Search

We investigate the convergence of quasi-particle energies for periodic systems to the thermodynamic limit using increasingly large simulation cells corresponding to increasingly dense integration meshes in reciprocal space. The quasi-particle energies are computed at the level of equation-of-motion coupled-cluster theory for ionization (IP-EOM-CC) and electron attachment processes (EA-EOM-CC). By introducing an electronic correlation structure factor, the expected asymptotic convergence rates for systems with different dimensionality are formally derived. We rigorously test these derivations through numerical simulations for trans-Polyacetylene using IP/EA-EOM-CCSD and the G0W0@HF approximation, which confirm the predicted convergence behavior. Our findings provide a solid foundation for efficient schemes to correct finite-size errors in IP/EA-EOM-CCSD calculations., Comment: 16 pages, 9 figures

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

We study integral operators on the space of square-integrable functions from a compact set, $X$, to a separable Hilbert space, $H$. The kernel of such an operator takes values in the ideal of Hilbert-Schmidt operators on $H$. We establish regularity conditions on the kernel under which the associated integral operator is trace class. First, we extend Mercer's theorem to operator-valued kernels by proving that a continuous, nonnegative-definite, Hermitian symmetric kernel defines a trace class integral operator on $L^2(X;H)$ under an additional assumption. Second, we show that a general operator-valued kernel that is defined on a compact set and that is H\"older continuous with H\"older exponent greater than a half is trace class provided that the operator-valued kernel is essentially bounded as a mapping into the space of trace class operators on $H$. Finally, when $\dim H < \infty$, we show that an analogous result also holds for matrix-valued kernels on the real line, provided that an additional exponential decay assumption holds., Comment: 27 pages

We report the first result from a dark photon dark matter search in the mass range from ${78.62}$ to $83.95~\mathrm{\mu eV}/c^2$ with a dielectric haloscope prototype for MADMAX (Magnetized Disc and Mirror Axion eXperiment). Putative dark photons would convert to observable photons within a stack consisting of three sapphire disks and a mirror. The emitted power of this system is received by an antenna and successively digitized using a low-noise receiver. No dark photon signal has been observed. Assuming unpolarized dark photon dark matter with a local density of $\rho_{\chi}=0.3~\mathrm{GeV/cm^3}$ we exclude a dark photon to photon mixing parameter $\chi > 3.0 \times 10^{-12}$ over the full mass range and $\chi > 1.2 \times 10^{-13}$ at a mass of $80.57~\mathrm{\mu eV}/c^2$ with a 95\% confidence level. This is the first physics result from a MADMAX prototype and exceeds previous constraints on $\chi$ in this mass range by up to almost three orders of magnitude., Comment: v1

Text-to-image generative models often present issues regarding fairness with respect to certain sensitive attributes, such as gender or skin tone. This study aims to reproduce the results presented in "ITI-GEN: Inclusive Text-to-Image Generation" by Zhang et al. (2023a), which introduces a model to improve inclusiveness in these kinds of models. We show that most of the claims made by the authors about ITI-GEN hold: it improves the diversity and quality of generated images, it is scalable to different domains, it has plug-and-play capabilities, and it is efficient from a computational point of view. However, ITI-GEN sometimes uses undesired attributes as proxy features and it is unable to disentangle some pairs of (correlated) attributes such as gender and baldness. In addition, when the number of considered attributes increases, the training time grows exponentially and ITI-GEN struggles to generate inclusive images for all elements in the joint distribution. To solve these issues, we propose using Hard Prompt Search with negative prompting, a method that does not require training and that handles negation better than vanilla Hard Prompt Search. Nonetheless, Hard Prompt Search (with or without negative prompting) cannot be used for continuous attributes that are hard to express in natural language, an area where ITI-GEN excels as it is guided by images during training. Finally, we propose combining ITI-GEN and Hard Prompt Search with negative prompting., Comment: Accepted to TMLR, see https://openreview.net/forum?id=d3Vj360Wi2

Recent research has challenged the widespread belief that gender inequities in academia would disappear simply by increasing the number of women. More complex causes might be at play, embodied in the networked structure of scientific collaborations. Here, we aim to understand the structural inequality between male and female scholars in the dissemination of scientific knowledge. We use a large-scale dataset of academic publications from the American Physical Society (APS) to build a time-varying network of collaborations from 1970 to 2020. We model knowledge dissemination as a contagion process in which scientists become informed based on the propagation of knowledge through their collaborators. We quantify the fairness of the system in terms of how women acquire and diffuse knowledge compared to men. Our results indicate that knowledge acquisition and diffusion are slower for women than expected. We find that the main determinant of women's disadvantage is the gap in the cumulative number of collaborators, highlighting how time creates structural disadvantages that contribute to marginalize women in physics. Our work sheds light on how the dynamics of scientific collaborations shape gender disparities in knowledge dissemination and calls for a deeper understanding on how to intervene to improve fairness and diversity in the scientific community.

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

Filaments connected to galaxy clusters are crucial environments to study the building up of cosmic structures as they funnel matter towards the clusters' deep gravitational potentials. Identifying gas in filaments is a challenge, due to their lower density contrast which produces faint signals. The best chance to detect these signals is therefore in the outskirts of galaxy clusters. We revisit the X-ray observation of the cluster Abell 2744 using statistical estimators of anisotropic matter distribution to identify filamentary patterns around it. We report for the first time the blind detection of filaments connected to a galaxy cluster from X-ray emission using a filament-finder technique and a multipole decomposition technique. We compare this result with filaments extracted from the distribution of spectroscopic galaxies, through which we demonstrate the robustness and reliability of our techniques in tracing a filamentary structure of 3 to 5 filaments connected to Abell 2744., Comment: 13 pages, 9 figures. Submitted to A&A

MADMAX, a future experiment to search for axion dark matter, is based on a novel detection concept called the dielectric haloscope. It consists of a booster composed of several dielectric disks positioned with $\mu$m precision. A prototype composed of one movable disk was built to demonstrate the mechanical feasibility of such a booster in the challenging environment of the experiment: high magnetic field to convert the axions into photons and cryogenic temperature to reduce the thermal noise. It was tested both inside a strong magnetic field up to 1.6 T and at cryogenic temperatures down to 35K. The measurements of the velocity and positioning accuracy of the disk are shown and are found to match the MADMAX requirements., Comment: 14 pages, 9 figures

Teams are the fundamental units propelling innovation and advancing modern science. A rich literature links the fundamental features of teams, such as their size and diversity, to academic success. However, such analyses fail to capture temporal patterns, treating each group of co-authors as a distinct unit and neglecting the existence of persistent collaborations. By contrast, teams are dynamical entities, made of core members who consistently work together, surrounded by transient members who sporadically participate. Leveraging on a large dataset of over 205 million scientific papers published since 1900, we extract 511,550 core teams of statistically significant persistent collaborations of pairs and larger groups of scientists. We look into `team careers' investigating their trajectories in time, characterizing their formation, productivity and eventual dissolution. We characterize team composition along multiple dimensions, including age, academic affiliation and scientific disciplines. Finally, we investigate the academic impact of persistent collaborations, hallmarking the key compositional features underlying their success. Our work sheds light on the nature of persistent teams, informing researchers, institutions and funding agencies about the dynamics of their formation, evolution and success., Comment: 18 pages, 13 figures

Most of the analyses concerning signed networks have focused on the balance theory, hence identifying frustration with undirected, triadic motifs having an odd number of negative edges; much less attention has been paid to their directed counterparts. To fill this gap, we focus on signed, directed connections, with the aim of exploring the notion of frustration in such a context. When dealing with signed, directed edges, frustration is a multi-faceted concept, admitting different definitions at different scales: if we limit ourselves to consider cycles of length two, frustration is related to reciprocity, i.e. the tendency of edges to admit the presence of partners pointing in the opposite direction. As the reciprocity of signed networks is still poorly understood, we adopt a principled approach for its study, defining quantities and introducing models to consistently capture empirical patterns of the kind. In order to quantify the tendency of empirical networks to form either mutualistic or antagonistic cycles of length two, we extend the Exponential Random Graphs framework to binary, directed, signed networks with global and local constraints and, then, compare the empirical abundance of the aforementioned patterns with the one expected under each model. We find that the (directed extension of the) balance theory is not capable of providing a consistent explanation of the patterns characterising the directed, signed networks considered in this work. Although part of the ambiguities can be solved by adopting a coarser definition of balance, our results call for a different theory, accounting for the directionality of edges in a coherent manner. In any case, the evidence that the empirical, signed networks can be highly reciprocated leads us to recommend to explicitly account for the role played by bidirectional dyads in determining frustration at higher levels (e.g. the triadic one)., Comment: 35 pages, 9 figures, 4 tables

Wind turbines degrade over time, resulting in varying structural, aeroelastic, and aerodynamic properties. In contrast, the turbine controller calibrations generally remain constant, leading to suboptimal performance and potential stability issues. The calibration of wind turbine controller parameters is therefore of high interest. To this end, several adaptive control schemes based on extremum seeking control (ESC) have been proposed in the literature. These schemes have been successfully employed to maximize turbine power capture by optimization of the $K\omega^2$-type torque controller. In practice, ESC is performed using electrical generator power, which is easily obtained. This paper analyses the feasibility of torque gain optimization using aerodynamic and generator powers. It is shown that, unlike aerodynamic power, using the generator power objective limits the dither frequency to lower values, reducing the convergence rate unless the phase of the demodulation ESC signal is properly adjusted. A frequency-domain analysis of both systems shows distinct phase behavior, impacting ESC performance. A solution is proposed by constructing an objective measure based on an estimate of the aerodynamic power., Comment: 8 pages, 8 figures, American Control Conference (ACC) 2024

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement., Comment: 90 pages, 22 figures, roadmap, neuromorphic

Quantum mechanical many-electron calculations can predict properties of atoms, molecules and even complex materials. The employed computational methods play a quintessential role in many scientifically and technologically relevant research fields. However, a question of paramount importance is whether approximations aimed at reducing the computational complexity for solving the many-electron Schr\"odinger equation, are accurate enough. Here, we investigate recently reported discrepancies of noncovalent interaction energies for large molecules predicted by two of the most widely-trusted many-electron theories: diffusion quantum Monte Carlo and coupled-cluster theory. We are able to unequivocally pin down the source of the puzzling discrepancies and present modifications to widely-used coupled-cluster methods needed for more accurate noncovalent interaction energies of large molecules on the hundred-atom scale. This enhances the reliability of predictions from quantum mechanical many-electron theories across a wide range of critical applications, including drug design, catalysis, and the innovation of new functional materials, such as those for renewable energy technologies., Comment: 14 pages, 3 figures, 2 tables

Tabular data is ubiquitous in many real-life systems. In particular, time-dependent tabular data, where rows are chronologically related, is typically used for recording historical events, e.g., financial transactions, healthcare records, or stock history. Recently, hierarchical variants of the attention mechanism of transformer architectures have been used to model tabular time-series data. At first, rows (or columns) are encoded separately by computing attention between their fields. Subsequently, encoded rows (or columns) are attended to one another to model the entire tabular time-series. While efficient, this approach constrains the attention granularity and limits its ability to learn patterns at the field-level across separate rows, or columns. We take a first step to address this gap by proposing Fieldy, a fine-grained hierarchical model that contextualizes fields at both the row and column levels. We compare our proposal against state of the art models on regression and classification tasks using public tabular time-series datasets. Our results show that combining row-wise and column-wise attention improves performance without increasing model size. Code and data are available at https://github.com/raphaaal/fieldy., Comment: 9 pages; Camera Ready version

The most fundamental social interactions among humans occur face to face. Their features have been extensively studied in recent years, owing to the availability of high-resolution data on individuals' proximity. Mathematical models based on mobile agents have been crucial to understand the spatio-temporal organization of face-to-face interactions. However, these models focus on dyadic relationships only, failing to characterize interactions in larger groups of individuals. Here, we propose a model in which agents interact with each other by forming groups of different sizes. Each group has a degree of social attractiveness, based on which neighboring agents decide whether to join. Our framework reproduces different properties of groups in face-to-face interactions, including their distribution, the correlation in their number, and their persistence in time, which cannot be replicated by dyadic models. Furthermore, it captures homophilic patterns at the level of higher-order interactions, going beyond standard pairwise approaches. Our work sheds light on the higher-order mechanisms at the heart of human face-to-face interactions, paving the way for further investigation of how group dynamics at a microscopic scale affects social phenomena at a macroscopic scale.

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models., Comment: 29 pages, 4 figures

The azimuthal correlation angle, $\Delta\phi$, between the scattered lepton and the leading jet in deep inelastic $e^{\pm}p$ scattering at HERA has been studied using data collected with the ZEUS detector at a centre-of-mass energy of $\sqrt{s} = 318 \;\mathrm{GeV}$, corresponding to an integrated luminosity of $326 \;\mathrm{pb}^{-1}$. A measurement of jet cross sections in the laboratory frame was made in a fiducial region corresponding to photon virtuality $10 \;\mathrm{GeV}^2 < Q^2 < 350 \;\mathrm{GeV}^2$, inelasticity $0.04 < y < 0.7$, outgoing lepton energy $E_e > 10 \;\mathrm{GeV}$, lepton polar angle $140^\circ < \theta_e < 180^\circ$, jet transverse momentum $2.5 \;\mathrm{GeV} < p_\mathrm{T,jet} < 30 \;\mathrm{GeV}$, and jet pseudorapidity $-1.5 < \eta_\mathrm{jet} < 1.8$. Jets were reconstructed using the $k_\mathrm{T}$ algorithm with the radius parameter $R = 1$. The leading jet in an event is defined as the jet that carries the highest $p_\mathrm{T,jet}$. Differential cross sections, $d\sigma/d\Delta\phi$, were measured as a function of the azimuthal correlation angle in various ranges of leading-jet transverse momentum, photon virtuality and jet multiplicity. Perturbative calculations at $\mathcal{O}(\alpha_{s}^2)$ accuracy successfully describe the data within the fiducial region, although a lower level of agreement is observed near $\Delta\phi \rightarrow \pi$ for events with high jet multiplicity, due to limitations of the perturbative approach in describing soft phenomena in QCD. The data are equally well described by Monte Carlo predictions that supplement leading-order matrix elements with parton showering.

This Letter reports the first measurement of the oscillation amplitude and frequency of reactor antineutrinos at Daya Bay via neutron capture on hydrogen using 1958 days of data. With over 3.6 million signal candidates, an optimized candidate selection, improved treatment of backgrounds and efficiencies, refined energy calibration, and an energy response model for the capture-on-hydrogen sensitive region, the relative $\overline{\nu}_{e}$ rates and energy spectra variation among the near and far detectors gives $\mathrm{sin}^22\theta_{13} = 0.0759_{-0.0049}^{+0.0050}$ and $\Delta m^2_{32} = (2.72^{+0.14}_{-0.15})\times10^{-3}$ eV$^2$ assuming the normal neutrino mass ordering, and $\Delta m^2_{32} = (-2.83^{+0.15}_{-0.14})\times10^{-3}$ eV$^2$ for the inverted neutrino mass ordering. This estimate of $\sin^2 2\theta_{13}$ is consistent with and essentially independent from the one obtained using the capture-on-gadolinium sample at Daya Bay. The combination of these two results yields $\mathrm{sin}^22\theta_{13}= 0.0833\pm0.0022$, which represents an 8% relative improvement in precision regarding the Daya Bay full 3158-day capture-on-gadolinium result.

Microscopic particles flowing through narrow channels may accumulate near bifurcation points provoking flow reduction, clogging and ultimately chip breakage. Here we show that the full flow behavior of colloidal particles through a microfluidic Y-junction (i.e. a three way intersection) can be controlled by tuning the pair interactions and the degree of confinement. By combining experiments with numerical simulations, we investigate the dynamic states emerging when magnetizable colloids flow through a symmetric Y-junction such that a single particle can pass through both gates with the same probability. We show that clogging can be avoided by repulsive interactions and branching into the two channels can be steered as well by interactions: attractive particles are flowing through the same gate, while repulsive colloids alternate between the two gates. Even details of the particle assembly such as buckling at the exit gate are tunable by interactions and the channel geometry., Comment: 7 pages, 4 figures

Diffusion models for Text-to-Image (T2I) conditional generation have seen tremendous success recently. Despite their success, accurately capturing user intentions with these models still requires a laborious trial and error process. This challenge is commonly identified as a model alignment problem, an issue that has attracted considerable attention by the research community. Instead of relying on fine-grained linguistic analyses of prompts, human annotation, or auxiliary vision-language models to steer image generation, in this work we present a novel method that relies on an information-theoretic alignment measure. In a nutshell, our method uses self-supervised fine-tuning and relies on point-wise mutual information between prompts and images to define a synthetic training set to induce model alignment. Our comparative analysis shows that our method is on-par or superior to the state-of-the-art, yet requires nothing but a pre-trained denoising network to estimate MI and a lightweight fine-tuning strategy.

Near-surface nitrogen-vacancy centres are critical to many diamond-based quantum technologies such as information processors and nanosensors. Surface defects play an important role in the design and performance of these devices. The targeted creation of defects is central to proposed bottom-up approaches to nanofabrication of quantum diamond processors, and uncontrolled surface defects may generate noise and charge trapping which degrade shallow NV device performance. Surface preparation protocols may be able to control the production of desired defects and eliminate unwanted defects, but only if their atomic structure can first be conclusively identified. This work uses a combination of scanning tunnelling microscopy (STM) imaging and first-principles simulations to identify several surface defects on H:C(100)-2x1 surfaces prepared using chemical vapour deposition (CVD). The atomic structure of these defects is elucidated, from which the microscopic origins of magnetic noise and charge trapping is determined based on modelling of their paramagnetic properties and acceptor states. Rudimentary control of these deleterious properties is demonstrated through STM tip-induced manipulation of the defect structure. Furthermore, the results validate accepted models for CVD diamond growth by identifying key adsorbates responsible for nucleation of new layers., Comment: 15 pages, 14 figures, combined manuscript and supporting information

We introduce a novel algorithm that leverages stochastic sampling techniques to compute the perturbative triples correction in the coupled-cluster (CC) framework. By combining elements of randomness and determinism, our algorithm achieves a favorable balance between accuracy and computational cost. The main advantage of this algorithm is that it allows for the calculation to be stopped at any time, providing an unbiased estimate, with a statistical error that goes to zero as the exact calculation is approached. We provide evidence that our semi-stochastic algorithm achieves substantial computational savings compared to traditional deterministic methods. Specifically, we demonstrate that a precision of 0.5 millihartree can be attained with only 10\% of the computational effort required by the full calculation. This work opens up new avenues for efficient and accurate computations, enabling investigations of complex molecular systems that were previously computationally prohibitive.

This work examines the oxygenation process of polycrystalline powder of YBa$_2$Cu$_3$O$_{6+\delta}$ superconductor material through constant temperature thermogravimetric analysis. Starting from completely deoxygenated samples ($\delta$ = 0), both the mass evolution in an oxygen atmosphere and a differential temperature value referenced to an inert sample (alumina powder) subjected to the same experimental conditions are recorded. The oxygenation process itself is identified as an exothermic process accompanied by a second also exothermic associated process. This second process is identified as the Tetragonal-Orthorhombic transition present in the material. Based on the obtained results, the structural phase diagram of the material is reconstructed. Issues of metastability of the structural phases, as well as the potential of the equipment used to obtain these results comparable to those obtained in large facilities, are discussed. Reinterpretations of previously discussed works in the literature are inferred., Comment: [v2]: For a better comprehension of the text, non relevant information was moved to Supplementary Materials. Also, Section "Conclusions" was renamed as "Discussion" as well as Section "Final Remarks" was renamed as "Conclusions". Results, discussions and conclusions have remained unchanged. None of the previous information [v1] have been removed

Previous X-ray works on Mrk 1239 have revealed a complex Narrow Line Seyfert 1 (NLS1) that exhibits substantial absorption and strong emission from both collisional (CIE) and photoionized (PIE) plasmas. Here, we report on deep-pointed observations with $XMM{\rm -}Newton$ and $NuSTAR$, along with $Swift$ monitoring, to understand the $0.3-30$ keV continuum emission and the central engine geometry. A strong X-ray flare, where the AGN brightens by a factor of five in $\sim30$ ks, is captured between $4-30$ keV and can be attributed to a brightening of the primary continuum. However, the lack of any variability below $\sim3$ keV on long- or short-time scales requires complete absorption of the AGN continuum with a neutral medium of column density $\sim 10^{23.5}{\rm cm}^{-2}$. The timing and spectral properties are consistent with a blurred reflection interpretation for the primary emission. The variability and presence of a Compton hump disfavours ionized partial covering. The neutral absorber, if outflowing, could be crashing into the surrounding medium and ISM to produce the low-energy continuum and CIE. Scattered emission off the inner torus could produce the PIE. The intricate scenario is demanded by the data and highlights the complexity of the environment that is normally invisible when overwhelmed by the AGN continuum. Objects like Mrk 1239 serve as important sources for unveiling the interface between the AGN and host galaxy environments., Comment: 26 pages, accepted for publication in ApJ

This study provides first empirical evidence on the impact of entrepreneurs' financial knowledge on borrower discouragement. Using novel survey data on Italian micro-enterprises, we find that less financially knowledgeable entrepreneurs are more likely to be discouraged from applying for new financing, due to higher application costs and expected rejection. Our main results are robust to several sensitivity checks, including accounting for potential endogeneity. Furthermore, we show that the observed self-rationing mechanism is rather inefficient, suggesting that financial knowledge might play a key role in reducing credit market imperfections.

In this paper we study convexity properties for quasilinear Lane-Emden-Fowler equations of the type $$ \begin{cases} -\Delta_p u = a(x) u^q & \quad \hbox{ in $\Omega$},\\ u >0 & \quad \hbox{ in $\Omega$}, \\ u =0 & \quad \hbox{ on $\partial \Omega$}, \end{cases} $$ when $\Omega \subset \mathbb{R}^N$ is a convex domain. In particular, in the subhomogeneous case $q \in [0,p-1]$, the solution $u$ inherits concavity properties from $a$ whenever assumed, while it is proved to be concave up to an error if $a$ is near to a constant. More general cases are also taken into account, including a wider class of nonlinearities. These results generalize some contained in [Kennington, Indiana Univ. Math. J., 1985] and [Sakaguchi, Ann. Sc. Norm. Super. Pisa, 1987]. Additionally, some results for the singular case $q \in [-1,0)$ and the superhomogeneous case $q>p-1$, $q \approx p-1$ are obtained. Some properties for the $p$-fractional Laplacian $(-\Delta)^s_p$, $s\in (0,1)$, $s \approx 1$, are shown as well. We highlight that some results are new even in the semilinear case $p=2$; in some of these cases, we deduce also uniqueness (and nondegeneracy) of the critical point of $u$.

From science to industry, teamwork plays a crucial role in knowledge production and innovation. Most studies consider teams as static groups of individuals, thereby failing to capture how the micro-dynamics of collaborative processes and organizational changes determine team success. