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Several reports about infinite-layer nickelate thin films suggest that the superconducting critical temperature versus chemical doping phase diagram has a dome-like shape, similar to cuprates. Here, we demonstrate a highly reproducible superconducting state in undoped PrNiO$_2$ thin films grown onto SrTiO$_3$. Scanning transmission electron microscopy measurements demonstrate coherent and defect-free infinite-layer phase, a high structural quality with no unintentional chemical doping and a total absence of interstitial oxygen. X-ray absorption measurements show very sharp features at the Ni L$_{3,2}$-edges with a large linear dichroism, indicating the preferential hole-occupation of Ni$^{1+}$-3d$_{x^2-y^2}$ orbitals in a square planar geometry. Resonant inelastic X-ray scattering measurements reveal sharp magnon excitations of 200\,meV energy at magnetic Brillouin zone boundary, highly resonant at the Ni$^{1+}$ absorption peak. The results indicate that, when properly stabilized, infinite-layer nickelate thin films are superconducting without chemical doping.

After the recent COVID-19 outbreaks, it became increasingly evident that individuals' thoughts and beliefs can have a strong impact on disease transmission. It becomes therefore important to understand how information and opinions on protective measures evolve during epidemics. To this end, incorporating the impact of social media is essential to take into account the hierarchical structure of these platforms. In this context, we present a novel approach to take into account the interplay between infectious disease dynamics and socially-structured opinion dynamics. Our work extends a conventional compartmental framework including behavioral attitudes in shaping public opinion and promoting the adoption of protective measures under the influence of different degrees of connectivity. The proposed approach is capable to reproduce the emergence of epidemic waves. Specifically, it provides a clear link between the social influence of highly connected individuals and the epidemic dynamics. Through a heterogeneity of numerical tests we show how this comprehensive framework offers a more nuanced understanding of epidemic dynamics in the context of modern information dissemination and social behavior.

Polynomial reproduction plays a relevant role in deriving error estimates for various approximation schemes. Local reproduction in a quasi-uniform setting is a significant factor in the estimation of error and the assessment of stability but for some computationally relevant schemes, such as Rescaled Localized Radial Basis Functions (RL-RBF), it becomes a limitation. To facilitate the study of a greater variety of approximation methods in a unified and efficient manner, this work proposes a framework based on fast decaying polynomial reproduction: we do not restrict to compactly supported basis functions, but we allow the basis function decay to infinity as a function of the separation distance. Implementing fast decaying polynomial reproduction provides stable and convergent methods, that can be smooth when approximating by moving least squares otherwise very efficient in the case of linear programming problems. All the results presented in this paper concerning the rate of convergence, the Lebesgue constant, the smoothness of the approximant, and the compactness of the support have been verified numerically, even in the multivariate setting.

Computing a Euclidean minimum spanning tree of a set of points is a seminal problem in computational geometry and geometric graph theory. We combine it with another classical problem in graph drawing, namely computing a monotone geometric representation of a given graph. More formally, given a finite set $S$ of points in the plane and a finite set $\cal D$ of directions, a geometric spanning tree $T$ with vertex set $S$ is ${\cal D}$-monotone if, for every pair $\{u,v\}$ of vertices of $T$, there exists a direction $d \in \cal D$ for which the unique path from $u$ to $v$ in $T$ is monotone with respect to $d$. We provide a characterization of ${\cal D}$-monotone spanning trees. Based on it, we show that a ${\cal D}$-monotone spanning tree of minimum length can be computed in polynomial time if the number $k=|{\cal D}|$ of directions is fixed, both when (i) the set ${\cal D}$ of directions is prescribed and when (ii) the objective is to find a minimum-length ${\cal D}$-monotone spanning tree over all sets ${\cal D}$ of $k$ directions. For $k = 2$, we describe algorithms that are much faster than those for the general case. Furthermore, in contrast to the classical Euclidean minimum spanning tree, whose vertex degree is at most six, we show that for every even integer $k$, there exists a point set $S_k$ and a set $\cal D$ of $k$ directions such that any minimum-length $\cal D$-monotone spanning tree of $S_k$ has maximum vertex degree $2k$., Comment: To appear in Proc. 50th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM 2025)

Studying the distribution and properties of ionised gas in outflows driven by AGN is crucial for understanding the feedback mechanisms at play in extragalactic environments. In this study, we explore the connection between ionised outflows traced by rest-frame UV absorption and optical emission lines in GS133, a Compton thick AGN at z = 3.47. We combine observations from the JWST NIRSpec Integral Field Spectrograph (IFS) with archival VLT VIMOS long-slit spectroscopic data, as part of the GA-NIFS project. We perform a multi-component kinematic decomposition of the UV and optical line profiles to derive the physical properties of the absorbing and emitting gas in GS133. Our kinematic decomposition reveals two distinct components in the optical lines. The first component likely traces a rotating disk with a dynamical mass of 2e10 Msun. The second component corresponds to a galaxy-wide, bi-conical outflow, with a velocity of 1000 km/s and an extension of 3 kpc. The UV absorption lines show two outflow components, with bulk velocities v_out = -900 km/s and -1900 km/s, respectively. This characterises GS133 as a mini-BAL system. Balmer absorption lines with similar velocities are tentatively detected in the NIRSpec spectrum. Both photoionisation models and outflow energetics suggest that the ejected absorbing gas is located at 1-10 kpc from the AGN. We use 3D gas kinematic modelling to infer the orientation of the [O III] bi-conical outflow, and find that a portion of the emitting gas resides along our line of sight, suggesting that [O III] and absorbing gas clouds are partially mixed in the outflow. The derived mass-loading factor (i.e. the mass outflow rate divided by the SFR) of 1-10, and the kinetic coupling efficiency (i.e. the kinetic power divided by LAGN) of 0.1-1% per cent suggest that the outflow in GS133 provides significant feedback on galactic scales., Comment: 18 pages, 13 figures, submitted to Astronomy & Astrophysics. Comments are welcome

One of the main goals of wireless sensor networks is to permit the involved nodes to communicate with low energy budgets, as they are typically battery-powered. When such networks are employed in industrial scenarios, constraints about latency may have a significant role, too. The TSCH mechanism, and more in general TDMA schemes, rely on traffic scheduling, and consequently they can feature low power consumption and more predictable latency. Some recent proposals like PRIL-M enable further consistent energy savings, but unfortunately they cause at the same time a dramatic increase in latency. This work presents an extension of PRIL-M, we named PRIL-ML, that achieves a significantly shorter latency in exchange for a slight increase in power consumption. Its operating principles are first illustrated, then some approximate equations are provided for assessing analytically the improvements it achieves, starting from simulation results obtained for both standard TSCH and the original PRIL-M technique., Comment: preprint, 4 pages, 2024

Recently, ultrasensitive calorimeters have been proposed as a resource-efficient solution for multiplexed qubit readout in superconducting large-scale quantum processors. However, experiments demonstrating frequency multiplexing of these superconductor-normal conductor-superconductor (SNS) sensors are coarse. To this end, we present the design, fabrication, and operation of three SNS sensors with frequency-multiplexed input and probe circuits, all on a single chip. These devices have their probe frequencies in the range \SI{150}{\mega\hertz} -- \SI{200}{\mega\hertz}, which is well detuned from the heater frequencies of \SI{4.4}{\giga\hertz} -- \SI{7.6}{\giga\hertz} compatible with typical readout frequencies of superconducting qubits. Importantly, we show on-demand triggering of both individual and multiple low-noise SNS bolometers with very low cross talk. These experiments pave the way for multiplexed bolometric characterization and calorimetric readout of multiple qubits, a promising step in minimizing related resources such as the number of readout lines and microwave isolators in large-scale superconducting quantum computers., Comment: 7 pages, 4 figures

We present the study of an end-to-end multi-track reconstruction algorithm for the central drift chamber of the Belle II experiment at the SuperKEKB collider using Graph Neural Networks for an unknown number of particles. The algorithm uses detector hits as inputs without pre-filtering to simultaneously predict the number of track candidates in an event and and their kinematic properties. In a second step, we cluster detector hits for each track candidate to pass to a track fitting algorithm. Using a realistic full detector simulation including beam-induced backgrounds and detector noise taken from actual collision data, we find significant improvements in track finding efficiencies for tracks in a variety of different event topologies compared to the existing baseline algorithm used in Belle II. For events with a hypothetical long-lived massive particle with a mass in the GeV-range decaying uniformly along its flight direction into two charged particles, the GNN achieves a combined track finding and fitting efficiency of 85.4% with a fake rate of 2.5%, compared to 52.2% and 4.1% for the baseline algorithm. This is the first end-to-end multi-track machine learning algorithm for a drift chamber detector that has been utilized in a realistic particle physics environment.

One potential factor that could impede searches for the stochastic gravitational-wave background (SGWB), arising from the incoherent superposition of a multitude of weak and unresolvable gravitational-wave signals in the Universe, is correlated magnetic noise at Earth-scale distances, such as the Schumann resonances. As the sensitivity of terrestrial detectors to SGWBs increases, these effects are expected to become even more pronounced, emphasizing the importance of considering correlated noise sources in SGWB searches. In this study, we explore for the first time the impact of this class of noise on anisotropic SGWB searches. We focus on the low-frequency range of 20-200 Hz with a resolution of 1/32 Hz, using data from the LEMI-120 and Metronix MFS-06e magnetometers, located at the sites of the Advanced LIGO and Advanced Virgo interferometers, respectively, during the third observing run. These measurements were then compared with the directional upper limit derived from gravitational-wave data collected during the third observing run of the LIGO-Virgo-KAGRA collaboration. The results indicate that the magnetic noise correlations are below the interferometers' sensitivity level during the O3 observing run. The potential impact of magnetic correlations on the Advanced LIGO detectors has also been investigated at the A+ design sensitivity level., Comment: 14 pages, 6 figures

Multi-core neuromorphic processors are becoming increasingly significant due to their energy-efficient local computing and scalable modular architecture, particularly for event-based processing applications. However, minimizing the cost of inter-core communication, which accounts for the majority of energy usage, remains a challenging issue. Beyond optimizing circuit design at lower abstraction levels, an efficient multicast addressing scheme is crucial. We propose a hierarchical bit string encoding scheme that largely expands the addressing capability of state-of-the-art symbol-based schemes for the same number of routing bits. When put at work with a real neuromorphic task, this hierarchical bit string encoding achieves a reduction in area cost by approximately 29% and decreases energy consumption by about 50%.

In a laser, the control of its spectral emission depends on the physical dimensions of the optical resonator, limiting it to a set of discrete cavity modes at specific frequencies. Here, we overcome this fundamental limit by demonstrating a monolithic semiconductor laser with a continuously tunable repetition rate from 4 up to 16 GHz, by employing a microwave driving signal that induces a spatiotemporal gain modulation along the entire laser cavity, generating intracavity mode-locked pulses with a continuously tunable group velocity. At the output, frequency combs with continuously tunable mode spacings are generated in the frequency domain, and coherent pulse trains with continuously tunable repetition rates are generated in the time domain. Our results pave the way for fully tunable chip-scale lasers and frequency combs, advantageous for use in a diverse variety of fields, from fundamental studies to applications such as high-resolution and dual-comb spectroscopy., Comment: 9 pages, 5 figures

We show that axion dark matter in the range meV $\lesssim m_a\lesssim$ 100 meV can incoherently excite phonons in crystal targets with unpolarised nuclear spins. This can occur through its coupling to nuclear spins and/or through its induced time-dependent electric dipole moment in nuclei. Due to the random orientation of the nuclear spins, translation symmetry is broken in the phonon effective theory, allowing axion absorption to create phonons with unrestricted momentum. The absorption rate is therefore proportional to the phonon density of states, which generically has support across a wide range of energies, allowing for a broadband detection scheme. We calculate the absorption rate for solid $\text{H}_2$, $\text{D}_2$, $\text{Al}_2\text{O}_3$, $\text{GaAs}$, $\text{H}_2\text{O}$, $\text{D}_2\text{O}$, $\text{Be}$ and $\text{Li}_2 \text{O}$, and find that materials containing light, non-zero spin nuclei are the most promising. The predicted rates for the QCD axion are of the order of a few events / 10 kg-year exposure, setting an ambitious target for the required exposure and background suppression., Comment: 14+4 pages, 2 figures. Comments welcome!

In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits and proof-of-principle error-correction on a single logical qubit. Nevertheless, despite significant progress and excitement, the path toward a full-stack scalable technology is largely unknown. There are significant outstanding quantum hardware, fabrication, software architecture, and algorithmic challenges that are either unresolved or overlooked. These issues could seriously undermine the arrival of utility-scale quantum computers for the foreseeable future. Here, we provide a comprehensive review of these scaling challenges. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits, employing system engineering approaches, and performing distributed quantum computation within heterogeneous high-performance computing infrastructures. These opportunities for research and development could unlock certain promising applications, in particular, efficient quantum simulation/learning of quantum data generated by natural or engineered quantum systems. To estimate the true cost of such promises, we provide a detailed resource and sensitivity analysis for classically hard quantum chemistry calculations on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. Furthermore, we argue that, to tackle industry-scale classical optimization and machine learning problems in a cost-effective manner, distributed quantum-assisted probabilistic computing with custom-designed accelerators should be considered as a complementary path toward scalability., Comment: 50 pages, 42 figures

The polishing of luxury leather shoes is a delicate, labor intensive process traditionally performed by skilled craftsmen. Footwear companies aim to automate parts of this process to enhance quality, productivity, and operator well-being, but the unique nature of luxury shoe production presents challenges. This paper introduces a solution involving a collaborative robotic cell to assist in shoe polishing. A collaborative robotic manipulator, equipped with a specialized tool and governed by force control, executes the polishing tasks. Key factors such as trajectory design, applied force, polishing speed, and polish amount were analyzed. Polishing trajectories are designed using CAM software and transferred to the robot control system. Human operators design the process, supervise the robot, and perform final finishing, ensuring their expertise is integral to achieving quality. Extensive testing on various shoe models showed significant improvements in quality and reliability, leading to successful implementation on an industrial production line.

Despite modular conditions to guarantee stability for large-scale systems have been widely studied, few methods are available to tackle the case of networks with multiple equilibria. This paper introduces small-gain like sufficient conditions for 2-contraction of large-scale interconnected systems on the basis of a family of upper-bounds to the $L_2$ gains that arise from the gains computed on individual channels of the second additive variational equation. Such a condition guarantee the 2-additive compound of the system's Jacobian to be exponentially contractive, thus implying convergence towards equilibria of the system's solutions. The gains are obtained by solving suitable Linear Matrix Inequalities. Three interconnected Thomas' systems are considered in order to illustrate the application of the theory and the degree of conservatism.

We introduce LTLf+ and PPLTL+, two logics to express properties of infinite traces, that are based on the linear-time temporal logics LTLf and PPLTL on finite traces. LTLf+/PPLTL+ use levels of Manna and Pnueli's LTL safety-progress hierarchy, and thus have the same expressive power as LTL. However, they also retain a crucial characteristic of the reactive synthesis problem for the base logics: the game arena for strategy extraction can be derived from deterministic finite automata (DFA). Consequently, these logics circumvent the notorious difficulties associated with determinizing infinite trace automata, typical of LTL reactive synthesis. We present DFA-based synthesis techniques for LTLf+/PPLTL+, and show that synthesis is 2EXPTIME-complete for LTLf+ (matching LTLf) and EXPTIME-complete for PPLTL+ (matching PPLTL). Notably, while PPLTL+ retains the full expressive power of LTL, reactive synthesis is EXPTIME-complete instead of 2EXPTIME-complete. The techniques are also adapted to optimally solve satisfiability, validity, and model-checking, to get EXPSPACE-complete for LTLf+ (extending a recent result for the guarantee level using LTLf), and PSPACE-complete for PPLTL+.

Although not as wide, and popular, as that of quantum mechanics, the investigation of fundamental aspects of statistical mechanics constitutes an important research field in the building of modern physics. Besides the interest for itself, both for physicists and philosophers, and the obvious pedagogical motivations, there is a further, compelling reason for a thorough understanding of the subject. The fast development of models and methods at the edge of the established domain of the field requires indeed a deep reflection on the essential aspects of the theory, which are at the basis of its success. These elements should never be disregarded when trying to expand the domain of statistical mechanics to systems with novel, little known features. It is thus important to (re)consider in a careful way the main ingredients involved in the foundations of statistical mechanics. Among those, a primary role is covered by the dynamical aspects (e.g. presence of chaos), the emergence of collective features for large systems, and the use of probability in the building of a consistent statistical description of physical systems. With this goal in mind, in the present review we aim at providing a consistent picture of the state of the art of the subject, both in the classical and in the quantum realm. In particular, we will highlight the similarities of the key technical and conceptual steps with emphasis on the relevance of the many degrees of freedom, to justify the use of statistical ensembles in the two domains., Comment: 94 pages, 9 figures

Measuring fluctuations in matter's low energy excitations is the key to unveil the nature of the nonequilibrium response of materials. A promising outlook in this respect is offered by spectroscopic methods that address matter fluctuations by exploiting the statistical nature of light-matter interactions with weak few-photon probes. Here we report the first implementation of ultrafast phase randomized tomography, combining pump-probe experiments with quantum optical state tomography, to measure the ultrafast non-equilibrium dynamics in complex materials. Our approach utilizes a time-resolved multimode heterodyne detection scheme with phase-randomized coherent ultrashort laser pulses, overcoming the limitations of phase-stable configurations and enabling a robust reconstruction of the statistical distribution of phase-averaged optical observables. This methodology is validated by measuring the coherent phonon response in $\alpha$-quartz. By tracking the dynamics of the shot-noise limited photon number distribution of few-photon probes with ultrafast resolution, our results set an upper limit to the non-classical features of phononic state in $\alpha$-quartz and provide a pathway to access nonequilibrium quantum fluctuations in more complex quantum materials., Comment: 21 pages, 10 figures (including Supplementary Information)

We demonstrate a quantum walk comb in synthetic frequency space formed by externally modulating a semiconductor optical amplifier operating in the telecommunication wavelength range in a unidirectional ring cavity. The ultrafast gain saturation dynamics of the gain medium and its operation at high current injections is responsible for the stabilization of the comb in a broad frequency modulated state. Our device produces a nearly flat broadband comb with a tunable repetition frequency reaching a bandwidth of 1.8THz at the fundamental repetition rate of 1GHz while remaining fully locked to the RF drive. Comb operation at harmonics of the repetition rate up to 14.1GHz is also demonstrated. This approach paves the way for next-generation optical frequency comb devices with potential applications in precision ranging and high-speed communications.

Firing rate models are dynamical systems widely used in applied and theoretical neuroscience to describe local cortical dynamics in neuronal populations. By providing a macroscopic perspective of neuronal activity, these models are essential for investigating oscillatory phenomena, chaotic behavior, and associative memory processes. Despite their widespread use, the application of firing rate models to associative memory networks has received limited mathematical exploration, and most existing studies are focused on specific models. Conversely, well-established associative memory designs, such as Hopfield networks, lack key biologically-relevant features intrinsic to firing rate models, including positivity and interpretable synaptic matrices that reflect excitatory and inhibitory interactions. To address this gap, we propose a general framework that ensures the emergence of re-scaled memory patterns as stable equilibria in the firing rate dynamics. Furthermore, we analyze the conditions under which the memories are locally and globally asymptotically stable, providing insights into constructing biologically-plausible and robust systems for associative memory retrieval., Comment: 20 pages, 7 figures

We discuss how the two existing approximate N$^3$LO (aN$^3$LO) sets of parton distributions (PDFs) from the MSHT20 and NNPDF4.0 series can be combined for LHC phenomenology, both in the pure QCD case and for the QCD$\otimes$QED sets that include the photon PDF. Using the resulting combinations, we present predictions for the total inclusive cross-section for Higgs production in gluon fusion, vector boson fusion, and associated production at the LHC Run-3. For the gluon fusion and vector boson fusion channels, the corrections that arise when using correctly matched aN$^3$LO PDFs with N$^3$LO cross section calculations, compared to using NNLO PDFs, are significant, in many cases larger than the PDF uncertainty, and generally larger than the differences between the two aN$^3$LO PDF sets entering the combination. The combined aN$^3$LO PDF sets, MSHT20xNNPDF40_an3lo and MSHT20xNNPDF40_an3lo_qed, are made publicly available in the LHAPDF format and can be readily used for LHC phenomenology., Comment: 22 pages, 11 figures, LHAPDF grids available from https://zenodo.org/records/13843626

The advent of digital neutral-atom quantum computers relies on the development of fast and robust protocols for high-fidelity quantum operations. In this work, we introduce a novel scheme for entangling gates using four atomic levels per atom: a ground state qubit and two Rydberg states. A laser field couples the qubit to one of the two Rydberg states, while a microwave field coupling the two Rydberg states enables a resonant dipole-dipole interaction between different atoms. We show that this interaction can mediate controlled-Z gates that are faster and less sensitive to Rydberg decay than state-of-the-art Rydberg gates based on van der Waals interactions. Moreover, we systematically stabilize our protocol against interatomic distance fluctuations and analyze its performance in realistic setups with rubidium or cesium atoms. Our results open up new avenues to the use of dipolar interactions for universal quantum computation with neutral atoms., Comment: 11 pages, 9 figures

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random effects. Such matrices are typically sparse; however, the sparsity pattern resembles a multi partite random graph, which does not lend itself well to default sparse linear algebra techniques. Notably, we show that, for typical GLMMs, the Cholesky factor is dense even when the original precision is sparse. We thus turn to approximate iterative techniques, in particular to the conjugate gradient (CG) method. We combine a detailed analysis of the spectrum of said precision matrices with results from random graph theory to show that CG-based methods applied to high-dimensional GLMMs typically achieve a fixed approximation error with a total cost that scales linearly with the number of parameters and observations. Numerical illustrations with both real and simulated data confirm the theoretical findings, while at the same time illustrating situations, such as nested structures, where CG-based methods struggle.

