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We propose TRAC, a tool for the specification and verification of coordinated multiparty distributed systems. Relying on finite-state machines (FSMs) where transition labels look like Hoare triples, \thetool can specify the coordination of the participants of a distributed protocol for instance an execution model akin blockchain smart contracts (SCs). In fact, the transitions of our FSMs yield guards, and assignments over data variables, and with participants binders. The latter allow us to model scenarios with an unbounded number of participants which can vary at run-time. We introduce a notion of well-formedness to rule out meaningless or problematic specifications. This notion is verified with TRAC and demonstrated on several case studies borrowed from the smart contracts domain. Then, we evaluate the performance of TRAC using a set of randomised examples, studying the correlations between the features supported and the time taken to decide well-formedness.

Telegram has grown into a significant platform for news and information sharing, favored for its anonymity and minimal moderation. This openness, however, makes it vulnerable to misinformation and conspiracy theories. In this study, we explore the dynamics of conspiratorial narrative dissemination within Telegram, focusing on Italian and English landscapes. In particular, we leverage the mechanism of message forwarding within Telegram and collect two extensive datasets through snowball strategy. We adopt a network-based approach and build the Italian and English Telegram networks to reveal their respective communities. By employing topic modeling, we uncover distinct narratives and dynamics of misinformation spread. Results highlight differences between Italian and English conspiracy landscapes, with Italian discourse involving assorted conspiracy theories and alternative news sources intertwined with legitimate news sources, whereas English discourse is characterized by a more focused approach on specific narratives such as QAnon and political conspiracies. Finally, we show that our methodology exhibits robustness across initial seed selections, suggesting broader applicability. This study contributes to understanding information and misinformation spread on Italian and English Telegram ecosystems through the mechanism of message forwarding, Comment: submitted to ASONAM 2024

This study investigates the feasibility of remote virtual reality (VR) studies conducted at home using VR headsets and video conferencing by deploying an experiment on emotion ratings. 20 participants used head-mounted displays to immerse themselves in 360{\deg} videos selected to evoke emotional responses. The research compares continuous ratings using a graphical interface to retrospective questionnaires on a digitized Likert Scale for measuring arousal and valence, both based on the self-assessment manikin (SAM). It was hypothesized that the two different rating methods would lead to significantly different values for both valence and arousal. The goal was to investigate whether continuous ratings during the experience would better reflect users' emotions compared to the post-questionnaire by mitigating biases such as the peak-end rule. The results show significant differences with moderate to strong effect sizes for valence and no significant differences for arousal with low to moderate effect sizes. This indicates the need for further investigation of the methods used to assess emotion ratings in VR studies. Overall, this study is an example of a remotely conducted VR experiment, offering insights into methods for emotion elicitation in VR by varying the timing and interface of the rating., Comment: The paper is accepted and will be presented at QoMEX 2024

One of the most promising technologies inside the Extended Reality (XR) spectrum is Augmented Reality. This technology is already in people's pockets regarding Mobile Augmented Reality with their smartphones. The scientific community still needs answers about how humans could and should interact in environments where perceived stimuli are different from fully physical or digital circumstances. Moreover, it is still being determined if people accept these new technologies in different social environments and interaction settings or if some obstacles could exist. This paper explores the impact of the Social Environment and the Focus of social interaction on users while playing a location-based augmented reality game, measuring it with user experience and social acceptance indicators. An empirical study in a within-subject fashion was performed in different social environments and under different settings of social interaction focus with N = 28 participants compiling self-reported questionnaires after playing a Scavenger Hunt in Augmented Reality. The measures from two different Social Environments (Crowded vs. Uncrowded) resulted in statistically relevant mean differences with indicators from the Social Acceptability dimension. Moreover, the analyses show statistically relevant differences between the variances from different degrees of Social Interaction Focus with Overall Social Presence, Perceived Psychological Engagement, Perceived Attentional Engagement, and Perceived Emotional Contagion. The results suggest that a location-based AR game played in different social environments and settings can influence the user experience's social dimension. Therefore, they should be carefully considered while designing immersive technological experiences in public spaces involving social interactions between players.

