system's predicted impact on overall real-world collision frequency and reduction of collision impact energy, weighted based on the real-world relevance of the test scenario. Next, we introduce a novel metric that quantifies the realism of the protocol and confirm that our test protocol is a plausible representation of real-world driving. Finally, we find that the prototype system in its pre-release state outperforms the mass-produced (post-consumer-release) vehicles in the majority of the tested scenarios on the test track.

The Lattice Boltzmann Method (LBM) is a computational technique of Computational Fluid Dynamics (CFD) that has gained popularity due to its high parallelism and ability to handle complex geometries with minimal effort. Although LBM frameworks are increasingly important in various industries and research fields, their complexity makes them difficult to modify and can lead to suboptimal performance. This paper presents miniLB, the first, to the best of our knowledge, SYCL-based LBM mini-app.miniLB addresses the need for a performance-portable LBM proxy app capable of abstracting complex fluid dynamics simulations across heterogeneous computing systems. We analyze SYCL semantics for performance portability and evaluate miniLB on multiple GPU architectures using various SYCL implementations. Our results, compared against a manually-tuned FORTRAN version, demonstrate effectiveness of miniLB in assessing LBM performance across diverse hardware, offering valuable insights for optimizing large-scale LBM frameworks in modern computing environments., Comment: 12 pages, conference

After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Graph theory methods have shown that brain connectivity undergoes frequency-dependent reorganization post-stroke, adapting to new conditions. Traditional methods often rely on handcrafted features that may not capture the complexities of clinical phenomena. In this study, we propose a novel approach using Graph Neural Networks (GNNs) to predict stroke severity, as measured by the NIH Stroke Scale (NIHSS). We analyzed electroencephalography (EEG) recordings from 71 patients at the time of hospitalization. For each patient, we generated five graphs weighted by Lagged Linear Coherence (LLC) between signals from distinct Brodmann Areas, covering $\delta$ (2-4 Hz), $\theta$ (4-8 Hz), $\alpha_1$ (8-10.5 Hz), $\alpha_2$ (10.5-13 Hz), and $\beta_1$ (13-20 Hz) frequency bands. To emphasize key neurological connections and maintain sparsity, we applied a sparsification process based on structural and functional brain network properties. We then trained a graph attention model to predict the NIHSS. By examining its attention coefficients, our model reveals insights into brain reconfiguration, providing clinicians with a valuable tool for diagnosis, personalized treatment, and early intervention in neurorehabilitation., Comment: 7 pages, 7 figures

We investigate the long time dynamics of the nonlinear Schr\"odinger equation (NLS) with combined powers on the waveguide manifold $\mathbb{R}^d\times\mathbb{T}$. Different from the previously studied NLS-models with single power on the waveguide manifolds, where the non-scale-invariance is mainly due to the mixed nature of the underlying domain, the non-scale-invariance of the present model is both geometrical and structural. By considering different combinations of the nonlinearities, we establish both qualitative and quantitative properties of the soliton, scattering and blow-up solutions. As one of the main novelties of the paper compared to the previous results for the NLS with single power, we particularly construct two different rescaled families of variational problems, which leads to an NLS with single power in different limiting profiles respectively, to establish the periodic dependence results., Comment: arXiv admin note: text overlap with arXiv:2205.04969

In this paper we analyse the spectro-photometric properties of the Type II supernova \sn, exploded at a distance of $19.9\,\rm{Mpc}$, in NGC~3206. Its early spectra are characterised by narrow high-ionisation emission lines, often interpreted as signatures of ongoing interaction between rapidly expanding ejecta and a confined dense circumstellar medium. However, we provide a model of the bolometric light curve of the transient that does not require sources of energy different than the H recombination and radioactive decays. Our model can reproduce the bolometric light curve of SN~2024bch adopting an ejected mass of $M_{bulk}\simeq5$\msun~surrounded by an extended envelope of only 0.2\msun~with an outer radius $R_{env}=7.0\times10^{13}\,\rm{cm}$. An accurate modelling focused on the radioactive part of the light curve, which accounts for incomplete $\gamma-$ray trapping, gives a $^{56}\rm{Ni}$ mass of 0.048\msun. We propose narrow lines to be powered by Bowen fluorescence induced by scattering of \ion{He}{II} Ly$\alpha$ photons, resulting in the emission of high-ionisation resonance lines. Simple light travel time calculations based on the maximum phase of the narrow emission lines place the inner radius of the H-rich, un-shocked shell at a radius $\simeq4.4\times10^{15}\,\rm{cm}$, compatible with an absence of ejecta-CSM interaction during the first weeks of evolution. Possible signatures of interaction appear only $\sim69\,\rm{days}$ after explosion, although the resulting conversion of kinetic energy into radiation does not seem to contribute significantly to the total luminosity of the transient., Comment: 13 pages, 11 figures, submitted to A&A

Hilbert space dimension is a key resource for quantum information processing. A large Hilbert space is not only an essential requirement for quantum error correction, but it can also be advantageous for realizing gates and algorithms more efficiently. There has thus been considerable experimental effort in recent years to develop quantum computing platforms using qudits (d-dimensional quantum systems with d>2) as the fundamental unit of quantum information. Just as with qubits, quantum error correction of these qudits will be necessary in the long run, but to date error correction of logical qudits has not been demonstrated experimentally. Here we report the experimental realization of an error-corrected logical qutrit (d=3) and ququart (d=4) by employing the Gottesman-Kitaev-Preskill (GKP) bosonic code. Using a reinforcement learning agent, we optimize the GKP qutrit (ququart) as a ternary (quaternary) quantum memory and achieve beyond break-even error correction with a gain of 1.82 +/- 0.03 (1.87 +/- 0.03). This work represents a new way of leveraging the large Hilbert space of a harmonic oscillator for hardware-efficient quantum error correction.

We characterise the digraphs for which the multipaths, that is disjoint unions of directed paths, yield a matroid. For such graphs, called MP-digraphs, we prove that the Tutte polynomial of the multipath matroid is related to counting certain digraph colourings. Finally, we prove that, for MP-forests, the decategorification of the multipath cohomology yields a specialisation of the Tutte polynomial., Comment: 23 pages, 15 figures, comments are welcome

The intensity noise of a laser source represents one of the key factors limiting the ultimate sensitivity in laser-based systems for sensing and telecommunication. For advanced applications based on interferometry, the availability of a shot-noise-limited local oscillator is even more important for the effective feasibility of high-precision measurements. This is particularly crucial in quantum optics applications based on homodyne detection schemes to measure non-classical light states, such as squeezed states. This work deeply investigates and analyzes the intensity noise features of the most widely used mid-infrared semiconductor heterostructured lasers: quantum cascade and interband cascade lasers. For this purpose, a comprehensive comparison of three different continuous-wave lasers operating at room temperature around 4.5 {\mu}m wavelength is presented. First, a thorough electro-optical characterization is given, highlighting the differences and the shared common characteristics of the tested devices. Then, a detailed intensity noise analysis is reported, identifying their different noise operations with a particular reference to shot-noise-limited operations. Finally, some perspectives towards advanced applications are discussed., Comment: 17 pages, 5 figures

The paper addresses task assignment and trajectory generation for collaborative inspection missions using a fleet of multi-rotors, focusing on the wind turbine inspection scenario. The proposed solution enables safe and feasible trajectories while accommodating heterogeneous time-bound constraints and vehicle physical limits. An optimization problem is formulated to meet mission objectives and temporal requirements encoded as Signal Temporal Logic (STL) specifications. Additionally, an event-triggered replanner is introduced to address unforeseen events and compensate for lost time. Furthermore, a generalized robustness scoring method is employed to reflect user preferences and mitigate task conflicts. The effectiveness of the proposed approach is demonstrated through MATLAB and Gazebo simulations, as well as field multi-robot experiments in a mock-up scenario., Comment: \c{opyright}2025 Elsevier. This work has been accepted to "Robotics and Autonomous Systems" for possible publication. Personal use of this material is permitted. Permission from Elsevier must be obtained for all other uses

Chalcogenide optical phase change materials (PCMs) have garnered significant interest for their growing applications in programmable photonics, optical analog computing, active metasurfaces, and beyond. Limited endurance or cycling lifetime is however increasingly becoming a bottleneck toward their practical deployment for these applications. To address this issue, we performed a systematic study elucidating the cycling failure mechanisms of Ge$_2$Sb$_2$Se$_4$Te (GSST), a common optical PCM tailored for infrared photonic applications, in an electrothermal switching configuration commensurate with their applications in on-chip photonic devices. We further propose a set of design rules building on insights into the failure mechanisms, and successfully implemented them to boost the endurance of the GSST device to over 67,000 cycles.

This paper introduces the Neural Transcoding Vision Transformer (\modelname), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) samples from simultaneous Electroencephalography (EEG) data. A key feature of \modelname is its Domain Matching (DM) sub-module which effectively aligns the latent EEG representations with those of fMRI volumes, enhancing the model's accuracy and reliability. Unlike previous methods that tend to struggle with fidelity and reproducibility of images, \modelname addresses these challenges by ensuring methodological integrity and higher-quality reconstructions which we showcase through extensive evaluation on two benchmark datasets; \modelname outperforms the current state-of-the-art by a significant margin in both cases, e.g. achieving a $10\times$ reduction in RMSE and a $3.14\times$ increase in SSIM on the Oddball dataset. An ablation study also provides insights into the contribution of each component to the model's overall effectiveness. This development is critical in offering a new approach to lessen the time and financial constraints typically linked with high-resolution brain imaging, thereby aiding in the swift and precise diagnosis of neurological disorders. Although it is not a replacement for actual fMRI but rather a step towards making such imaging more accessible, we believe that it represents a pivotal advancement in clinical practice and neuroscience research. Code is available at \url{https://github.com/rom42pla/ntvit}., Comment: ECCV24 Workshop on Synthetic Data for Computer Vision

Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has focused on fMRI-to-Image tasks as fMRI is characterized by high spatial resolution. However, fMRI is an expensive neuroimaging modality and does not allow for real-time BCI. On the other hand, electroencephalography (EEG) is a low-cost, non-invasive, and portable neuroimaging technique, making it an attractive option for future real-time applications. Nevertheless, EEG presents inherent challenges due to its low spatial resolution and susceptibility to noise and artifacts, which makes generating images from EEG more difficult. In this paper, we address these problems with a streamlined framework based on the ControlNet adapter for conditioning a latent diffusion model (LDM) through EEG signals. We conduct experiments and ablation studies on popular benchmarks to demonstrate that the proposed method beats other state-of-the-art models. Unlike these methods, which often require extensive preprocessing, pretraining, different losses, and captioning models, our approach is efficient and straightforward, requiring only minimal preprocessing and a few components. The code is available at https://github.com/LuigiSigillo/GWIT., Comment: Accepted at ICASSP 2025

Open-vocabulary Multiple Object Tracking (MOT) aims to generalize trackers to novel categories not in the training set. Currently, the best-performing methods are mainly based on pure appearance matching. Due to the complexity of motion patterns in the large-vocabulary scenarios and unstable classification of the novel objects, the motion and semantics cues are either ignored or applied based on heuristics in the final matching steps by existing methods. In this paper, we present a unified framework SLAck that jointly considers semantics, location, and appearance priors in the early steps of association and learns how to integrate all valuable information through a lightweight spatial and temporal object graph. Our method eliminates complex post-processing heuristics for fusing different cues and boosts the association performance significantly for large-scale open-vocabulary tracking. Without bells and whistles, we outperform previous state-of-the-art methods for novel classes tracking on the open-vocabulary MOT and TAO TETA benchmarks. Our code is available at \href{https://github.com/siyuanliii/SLAck}{github.com/siyuanliii/SLAck}., Comment: ECCV2024

Extended Berkeley Packet Filter (eBPF) is a runtime that enables users to load programs into the operating system (OS) kernel, like Linux or Windows, and execute them safely and efficiently at designated kernel hooks. Each program passes through a verifier that reasons about the safety guarantees for execution. Hosting a safe virtual machine runtime within the kernel makes it dynamically programmable. Unlike the popular approach of bypassing or completely replacing the kernel, eBPF gives users the flexibility to modify the kernel on the fly, rapidly experiment and iterate, and deploy solutions to achieve their workload-specific needs, while working in concert with the kernel. In this paper, we present the first comprehensive description of the design and implementation of the eBPF runtime in the Linux kernel. We argue that eBPF today provides a mature and safe programming environment for the kernel. It has seen wide adoption since its inception and is increasingly being used not just to extend, but program entire components of the kernel, while preserving its runtime integrity. We outline the compelling advantages it offers for real-world production usage, and illustrate current use cases. Finally, we identify its key challenges, and discuss possible future directions., Comment: 22 pages, 6 figures

This work introduces a framework to diagnose the strengths and shortcomings of Autonomous Vehicle (AV) collision avoidance technology with synthetic yet realistic potential collision scenarios adapted from real-world, collision-free data. Our framework generates counterfactual collisions with diverse crash properties, e.g., crash angle and velocity, between an adversary and a target vehicle by adding perturbations to the adversary's predicted trajectory from a learned AV behavior model. Our main contribution is to ground these adversarial perturbations in realistic behavior as defined through the lens of data-alignment in the behavior model's parameter space. Then, we cluster these synthetic counterfactuals to identify plausible and representative collision scenarios to form the basis of a test suite for downstream AV system evaluation. We demonstrate our framework using two state-of-the-art behavior prediction models as sources of realistic adversarial perturbations, and show that our scenario clustering evokes interpretable failure modes from a baseline AV policy under evaluation.

In this work, we propose a novel and efficient method for smoothing polylines in motion planning tasks. The algorithm applies to motion planning of vehicles with bounded curvature. In the paper, we show that the generated path: 1) has minimal length, 2) is $G^1$ continuous, and 3) is collision-free by construction, if the hypotheses are respected. We compare our solution with the state-of.the-art and show its convenience both in terms of computation time and of length of the compute path.

High-quality element distribution maps enable precise analysis of the material composition and condition of Old Master paintings. These maps are typically produced from data acquired through Macro X-ray fluorescence (MA-XRF) scanning, a non-invasive technique that collects spectral information. However, MA-XRF is often limited by a trade-off between acquisition time and resolution. Achieving higher resolution requires longer scanning times, which can be impractical for detailed analysis of large artworks. Super-resolution MA-XRF provides an alternative solution by enhancing the quality of MA-XRF scans while reducing the need for extended scanning sessions. This paper introduces a tailored super-resolution approach to improve MA-XRF analysis of Old Master paintings. Our method proposes a novel adversarial neural network architecture for MA-XRF, inspired by the Learned Iterative Shrinkage-Thresholding Algorithm. It is specifically designed to work in an unsupervised manner, making efficient use of the limited available data. This design avoids the need for extensive datasets or pre-trained networks, allowing it to be trained using just a single high-resolution RGB image alongside low-resolution MA-XRF data. Numerical results demonstrate that our method outperforms existing state-of-the-art super-resolution techniques for MA-XRF scans of Old Master paintings.

Planet-planet occultations (PPOs) occur when one exoplanet occults another exoplanet in the same system as seen from the Earth's vantage point. PPOs may provide a unique opportunity to observe radio "spillover" from extraterrestrial intelligences' (ETIs) radio transmissions or radar being transmitted from the further exoplanet towards the nearer one for the purposes of communication or scientific exploration. Planetary systems with many tightly packed, low-inclination planets, such as TRAPPIST-1, are predicted to have frequent PPOs. Here, the narrowband technosignature search code turboSETI was used in combination with the newly developed NbeamAnalysis filtering pipeline to analyze 28 hours of beamformed data taken with the Allen Telescope Array (ATA) during late October and early November 2022, from 0.9--9.3~GHz, targeting TRAPPIST-1. During this observing window, 7 possible PPO events were predicted using the NbodyGradient code. The filtering pipeline reduced the original list of 25 million candidate signals down to 6 million by rejecting signals that were not sky-localized and, from these, identified a final list of 11127 candidate signals above a power law cutoff designed to segregate signals by their attenuation and morphological similarity between beams. All signals were plotted for visual inspection, 2264 of which were found to occur during PPO windows. We report no detection of signals of non-human origin, with upper limits calculated for each PPO event exceeding EIRPs of 2.17--13.3 TW for minimally drifting signals and 40.8--421 TW in the maximally drifting case. This work constitutes the longest single-target radio SETI search of TRAPPIST-1 to date.

We consider a Bianchi I geometry of the Universe. We obtain a cosmic shear expression related with the eccentricity of the Universe. In particular we study the connection among cosmic shear, eccentricity and CMB. The equation are self-contained with only two parameters., Comment: 11 pages, accepted for publication on Universe

Enhanced sampling simulations make the computational study of rare events feasible. A large family of such methods crucially depends on the definition of some collective variables (CVs) that could provide a low-dimensional representation of the relevant physics of the process. Recently, many methods have been proposed to semi-automatize the CV design by using machine learning tools to learn the variables directly from the simulation data. However, most methods are based on feed-forward neural networks and require as input some user-defined physical descriptors. Here, we propose to bypass this step using a graph neural network to directly use the atomic coordinates as input for the CV model. This way, we achieve a fully automatic approach to CV determination that provides variables invariant under the relevant symmetries, especially the permutational one. Furthermore, we provide different analysis tools to favor the physical interpretation of the final CV. We prove the robustness of our approach using different methods from the literature for the optimization of the CV, and we prove its efficacy on several systems, including a small peptide, an ion dissociation in explicit solvent, and a simple chemical reaction.

Optical flow, inspired by the mechanisms of biological visual systems, calculates spatial motion vectors within visual scenes that are necessary for enabling robotics to excel in complex and dynamic working environments. However, current optical flow algorithms, despite human-competitive task performance on benchmark datasets, remain constrained by unacceptable time delays (~0.6 seconds per inference, 4X human processing speed) in practical deployment. Here, we introduce a neuromorphic optical flow approach that addresses delay bottlenecks by encoding temporal information directly in a synaptic transistor array to assist spatial motion analysis. Compared to conventional spatial-only optical flow methods, our spatiotemporal neuromorphic optical flow offers the spatial-temporal consistency of motion information, rapidly identifying regions of interest in as little as 1-2 ms using the temporal motion cues derived from the embedded temporal information in the two-dimensional floating gate synaptic transistors. Thus, the visual input can be selectively filtered to achieve faster velocity calculations and various task execution. At the hardware level, due to the atomically sharp interfaces between distinct functional layers in two-dimensional van der Waals heterostructures, the synaptic transistor offers high-frequency response (~100 {\mu}s), robust non-volatility (>10000 s), and excellent endurance (>8000 cycles), enabling robust visual processing. In software benchmarks, our system outperforms state-of-the-art algorithms with a 400% speedup, frequently surpassing human-level performance while maintaining or enhancing accuracy by utilizing the temporal priors provided by the embedded temporal information., Comment: 22 pages, 6 figures

A ring of capacitively-coupled transmons threaded by a synthetic magnetic field is studied as a realization of a strongly interacting bosonic system. The synthetic flux is imparted through a specific Floquet modulation scheme based on a suitable periodic sequence of Lorentzian pulses that are known as `Levitons'. Such scheme has the advantage to preserve the translation invariance of the system and to work at the qubits sweet spots. We employ this system to demonstrate the concept of fractional values of flux quanta. Although such fractionalization phenomenon was originally predicted for bright solitons in cold atoms, it may be in fact challenging to access with that platform. Here, we show how fractional flux quanta can be read-out in the absorption spectrum of a suitable 'scattering experiment' in which the qubit ring is driven by microwaves., Comment: Main: 5 pages + 3 figures, Sup. Mat: 9 pages + 6 figures

Van der Waals monolayers featuring magnetic states provide a fundamental building block for artificial quantum matter. Here, we establish the emergence of a multicomponent ground state featuring magneto-orbital excitations of the 3d2-transition metal trihalide VCl3 monolayer. We show that monolayer VCl3 realizes a ground state with simultaneous magnetic and orbital ordering using density functional theory. Using first-principles methods we derive an effective Hamiltonian with intertwined spin and orbital degree of freedom, which we demonstrate can be tuned by strain. We show that magneto-orbitons appear as the collective modes of this complex order, and arise from coupled orbiton magnon excitations due to the magneto-orbital coupling in the system. Our results establish VCl3 as a promising 2D material to observe emergent magneto-orbital excitations and provide a platform for multicomponent symmetry breaking., Comment: 7 pages, 3 figures

Teleoperation of humanoid robots has long been a challenging domain, necessitating advances in both hardware and software to achieve seamless and intuitive control. This paper presents an integrated solution based on several elements: calibration-free motion capture and retargeting, low-latency fast whole-body kinematics streaming toolbox and high-bandwidth cycloidal actuators. Our motion retargeting approach stands out for its simplicity, requiring only 7 IMUs to generate full-body references for the robot. The kinematics streaming toolbox, ensures real-time, responsive control of the robot's movements, significantly reducing latency and enhancing operational efficiency. Additionally, the use of cycloidal actuators makes it possible to withstand high speeds and impacts with the environment. Together, these approaches contribute to a teleoperation framework that offers unprecedented performance. Experimental results on the humanoid robot Nadia demonstrate the effectiveness of the integrated system.

The problem of obtaining spectral densities from lattice data has been receiving great attention due to its importance in our understanding of scattering processes in Quantum Field Theory, with applications both in the Standard Model and beyond. The problem is notoriously difficult as it amounts to performing an inverse Laplace transform, starting from a finite set of noisy data. Several strategies are now available to tackle this inverse problem. In this work, we discuss how Backus-Gilbert methods, in particular the variation introduced by some of the authors, relate to the solution based on Gaussian Processes. Both methods allow computing spectral densities smearing with a kernel, whose features depend on the detail of the algorithm. We will discuss such kernel, and show how Backus-Gilbert methods can be understood in a Bayesian fashion. As a consequence of this correspondence, we are able to interpret the algorithmic parameters of Backus-Gilbert methods as hyperparameters in the Bayesian language, which can be chosen by maximising a likelihood function. By performing a comparative study on lattice data, we show that, when both frameworks are set to compute the same quantity, the results are generally in agreement. Finally, we adopt a strategy to systematically validate both methodologies against pseudo-data, using covariance matrices measured from lattice simulations. In our setup, we find that the determination of the algorithmic parameters based on a stability analysis provides results that are, on average, more conservative than those based on the maximisation of a likelihood function.

Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-standard MCMC techniques to achieve both speed and accuracy when performing inference on many observed datasets. Our approach uses principled diagnostics to guide the choice of inference method for each dataset, moving along the Pareto front from fast amortized sampling to slower but guaranteed-accurate MCMC when necessary. By reusing computations across steps, our workflow creates synergies between amortized and MCMC-based inference. We demonstrate the effectiveness of this integrated approach on a generalized extreme value task with 1000 observed data sets, showing 90x time efficiency gains while maintaining high posterior quality., Comment: Extended Abstract

We report the observation of Shapiro steps in a periodically driven Josephson junction between strongly-interacting Fermi superfluids of ultracold atoms. We observe quantized plateaus in the current-potential characteristics, the height and width of which mirror the external drive frequency and the junction nonlinear response. Direct measurements of the current-phase relationship showcase how Shapiro steps arise from the synchronization between the relative phase of the two reservoirs and the external drive. Such mechanism is further supported by the detection of periodic phase-slippage processes, in the form of vortex-antivortex pairs. Our results are corroborated by a circuital model and numerical simulations, overall providing a clear understanding of Shapiro dynamics in atomic Fermi superfluids. Our work demonstrates phase-coherent and synchronization effects in driven strongly-interacting superfluids, opening prospects for studying emergent non-equilibrium dynamics in quantum many-body systems under external drives.

The current-voltage characteristic of a driven superconducting Josephson junction displays discrete steps. This phenomenon, discovered by Sydney Shapiro, forms today's voltage standard. Here, we report the observation of Shapiro steps in a driven Josephson junction in a gas of ultracold atoms. We demonstrate that the steps exhibit universal features, and provide key insight into the microscopic dissipative dynamics that we directly observe in the experiment. Most importantly, the steps are directly connected to phonon emission and soliton nucleation. The experimental results are underpinned by extensive numerical simulations based on classical-field dynamics and represent the transfer of the voltage standard to the realm of ultracold quantum gases., Comment: Update reference

'Bare' active galactic nuclei (AGN) are a subclass of Type 1 AGN that show little or no intrinsic absorption. They offer an unobscured view of the central regions of the AGN and therefore serve as ideal targets to study the relativistic reflection features originating from the innermost regions of the accretion disc. We present a detailed broadband spectral analysis ($0.3 - 70$ keV) of one of the most luminous bare AGN in the local universe, RBS 1124 ($z= 0.208$) using a new, co-ordinated high signal-to-noise observation obtained by $\textit{XMM-Newton}$ and $\textit{NuSTAR}$. The source exhibits a power-law continuum with $\Gamma \sim$ 1.8 along with a soft excess below 2 keV, a weak neutral iron line and curvature at high energies ($\sim 30$ keV). The broadband spectrum, including the soft excess and the high-energy continuum, is well fit by the relativistic reflection model when the accretion disc is allowed to have densities of log$(n_{\rm e}$/cm$^{-3}$) $\gtrsim 19.2$. Our analysis therefore suggests that when high-density effects are considered, relativistic reflection remains a viable explanation for the soft excess., Comment: 14 pages, 9 figures, accepted for publication in MNRAS

We demonstrate spectroscopy of incoherent light with sub-diffraction resolution. In a proof-of-principle experiment we analyze the spectrum of a pair of incoherent point-like sources whose separation is below the diffraction limit. The two sources mimic a planetary system, with a brighter source for the star and a dimmer one for the planet. Acquiring spectral information about the secondary source is hard because the two images have a substantial overlap. This limitation is solved by leveraging a structured measurement based on spatial-mode demultiplexing, where light is first sorted in its Hermite-Gaussian components in the transverse field, then measured by photon detection. This allows us to effectively decouple the photons coming from the two sources. An application is suggested to enhance exoplanets' atmosphere spectroscopy. A number of experiments of super-resolution imaging based on spatial demultiplexing have been conducted in the past few years, with promising results. Here, for the first time to the best of our knowledge, we extend this concept to the domain of spectroscopy., Comment: 11 pages, 6 figures, comments welcome

Patterns of human motion in outdoor and indoor environments are substantially different due to the scope of the environment and the typical intentions of people therein. While outdoor trajectory forecasting has received significant attention, indoor forecasting is still an underexplored research area. This paper proposes SITUATE, a novel approach to cope with indoor human trajectory prediction by leveraging equivariant and invariant geometric features and a self-supervised vision representation. The geometric learning modules model the intrinsic symmetries and human movements inherent in indoor spaces. This concept becomes particularly important because self-loops at various scales and rapid direction changes often characterize indoor trajectories. On the other hand, the vision representation module is used to acquire spatial-semantic information about the environment to predict users' future locations more accurately. We evaluate our method through comprehensive experiments on the two most famous indoor trajectory forecasting datasets, i.e., TH\"OR and Supermarket, obtaining state-of-the-art performance. Furthermore, we also achieve competitive results in outdoor scenarios, showing that indoor-oriented forecasting models generalize better than outdoor-oriented ones. The source code is available at https://github.com/intelligolabs/SITUATE., Comment: Accepted at the 27th International Conference on Pattern Recognition (ICPR 2024)

Protection of data-in-use is a key priority, for which Trusted Execution Environment (TEE) technology has unarguably emerged as a, possibly the most, promising solution. Multiple server-side TEE offerings have been released over the years, exhibiting substantial differences with respect to several aspects. The first comer was Intel SGX, which featured Process-based TEE protection, an efficient yet difficult to use approach. Some SGX limitations were (partially) overcome by runtimes, notably: Gramine, Scone, and Occlum. A major paradigm shift was later brought by AMD SEV, with VM-based TEE protection, which enabled lift-and-shift deployment of legacy applications. This new paradigm has been implemented by Intel only recently, in TDX. While the threat model of the aforementioned TEE solutions has been widely discussed, a thorough performance comparison is still lacking in the literature. This paper provides a comparative evaluation of TDX, SEV, Gramine-SGX, and Occlum-SGX. We study computational overhead and resource usage, under different operational scenarios and using a diverse suite of legacy applications. By doing so, we provide a reliable performance assessment under realistic conditions. We explicitly emphasize that, at the time of writing, TDX was not yet available to the public. Thus, the evaluation of TDX is a unique feature of this study., Comment: Under review at IEEE Transactions on Dependable and Secure Computing