The Hopfield model provides a mathematically idealized yet insightful framework for understanding the mechanisms of memory storage and retrieval in the human brain. This model has inspired four decades of extensive research on learning and retrieval dynamics, capacity estimates, and sequential transitions among memories. Notably, the role and impact of external inputs has been largely underexplored, from their effects on neural dynamics to how they facilitate effective memory retrieval. To bridge this gap, we propose a novel dynamical system framework in which the external input directly influences the neural synapses and shapes the energy landscape of the Hopfield model. This plasticity-based mechanism provides a clear energetic interpretation of the memory retrieval process and proves effective at correctly classifying highly mixed inputs. Furthermore, we integrate this model within the framework of modern Hopfield architectures, using this connection to elucidate how current and past information are combined during the retrieval process. Finally, we embed both the classic and the new model in an environment disrupted by noise and compare their robustness during memory retrieval., Comment: 24 pages, 15 figures

Quantum measurements affect the state of the observed systems via back-action. While projective measurements extract maximal classical information, they drastically alter the system. In contrast, weak measurements balance information extraction with the degree of disturbance. Considering the prevalent use of projective measurements in quantum computing and communication protocols, the potential benefits of weak measurements in these fields remain largely unexplored. In this work, we demonstrate that incorporating weak measurements into a quantum machine-learning protocol known as quantum reservoir computing provides advantages in both execution time scaling and overall performance. We analyze different measurement settings by varying the measurement strength across two benchmarking tasks. Our results reveal that carefully optimizing both the reservoir Hamiltonian parameters and the measurement strength can significantly improve the quantum reservoir computing algorithm performance. This work provides a comprehensive and practical recipe to promote the implementation of weak measurement-based protocols in quantum reservoir computing. Moreover, our findings motivate further exploration of experimental protocols that leverage the back-action effects of weak measurements.

We investigated the reactivity of layered GeAs in the presence of oxygen and/or water using synchrotron-based X-ray photoelectron spectroscopy and ab initio calculations.

The seesaw mechanism with three heavy Majorana right-handed neutrinos provides an elegant explanation for neutrino masses and, combined with leptogenesis, can generate the baryon asymmetry of the universe (BAU). Naturally embedded in a Grand Unified Theory, this framework stands as one of the best-motivated extensions beyond the Standard Model. However, it does not account for dark matter (DM). In this paper, we propose a minimal extension that introduces a dark sector with a singlet Majorana fermion (as the DM candidate) and a complex scalar singlet. The heavy right-handed neutrinos serve multiple roles: i) explaining the small masses of active neutrinos via the seesaw mechanism, ii) generating the BAU through leptogenesis, and iii) producing the cold DM density through their decays. Interestingly, the model also predicts a subdominant DM component from late scalar decays, which in some cases may be hot or warm at the onset of structure formation, as well as an equal number of non-thermal neutrinos. These components leave distinct signatures in various cosmological observables. Furthermore, electromagnetic energy injection from scalar decays alter predictions from Big Bang Nucleosynthesis and induce spectral distortions in the Cosmic Microwave Background black-body spectrum. Upcoming experiments, such as the Primordial Inflation Explorer (PIXIE), could probe a significant portion of the parameter space of this extended high-scale seesaw scenario., Comment: 9 pages, 2 figures, 1 table

Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approximation in machine learning algorithms suitable for mixed-signal Analog In-Memory Computing (AIMC) architectures. Analog In-Memory Kernel Approximation addresses the performance bottlenecks of conventional kernel-based methods by executing most operations in approximate kernel methods directly in memory. The IBM HERMES Project Chip, a state-of-the-art phase-change memory based AIMC chip, is utilized for the hardware demonstration of kernel approximation. Experimental results show that our method maintains high accuracy, with less than a 1% drop in kernel-based ridge classification benchmarks and within 1% accuracy on the Long Range Arena benchmark for kernelized attention in Transformer neural networks. Compared to traditional digital accelerators, our approach is estimated to deliver superior energy efficiency and lower power consumption. These findings highlight the potential of heterogeneous AIMC architectures to enhance the efficiency and scalability of machine learning applications.

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

We present a comprehensive multi-epoch photometric and spectroscopic study of SN 2024bch, a nearby (19.9 Mpc) Type II supernova (SN) with prominent early high ionization emission lines. Optical spectra from 2.9 days after the estimated explosion reveal narrow lines of H I, He II, C IV, and N IV that disappear by day 6. High cadence photometry from the ground and TESS show that the SN brightened quickly and reached a peak M$_V \sim$ $-$17.8 mag within a week of explosion, and late-time photometry suggests a $^{56}$Ni mass of 0.050 M$_{\odot}$. High-resolution spectra from day 8 and 43 trace the unshocked circumstellar medium (CSM) and indicate a wind velocity of 30--40 km s$^{-1}$, a value consistent with a red supergiant (RSG) progenitor. Comparisons between models and the early spectra suggest a pre-SN mass-loss rate of $\dot{M} \sim 10^{-3}-10^{-2}\ M_\odot\ \mathrm{yr}^{-1}$, which is too high to be explained by quiescent mass loss from RSGs, but is consistent with some recent measurements of similar SNe. Persistent blueshifted H I and [O I] emission lines seen in the optical and NIR spectra could be produced by asymmetries in the SN ejecta, while the multi-component H$\alpha$ may indicate continued interaction with an asymmetric CSM well into the nebular phase. SN 2024bch provides another clue to the complex environments and mass-loss histories around massive stars., Comment: Submitted to ApJ

We present supernova (SN) 2023ufx, a unique Type IIP SN with the shortest known plateau duration ($t_\mathrm{PT}$ $\sim$47 days), a luminous V-band peak ($M_{V}$ = $-$18.42 $\pm$ 0.08 mag), and a rapid early decline rate ($s1$ = 3.47 $\pm$ 0.09 mag (50 days)$^{-1}$). By comparing observed photometry to a hydrodynamic MESA+STELLA model grid, we constrain the progenitor to be a massive red supergiant with M$_\mathrm{ZAMS}$ $\simeq$19 - 25 M$_{\odot}$. Independent comparisons with nebular spectral models also suggest an initial He-core mass of $\sim$6 M$_{\odot}$, and thus a massive progenitor. For a Type IIP, SN 2023ufx produced an unusually high amount of nickel ($^{56}$Ni) $\sim$0.14 $\pm$ 0.02 M$_{\odot}$, during the explosion. We find that the short plateau duration in SN 2023ufx can be explained with the presence of a small hydrogen envelope (M$_\mathrm{H_\mathrm{env}}$ $\simeq$1.2 M$_{\odot}$), suggesting partial stripping of the progenitor. About $\simeq$0.09 M$_{\odot}$ of CSM through mass loss from late-time stellar evolution of the progenitor is needed to fit the early time ($\lesssim$10 days) pseudo-bolometric light curve. Nebular line diagnostics of broad and multi-peak components of [O I] $\lambda\lambda$6300, 6364, H$\alpha$, and [Ca II] $\lambda \lambda$7291, 7323 suggest that the explosion of SN 2023ufx could be inherently asymmetric, preferentially ejecting material along our line-of-sight., Comment: Submitted to ApJ, 30 pages, 19 figures

Increasing complications due to bacterial and viral infections require novel antimicrobial approaches. One emerging strategy is that based on catalysts able to selectively deliver reactive oxygen species (ROS) without leaching of other substances. In particular, metal oxide thin films activated by daylight can produce ROS by simply catalyzing oxygen and water molecules. This study examines plasma technology, combining deposition and oxidation processes, as well as plasma polymerization, to obtain functionalized AgOx-doped titanium oxide (TiOx) catalytic materials. The high-energy conditions in the reactive, ionized gas enable the intrinsic formation of a large number of reactive sites at defects and interfaces between the metal oxide nanostructures. Furthermore, plasma functionalization with nanoporous SiOx films (up to 100 nm thick) allows to precisely control the ROS delivery as well as unravel ROS formation mechanism at the metal oxide interface. Combining fluorescence spectroscopy and electron paramagnetic resonance, the controlled delivery of superoxide anion and singlet oxygen has been tuned based on the thickness of the nanoporous functional layer. ROS delivery by functionalized catalytic plasma coating has been related to excellent antimicrobial activity against E. coli bacteria as well as murine hepatitis virus, while avoiding cytotoxic and sensitization effects., Comment: 23 pages, 7 figures

The well-posedness of a class of optimal control problems is analysed, where the state equation couples a nonlinear degenerate Fokker-Planck equation with a system of Ordinary Differential Equations (ODEs). Such problems naturally arise as mean-field limits of Stochastic Differential models for multipopulation dynamics, where a large number of agents (followers) is steered through parsimonious intervention on a selected class of leaders. The proposed approach combines stability estimates for measure solutions of nonlinear degenerate Fokker-Planck equations with a general framework of assumptions on the cost functional, ensuring compactness and lower semicontinuity properties. The Lie structure of the state equations allows one for considering non-Lipschitz nonlinearities, provided some suitable dissipativity assumptions are considered in addition to non-Euclidean H\"{o}lder and sublinearity conditions.

Bayesian Additive Regression Trees (BART) is a nonparametric Bayesian regression technique based on an ensemble of decision trees. It is part of the toolbox of many statisticians. The overall statistical quality of the regression is typically higher than other generic alternatives, and it requires less manual tuning, making it a good default choice. However, it is a niche method compared to its natural competitor XGBoost, due to the longer running time, making sample sizes above 10,000-100,000 a nuisance. I present a GPU-enabled implementation of BART, faster by up to 200x relative to a single CPU core, making BART competitive in running time with XGBoost. This implementation is available in the Python package bartz., Comment: Check out the software at https://github.com/Gattocrucco/bartz

We study multiproposal Markov chain Monte Carlo algorithms, such as Multiple-try or generalised Metropolis-Hastings schemes, which have recently received renewed attention due to their amenability to parallel computing. First, we prove that no multiproposal scheme can speed-up convergence relative to the corresponding single proposal scheme by more than a factor of $K$, where $K$ denotes the number of proposals at each iteration. This result applies to arbitrary target distributions and it implies that serial multiproposal implementations are always less efficient than single proposal ones. Secondly, we consider log-concave distributions over Euclidean spaces, proving that, in this case, the speed-up is at most logarithmic in $K$, which implies that even parallel multiproposal implementations are fundamentally limited in the computational gain they can offer. Crucially, our results apply to arbitrary multiproposal schemes and purely rely on the two-step structure of the associated kernels (i.e. first generate $K$ candidate points, then select one among those). Our theoretical findings are validated through numerical simulations., Comment: 19 pages, 1 figure

We study stochastic pairwise interaction network systems whereby a finite population of agents, identified with the nodes of a graph, update their states in response to both individual mutations and pairwise interactions with their neighbors. The considered class of systems include the main epidemic models -such as the SIS, SIR, and SIRS models-, certain social dynamics models -such as the voter and anti-voter models-, as well as evolutionary dynamics on graphs. Since these stochastic systems fall into the class of finite-state Markov chains, they always admit stationary distributions. We analyze the asymptotic behavior of these stationary distributions in the limit as the population size grows large while the interaction network maintains certain mixing properties. Our approach relies on the use of Lyapunov-type functions to obtain concentration results on these stationary distributions. Notably, our results are not limited to fully mixed population models, as they do apply to a much broader spectrum of interaction network structures, including, e.g., Erd\"oos-R\'enyi random graphs., Comment: 13 pages

In 2023, the 12th edition of Global Trajectory Competition was organised around the problem referred to as "Sustainable Asteroid Mining". This paper reports the developments that led to the solution proposed by ESA's Advanced Concepts Team. Beyond the fact that the proposed approach failed to rank higher than fourth in the final competition leader-board, several innovative fundamental methodologies were developed which have a broader application. In particular, new methods based on machine learning as well as on manipulating the fundamental laws of astrodynamics were developed and able to fill with remarkable accuracy the gap between full low-thrust trajectories and their representation as impulsive Lambert transfers. A novel technique was devised to formulate the challenge of optimal subset selection from a repository of pre-existing optimal mining trajectories as an integer linear programming problem. Finally, the fundamental problem of searching for single optimal mining trajectories (mining and collecting all resources), albeit ignoring the possibility of having intra-ship collaboration and thus sub-optimal in the case of the GTOC12 problem, was efficiently solved by means of a novel search based on a look-ahead score and thus making sure to select asteroids that had chances to be re-visited later on.

As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output and, hence, are crucial for supporting decision-making. Despite their importance, the evaluation of these explanations often lacks grounding in user studies and remains fragmented, with existing metrics not fully capturing human perspectives. To address this challenge, we developed a diverse set of 30 counterfactual scenarios and collected ratings across 8 evaluation metrics from 206 respondents. Subsequently, we fine-tuned different Large Language Models (LLMs) to predict average or individual human judgment across these metrics. Our methodology allowed LLMs to achieve an accuracy of up to 63% in zero-shot evaluations and 85% (over a 3-classes prediction) with fine-tuning across all metrics. The fine-tuned models predicting human ratings offer better comparability and scalability in evaluating different counterfactual explanation frameworks., Comment: This paper has been submitted in August and is currently under review to AAAI-2025

1T1R (1-transistor-1-resistor) memory crossbar arrays represent a promising solution for compute-in-memory matrix-vector multiplication accelerators and embedded or storage-class memory. However, the size and scaling of these arrays are hindered by critical challenges, such as the IR drop on metal lines and the accumulation of leakage current from the transistors. Although the IR drop issue has been extensively investigated, the impact of transistor leakage current has received limited attention. In this work, we investigate both issues and highlight how transistor leakage in 1T1R arrays has effects similar to IR drop, which degrades the memory cell sensing margin, especially as the technology node scales down. This degradation could pose reliability concerns, particularly where the on/off ratio or sensing margin of memristors is critical. We characterized the joint effects of transistor read resistance, transistor leakage current, and IR drop as the array size scales up and the fabrication node scales down. Based on a model developed using specifications of a 22nm FDSOI technology, we found that an optimal resistance range of memristors exists for good array scaling capability, where the transistor read resistance and the IR drop issue establish a lower resistance boundary, while the transistor leakage issue sets an upper resistance boundary. This work provides valuable scaling guidelines for engineering the properties of memristor devices in 1T1R memory arrays., Comment: 5 pages, 6 figures

Bayesian Additive Regression Trees (BART) is a nonparametric Bayesian regression technique of rising fame. It is a sum-of-decision-trees model, and is in some sense the Bayesian version of boosting. In the limit of infinite trees, it becomes equivalent to Gaussian process (GP) regression. This limit is known but has not yet led to any useful analysis or application. For the first time, I derive and compute the exact BART prior covariance function. With it I implement the infinite trees limit of BART as GP regression. Through empirical tests, I show that this limit is worse than standard BART in a fixed configuration, but also that tuning the hyperparameters in the natural GP way yields a competitive method, although a properly tuned BART is still superior. The advantage of using a GP surrogate of BART is the analytical likelihood, which simplifies model building and sidesteps the complex BART MCMC. More generally, this study opens new ways to understand and develop BART and GP regression. The implementation of BART as GP is available in the Python package https://github.com/Gattocrucco/lsqfitgp .

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

Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device mismatch in the analog circuits of these neuromorphic processors poses a significant challenge to the deployment of robust processing in these systems. Here we introduce a novel architectural solution inspired by biological development to address this issue. Specifically we propose to implement architectures that incorporate network motifs found in developed brains through a differentiable re-parameterization of weight matrices based on gene expression patterns and genetic rules. Thanks to the gradient descent optimization compatibility of the method proposed, we can apply the robustness of biological neural development to neuromorphic computing. To validate this approach we benchmark it using the Yin-Yang classification dataset, and compare its performance with that of standard multilayer perceptrons trained with state-of-the-art hardware-aware training method. Our results demonstrate that the proposed method mitigates mismatch-induced noise without requiring precise device mismatch measurements, effectively outperforming alternative hardware-aware techniques proposed in the literature, and providing a more general solution for improving the robustness of SNNs in neuromorphic hardware.

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

The field of autonomous navigation for unmanned ground vehicles (UGVs) is in continuous growth and increasing levels of autonomy have been reached in the last few years. However, the task becomes more challenging when the focus is on the exploration of planet surfaces such as Mars. In those situations, UGVs are forced to navigate through unstable and rugged terrains which, inevitably, open the vehicle to more hazards, accidents, and, in extreme cases, complete mission failure. The paper addresses the challenges of autonomous navigation for unmanned ground vehicles in planetary exploration, particularly on Mars, introducing a hybrid architecture for terrain traversability analysis that combines two approaches: appearance-based and geometry-based. The appearance-based method uses semantic segmentation via deep neural networks to classify different terrain types. This is further refined by pixel-level terrain roughness classification obtained from the same RGB image, assigning different costs based on the physical properties of the soil. The geometry-based method complements the appearance-based approach by evaluating the terrain's geometrical features, identifying hazards that may not be detectable by the appearance-based side. The outputs of both methods are combined into a comprehensive hybrid cost map. The proposed architecture was trained on synthetic datasets and developed as a ROS2 application to integrate into broader autonomous navigation systems for harsh environments. Simulations have been performed in Unity, showing the ability of the method to assess online traversability analysis.

Time-resolved single photon imaging is a promising imaging modality characterized by the unique capability of timestamping the arrivals of single photons. Single-Photon Avalanche Diodes (SPADs) are the leading technology for implementing modern time-resolved pixels, suitable for passive imaging with asynchronous readout. However, they are currently limited to small sized arrays, thus there is a lack of datasets for passive time-resolved SPAD imaging, which in turn hinders research on this peculiar imaging data. In this paper we describe a realistic simulation process for SPAD imaging, which takes into account both the stochastic nature of photon arrivals and all the noise sources involved in the acquisition process of time-resolved SPAD arrays. We have implemented this simulator in a software prototype able to generate arbitrary-sized time-resolved SPAD arrays operating in passive mode. Starting from a reference image, our simulator generates a realistic stream of timestamped photon detections. We use our simulator to generate a time-resolved version of MNIST, which we make publicly available. Our dataset has the purpose of encouraging novel research directions in time-resolved SPAD imaging, as well as investigating the performance of CNN classifiers in extremely low-light conditions., Comment: 12 pages, 4 figures. Accepted for Workshop on Synthetic Data for Computer Vision at ECCV 2024

In an era marked by renewed interest in lunar exploration and the prospect of establishing a sustainable human presence on the Moon, innovative approaches supporting mission preparation and astronaut training are imperative. To this end, the advancements in Virtual Reality (VR) technology offer a promising avenue to simulate and optimize future human missions to the Moon. Through VR simulations, tests can be performed quickly, with different environment parameters and a human-centered perspective can be maintained throughout the experiments. This paper presents a comprehensive framework that harnesses VR simulations to replicate the challenges and opportunities of lunar exploration, aiming to enhance astronaut readiness and mission success. Multiple environments with physical and visual characteristics that reflect those found in interesting Moon regions have been modeled and integrated into simulations based on the Unity graphical engine. We exploit VR to allow the user to fully immerse in the simulations and interact with assets in the same way as in real contexts. Different scenarios have been replicated, from upcoming exploration missions where it is possible to deploy scientific payloads, collect samples, and traverse the surrounding environment, to long-term habitation in a futuristic lunar base, performing everyday activities. Moreover, our framework allows us to simulate human-robot collaboration and surveillance directly displaying sensor readings and scheduled tasks of autonomous agents which will be part of future hybrid missions, leveraging the ROS2-Unity bridge. Thus, the entire project can be summarized as a desire to define cornerstones for human-machine design and interaction, astronaut training, and learning of potential weak points in the context of future lunar missions, through targeted operations in a variety of contexts as close to reality as possible.

Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization of large-scale, fully functional systems. One key challenge is determining which devices and materials are best suited for specific functions and how they can be paired with CMOS circuitry. To address this, we introduce TEXEL, a mixed-signal neuromorphic architecture designed to explore the integration of on-chip learning circuits and novel two- and three-terminal devices. TEXEL serves as an accessible platform to bridge the gap between CMOS-based neuromorphic computation and the latest advancements in emerging devices. In this paper, we demonstrate the readiness of TEXEL for device integration through comprehensive chip measurements and simulations. TEXEL provides a practical system for testing bio-inspired learning algorithms alongside emerging devices, establishing a tangible link between brain-inspired computation and cutting-edge device research., Comment: 17 pages, 7 figures. Supplementary material: 8 pages, 4 figures

Bosonic bunching is a term used to describe the well-known tendency of bosons to bunch together, and which differentiates their behaviour from that of fermions or classical particles. However, in some situations perfectly indistinguishable bosons may counter-intuitively bunch less than classical, distinguishable particles. Here we report two such counter-intuitive multiphoton bunching effects observed with three photons in a three-mode balanced photonic Fourier interferometer. In this setting, we show indistinguishable photons actually minimize the probability of bunching. We also show that any non-trivial value of the three-photon collective photonic phase leads to a decreased probability of all photons ending up in the same mode, even as we increase pairwise indistinguishability. Our experiments feature engineering of partial indistinguishability scenarios using both the time and the polarization photonic degrees of freedom, and a polarization-transparent 8-mode tunable interferometer with a quantum-dot source of single photons. Besides the foundational understanding, the observation of these counter-intuitive phenomena open news perspective in devising more efficient ways of routing photons for advantage in metrology and quantum computation., Comment: 10+8 pages, 5+1 figures

The use of optical cavities on resonance with material excitations allows controlling light-matter interaction in both the regimes of weak and strong coupling. We study here the multimode vibrational coupling of low energy phonons in the charge-density-wave material 1T-TaS$_{2}$ across its insulator-to-metal phase transition. For this purpose, we embed 1T-TaS$_{2}$ into THz Fabry-P\'erot cryogenic cavities tunable in frequency within the spectral range of the vibrational modes of the insulating phase and track the linear response of the coupled phonons across the insulator-to-metal transition. In the low temperature dielectric state, we reveal the signatures of a multimode vibrational strong collective coupling. The observed polariton modes inherit character from all the vibrational resonances as a consequence of the cavity-mediated hybridization. We reveal that the vibrational strong collective coupling is suppressed across the insulator-to-metal transition as a consequence of the phonon-screening induced by the free charges. Our findings emphasize how the response of cavity-coupled vibrations can be modified by the presence of free charges, uncovering a new direction toward the tuning of coherent light-matter interaction in cavity-confined correlated materials., Comment: 24 pages, 9 figures (including Supplementary Material)