The auditory sense of humans is important when it comes to navigation. The importance is especially high in cases when an object of interest is visually partly or fully covered. Interactions with users of technology are mainly focused on the visual domain of navigation tasks. This paper presents the results of a literature review and user study exploring the impact of spatial auditory navigation on user experience during an augmented outdoor navigation task. For the user test, participants used an augmented reality app guiding them to different locations with different digital augmentation. We conclude that the utilization of the auditory sense is yet still underrepresented in augmented reality applications. In the future, more usage scenarios for audio-augmented reality such as navigation will enhance user experience and interaction quality.

We report the detection of a long X-ray burst triggered on MJD 60171.65 from the ultra-compact binary 4U 1850$-$087 by the Monitor of All-sky X-ray Image and Neutron Star Interior Composition Explorer (NICER). We analyse the NICER data observed in between MJD 60095.19$-$60177.43, including one observation covered part of the long X-ray burst tail, i.e., $0.15-3.8$ hr after the trigger. The persistent spectra are quite similar and well described by a combination of multi-color disk blackbody, with the inner temperature of 0.5 keV, and a thermally comptonized continuum with the asymptotic power-law photon index of $\Gamma\sim2.2$, and electron temperature of $kT_{\rm e}\sim20-30$ keV. The persistent fluxes were around $3.8\times10^{-10}~{\rm erg~cm^{-2}~s^{-1}}$, corresponding to a local accretion rate of $1\%~\dot{m}_{\rm Edd}$. Part of time-resolved burst spectra show a clear deviation from the blackbody model, which can be improved by considering the enhanced persistent emission due to the Poynting-Robertson drag, or the reflected disk emission illuminated by the burst. From the burst flux during the cooling tail, we estimate the burst duration, $\tau \approx 0.78$ hr, the burst fluence, $E_\mathrm{b} \approx 4.1 \times 10^{41}$ ergs, and the ignition column depth, $y_{\rm ign}\approx 3.5\times10^{10}~{\rm g~cm^{-2}}$. We propose that the long X-ray burst is powered by unstable burning of pure helium in deep layer. Moreover, we identify significant 1 keV emission lines in the burst spectra, which may originate from the surrounding disk., Comment: 9 pages, 6 figures, to match the published version in ApJ

On 24 September 2023, the NASA OSIRIS-REx mission dropped a capsule to Earth containing approximately 120 g of pristine carbonaceous regolith from Bennu. We describe the delivery and initial allocation of this asteroid sample and introduce its bulk physical, chemical, and mineralogical properties from early analyses. The regolith is very dark overall, with higher-reflectance inclusions and particles interspersed. Particle sizes range from sub-micron dust to a stone about 3.5 cm long. Millimeter-scale and larger stones typically have hummocky or angular morphologies. A subset of the stones appears mottled by brighter material that occurs as veins and crusts. Hummocky stones have the lowest densities and mottled stones have the highest. Remote sensing of the surface of Bennu detected hydrated phyllosilicates, magnetite, organic compounds, carbonates, and scarce anhydrous silicates, all of which the sample confirms. We also find sulfides, presolar grains, and, less expectedly, Na-rich phosphates, as well as other trace phases. The sample composition and mineralogy indicate substantial aqueous alteration and resemble those of Ryugu and the most chemically primitive, low-petrologic-type carbonaceous chondrites. Nevertheless, we find distinct hydrogen, nitrogen, and oxygen isotopic compositions, and some of the material we analyzed is enriched in fluid-mobile elements. Our findings underscore the value of sample return, especially for low-density material that may not readily survive atmospheric entry, and lay the groundwork for more comprehensive analyses., Comment: 73 pages, 22 figures

We study collective light scattering from linear crystals with up to twelve $^{40}\text{Ca}^+$ ions, acting as coherent single photon emitters. Light-scattering is induced by two-photon laser excitation, starting from the S$_{1/2}$ to D$_{5/2}$ quadrupole transition at 729~nm in combination with laser excitation of the D$_{5/2}$ to P$_{3/2}$ dipole transition at 854~nm, followed by a decay back to S$_{1/2}$ via a single photon emission near 393~nm. The scattered intensity is recorded in the far field, featuring the interference of emitted light fields of the entire crystal. Furthermore, we demonstrate spin-dependent coherent scattering and unveil the time evolution of a previously encoded spin texture in a crystal employing the recorded dynamics of the spatial frequency components of the fringe pattern.

We consider local and nonlocal Cahn-Hilliard equations with constant mobility and singular potentials including, e.g., the Flory-Huggins potential, subject to no-flux (or periodic) boundary conditions. The main goal is to show that the presence of a suitable class of reaction terms allows to establish the existence of a weak solution to the corresponding initial and boundary value problem even though the initial condition is a pure state. In other words, the separation process takes place even in presence of a pure phase, provided that it is triggered by a convenient reaction term. This fact was already observed by the authors in a previous contribution devoted to a specific biological model. In this context, we generalize the previously-mentioned concept by examining the essential assumptions required for the reaction term to apply the new strategy. Also, we explore the scenario involving the nonlocal Cahn-Hilliard equation and provide illustrative examples that contextualize within our abstract framework., Comment: 32 pages

We consider an evolution system modeling a flow of colloidal particles which are suspended in an incompressible fluid and accounts for colloidal crystallization. The system consists of the Navier-Stokes equations for the volume averaged velocity coupled with the so-called Phase-Field Crystal equation for the density deviation. Considering this system in a periodic domain and assuming that the viscosity as well as the mobility depend on the density deviation, we first prove the existence of a weak solution in dimension three. Then, in dimension two, we establish the existence of a (unique) strong solution.

The Lunar Gravitational-wave Antenna (LGWA) is a proposed array of next-generation inertial sensors to monitor the response of the Moon to gravitational waves (GWs). Given the size of the Moon and the expected noise produced by the lunar seismic background, the LGWA would be able to observe GWs from about 1 mHz to 1 Hz. This would make the LGWA the missing link between space-borne detectors like LISA with peak sensitivities around a few millihertz and proposed future terrestrial detectors like Einstein Telescope or Cosmic Explorer. In this article, we provide a first comprehensive analysis of the LGWA science case including its multi-messenger aspects and lunar science with LGWA data. We also describe the scientific analyses of the Moon required to plan the LGWA mission.

Currently, there is a growing trend of outsourcing the execution of DNNs to cloud services. For service providers, managing multi-tenancy and ensuring high-quality service delivery, particularly in meeting stringent execution time constraints, assumes paramount importance, all while endeavoring to maintain cost-effectiveness. In this context, the utilization of heterogeneous multi-accelerator systems becomes increasingly relevant. This paper presents RELMAS, a low-overhead deep reinforcement learning algorithm designed for the online scheduling of DNNs in multi-tenant environments, taking into account the dataflow heterogeneity of accelerators and memory bandwidths contentions. By doing so, service providers can employ the most efficient scheduling policy for user requests, optimizing Service-Level-Agreement (SLA) satisfaction rates and enhancing hardware utilization. The application of RELMAS to a heterogeneous multi-accelerator system composed of various instances of Simba and Eyeriss sub-accelerators resulted in up to a 173% improvement in SLA satisfaction rate compared to state-of-the-art scheduling techniques across different workload scenarios, with less than a 1.5% energy overhead.

Dilaton effective field theory (dEFT) describes the long distance behavior of certain confining, near-conformal gauge theories that have been studied via lattice computation. Pseudo-Nambu-Goldstone bosons (pNGBs), emerging from the breaking of approximate, continuous, internal symmetries, are coupled to an additional scalar particle, the dilaton, arising from the spontaneous breaking of approximate scale invariance. This effective theory has been employed to study possible extensions of the standard model. In this paper, we propose a complementary role for dEFT, as a description of the dark matter of the universe, with the pNGBs identified as the dark-matter particles. We show that this theory provides a natural implementation of the "forbidden" dark matter mechanism, and we identify regions of parameter space for which the thermal history of dEFT yields the measured dark matter relic density., Comment: Grant information added. Version accepted for publication at PRD: 9 pages, 3 figures, 1 table

Over the past decade, experimental microscopy and spectroscopy have made significant progress in the study of the morphological, optical, electronic and transport properties of materials. These developments include higher spatial resolution, shorter acquisition times, more efficient monochromators and electron analysers, improved contrast imaging and advancements in sample preparation techniques. These advances have driven the need for more accurate theoretical descriptions and predictions of material properties. Computer simulations based on first principles and Monte Carlo methods have emerged as a rapidly growing field for modeling the interaction of charged particles, such as electron, proton and ion beams, with various systems, such as slabs, nanostructures and crystals. This report delves into the theoretical and computational approaches to modeling the physico-chemical mechanisms that occur when charged beams interact with a medium. These mechanisms encompass single and collective electronic excitation, ionization of the target atoms and the generation of a secondary electron cascade that deposits energy into the irradiated material. We show that the combined application of ab initio methods, which are able to model the dynamics of interacting many-fermion systems, and Monte Carlo methods, which capture statistical fluctuations in energy loss mechanisms by random sampling, proves to be an optimal strategy for the accurate description of charge transport in solids. This joint quantitative approach enables the theoretical interpretation of excitation, loss and secondary electron spectra, the analysis of the chemical composition and dielectric properties of solids and contributes to our understanding of irradiation-induced damage in materials, including those of biological significance., Comment: 154 pages, 49 figures, 6 tables, review paper

Behavioral changes in animals and humans, as a consequence of an error or a verbal instruction, can be extremely rapid. Improvement in behavioral performances are usually associated in machine learning and reinforcement learning to synaptic plasticity, and, in general, to changes and optimization of network parameters. However, such rapid changes are not coherent with the timescales of synaptic plasticity, suggesting that the mechanism responsible for that could be a dynamical network reconfiguration. In the last few years, similar capabilities have been observed in transformers, foundational architecture in the field of machine learning that are widely used in applications such as natural language and image processing. Transformers are capable of in-context learning, the ability to adapt and acquire new information dynamically within the context of the task or environment they are currently engaged in, without the need for significant changes to their underlying parameters. Building upon the notion of something unique within transformers enabling the emergence of this property, we claim that it could also be supported by input segregation and dendritic amplification, features extensively observed in biological networks. We propose an architecture composed of gain-modulated recurrent networks that excels at in-context learning, showing abilities inaccessible to standard networks.

Context: Quasar variability has proven to be a powerful tool to constrain the properties of their inner engine and the accretion process onto supermassive black holes. Correlations between UV/optical variability and physical properties have been long studied with a plethora of different approaches and time-domain surveys, although the detailed picture is not yet clear. Aims: We analysed archival data from the SDSS Stripe-82 region to study how the quasar power spectral density (PSD) depends on the black hole mass, bolometric luminosity, accretion rate, redshift, and rest-frame wavelength. We developed a model-independent analysis framework that could be easily applied to upcoming large surveys such as the Legacy Survey of Space and Time (LSST). Methods: We used light curves of 8042 spectroscopically confirmed quasars, observed in at least six yearly seasons in five filters ugriz. We split the sample into bins of similar physical properties containing at least 50 sources, and we measured the ensemble PSD in each of them. Results: We find that a simple power law is a good fit to the power spectra in the frequency range explored. Variability does not depend on redshift at a fixed wavelength. Instead, both PSD amplitude and slope depend on the black hole mass, accretion rate, and rest-frame wavelength. We provide scaling relations to model the observed variability as a function of the physical properties, and discuss the possibility of a universal PSD shape for all quasars, where frequencies scale with the black hole mass, while normalization and slope(s) are fixed (at any given wavelength and accretion rate)., Comment: Accepted for publication in A&A

Understanding the dynamics of the turbulent velocity gradient tensor (VGT) is essential to gain insights into the Navier-Stokes equations and improve small-scale turbulence modeling. However, characterizing the VGT dynamics conditional on all its relevant invariants in a continuous fashion is extremely difficult. In this paper, we represent the VGT Lagrangian dynamics using a network where each node represents a unique flow state. This approach enables us to discern how the VGT transitions from one state to another in a simplified fashion. Our analysis reveals intriguing features of the resulting network, such as the clustering of the commonly visited nodes where the eigenvalues of the VGT are real, in the proximity of the Vieillefosse tail. We then relate our complex network approach to the well-established VGT discretization based on the sign of its principal invariants, $Q$ and $R$, and its discriminant, $\Delta$. To this end, we separate the shortest paths on the network (geodesics) based on the $Q$-$R$ region to which their starting and arrival nodes belong. The distribution of the length of intra-region geodesics, with starting and arrival nodes belonging to the same $Q$-$R$ region, exhibits a distinct bimodality in two regions of the $Q$-$R$ plane, those in which the deviatoric part of the pressure Hessian introduces complexity to the VGT dynamics. Such bimodality is associated with infrequently visited nodes having to follow a long, low probability path to drastically change the state of the VGT compared to other flow states that can acquire the necessary characteristics without changing their sign for $Q$ or $R$. We complement the geodesics approach by examining random walks on the network, showing how the VGT non-normality and the associated production terms distinguish the shortest commuting paths between different $Q$-$R$ regions., Comment: 19 pages, 9 figures

Local networks of neurons are nonlinear systems driven by synaptic currents elicited by its own spiking activity and the input received from other brain areas. Synaptic currents are well approximated by correlated Gaussian noise. Besides, the population dynamics of neuronal networks is often found to be multistable, allowing the noise source to induce state transitions. State changes in neuronal systems underlies the way information is encoded and transformed. The characterization of the escape time from metastable states is then a cornerstone to understand how information is processed in the brain. The effects of correlated input forcing bistable systems have been studied for over half a century, nonetheless most results are perturbative or valid only when a separation of time scales is present. Here, we present a novel and exact result holding when the correlation time of the noise source is identical to that of the neural population, hence solving in a very general setting the mean escape time problem.

Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) and established to investigate the use of synthetic data for training face recognition models. The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones. In the first task, the face recognition backbone was fixed and the dataset size was limited, while the second task provided almost complete freedom on the model backbone, the dataset, and the training pipeline. The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data. The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets. The paper gives an overview of the submitted face recognition models and reports achieved performance compared to baseline models trained on real and synthetic datasets. Furthermore, the evaluation of submissions is extended to bias assessment across different demography groups. Lastly, an outlook on the current state of the research in training face recognition models using synthetic data is presented, and existing problems as well as potential future directions are also discussed., Comment: The 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024)

As an alternative to the metastability of the electroweak vacuum, resulting from perturbative calculations, one can consider a non-perturbative effective potential which, as at the beginning of the Standard Model, is restricted to the pure $\Phi^4$ sector yet consistent with the known analytical and numerical studies. In this approach, where the electroweak vacuum is now the lowest-energy state, besides the resonance of mass $m_h=$ 125 GeV defined by the quadratic shape of the potential at its minimum, the Higgs field should exhibit a second resonance with mass $(M_H)^{\rm Theor}=690\,(30)$ GeV associated with the zero-point energy determining the potential depth. In spite of its large mass, this resonance would couple to longitudinal $W$s with the same typical strength as the low-mass state at 125 GeV and represent a relatively narrow resonance, mainly produced at LHC by gluon-gluon fusion. In this Letter, we review LHC data suggesting a new resonance of mass $(M_H)^{\rm EXP} \sim 682\,(10)$ GeV, with a statistical significance that is far from negligible., Comment: 5 pages, 5 tables, 7 figures, accepted for publication in LHEP. arXiv admin note: substantial text overlap with arXiv:2308.01429

Inside-out tracking is growing popular in consumer VR, enhancing accessibility. It uses HMD camera data and neural networks for effective hand tracking. However, limited user experience studies have compared this method to traditional controllers, with no consensus on the optimal control technique. This paper investigates the impact of control methods and gaming duration on VR user experience, hypothesizing hand tracking might be preferred for short sessions and by users new to VR due to its simplicity. Through a lab study with twenty participants, evaluating presence, emotional response, UX quality, and flow, findings revealed control type and session length affect user experience without significant interaction. Controllers were generally superior, attributed to their reliability, and longer sessions increased presence and realism. The study found that individuals with more VR experience were more inclined to recommend hand tracking to others, which contradicted predictions., Comment: Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Digital Library, https://doi.org/10.1145/3652212.3652222

Neuropathies are gaining higher relevance in clinical settings, as they risk permanently jeopardizing a person's life. To support the recovery of patients, the use of fully implanted devices is emerging as one of the most promising solutions. However, these devices, even if becoming an integral part of a fully complex neural nanonetwork system, pose numerous challenges. In this article, we address one of them, which consists of the classification of motor/sensory stimuli. The task is performed by exploring four different types of artificial neural networks (ANNs) to extract various sensory stimuli from the electroneurographic (ENG) signal measured in the sciatic nerve of rats. Different sizes of the data sets are considered to analyze the feasibility of the investigated ANNs for real-time classification through a comparison of their performance in terms of accuracy, F1-score, and prediction time. The design of the ANNs takes advantage of the modelling of the ENG signal as a multiple-input multiple-output (MIMO) system to describe the measures taken by state-of-the-art implanted nerve interfaces. These are based on the use of multi-contact cuff electrodes to achieve nanoscale spatial discrimination of the nerve activity. The MIMO ENG signal model is another contribution of this paper. Our results show that some ANNs are more suitable for real-time applications, being capable of achieving accuracies over $90\%$ for signal windows of $100$ and $200\,$ms with a low enough processing time to be effective for pathology recovery.

This paper investigates the robustness of a novel high-dimensional continuification control method for complex multi-agent systems. We begin by formulating a partial differential equation describing the spatio-temporal density dynamics of swarming agents. A stable control action for the density is then derived and validated under nominal conditions. Subsequently, we discretize this macroscopic strategy into actionable velocity inputs for the system's agents. Our analysis demonstrates the robustness of the approach beyond idealized assumptions of unlimited sensing and absence of perturbations., Comment: arXiv admin note: text overlap with arXiv:2310.01573

Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we analyze the similarities between scalable classifiers and conformal predictions by introducing a new definition of a score function and defining a special set of input variables, the conformal safety set, which can identify patterns in the input space that satisfy the error coverage guarantee, i.e., that the probability of observing the wrong (possibly unsafe) label for points belonging to this set is bounded by a predefined $\varepsilon$ error level. We demonstrate the practical implications of this framework through an application in cybersecurity for identifying DNS tunneling attacks. Our work contributes to the development of probabilistically robust and reliable machine learning models., Comment: 19 pages, 6 figures, journal paper

A virtual embodiment can benefit conversational agents, but it is unclear how their personalities and non-verbal behavior influence the User Experience and Social Presence in Augmented Reality (AR). We asked 30 users to converse with a virtual assistant who gives recommendations about city activities. The participants interacted with two different personalities: Sammy, a cheerful blue mouse, and Olive, a serious green human-like agent. Each was presented with two body languages - happy/friendly and annoyed/unfriendly. We conclude how agent representation and humor affect User Experience aspects, and that body language is significant in the evaluation and perception of the AR agent.

Millions of people have seen their daily habits transform, reducing physical activity and leading to mental health issues. This study explores how virtual characters impact motivation for well-being. Three prototypes with cartoon, robotic, and human-like avatars were tested by 22 participants. Results show that animated virtual avatars, especially with extended reality, boost motivation, enhance comprehension of activities, and heighten presence. Multiple output modalities, like audio and text, with character animations, improve the user experience. Notably, the cartoon-like character evoked positive responses. This research highlights virtual characters' potential to engage individuals in daily well-being activities.

Quantum emitters, such as the negatively charged nitrogen-vacancy center in diamond, are attractive for quantum technologies such as nano-sensing, quantum information processing, and as a non-classical light source. However, it is still challenging to position individual emitters in photonic structures whilst preserving the spin coherence properties of the defect. In this paper, we investigate single and ensemble waveguide-integrated nitrogen-vacancy centers in diamond fabricated by femtosecond laser writing followed by thermal annealing. Their spin coherence properties are systematically investigated and are shown to be comparable to native nitrogen-vacancy centers in diamond. This method paves the way for the fabrication of coherent spins integrated within photonic devices., Comment: 9 pages, 5 figures

Recent Monte Carlo plasma simulations to study in crystallizing carbon-oxygen (CO) white dwarfs (WDs) the phase separation of Ne22 (the most abundant metal after carbon and oxygen) have shown that, under the right conditions, a distillation process that transports Ne22 toward the WD centre is efficient and releases a considerable amount of gravitational energy that can lead to cooling delays of up to several Gyr. Here we present the first CO WD stellar evolution models that self-consistently include the effect of neon distillation, and cover the full range of CO WD masses, for a progenitor metallicity twice-solar appropriate for the old open cluster NGC 6791. The old age (about 8.5 Gyr) and high metallicity of this cluster -- hence the high neon content (about 3% by mass) in the cores of its WDs -- maximize the effect of neon distillation in the models to be compared with the observed cooling sequence. We discuss the effect of distillation on the internal chemical stratification and cooling time of the models, confirming that distillation causes cooling delays up to several Gyr, that depend in a non-monotonic way on the mass. We also show how our models produce luminosity functions (LFs) that can match the faint end of the observed WD LF in NGC 6791, for ages consistent with the range determined from a sample of cluster's eclipsing binary stars, and the main sequence turn-off. Without the inclusion of distillation the theoretical WD cooling sequences reach too faint magnitudes compared to the observations. We also propose James Webb Space Telescope observations that can independently demonstrate the efficiency of neon distillation in the interiors of NGC 6791 WDs, and help resolve the current uncertainty on the treatment of the electron conduction opacities for the hydrogen-helium envelope of the WD models., Comment: 10 pages, 10 figures, accepted for publication in A&A

Recently, efficiently deploying deep learning solutions on the edge has received increasing attention. New platforms are emerging to support the increasing demand for flexibility and high performance. In this work, we explore the efficient mapping of convolutional layers on an open-hardware, low-power Coarse-Grain Reconfigurable Array (CGRA), namely OpenEdgeCGRA. We explore both direct implementations of convolution and solutions that transform it into a matrix multiplication through an Im2col transformation, and experiment with various tensor parallelism axes. We show that for this hardware target, direct convolution, coupled with weight parallelism reaches the best latency and energy efficiency, outperforming a CPU implementation by 3.4x and 9.9x in terms of energy and latency, respectively.

We propose a novel indicator for chaotic quantum scattering processes, the scattering form factor (ScFF). It is based on mapping the locations of peaks in the scattering amplitude to random matrix eigenvalues, and computing the analog of the spectral form factor (SFF). We compute the spectral and scattering form factors of several non-chaotic systems. We determine the ScFF associated with the phase shifts of the leaky torus, closely related to the distribution of the zeros of Riemann zeta function. We compute the ScFF for the decay amplitude of a highly excited string states into two tachyons. We show that it displays the universal features expected from random matrix theory - a decline, a ramp and a plateau - and is in general agreement with the Gaussian unitary ensemble. It also shows some new features, owning to the special structure of the string amplitude, including a "bump" before the ramp associated with gaps in the average eigenvalue density. The "bump" is removed for highly excited string states with an appropriate state dependent unfolding. We also discuss the SFF for the Gaussian $\beta$- ensemble, writing an interpolation between the known results of the Gaussian orthogonal, unitary, and symplectic ensembles., Comment: 47 pages, 17 figures

Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for non-local pressure and viscous contributions. The fidelity of the resulting models depends on the accuracy of the underlying modeling assumptions. Here, we discuss an alternative data-driven approach leveraging machine learning to derive a velocity gradient model which captures its statistics by construction. We use a normalizing flow to learn the velocity gradient probability density function (PDF) from direct numerical simulation (DNS) of incompressible turbulence. Then, by using the equation for the single-time PDF of the velocity gradient, we construct a deterministic, yet chaotic, dynamical system featuring the learned steady-state PDF by design. Finally, utilizing gauge terms for the velocity gradient single-time statistics, we optimize the time correlations as obtained from our model against the DNS data. As a result, the model time realizations statistically closely resemble the time series from DNS.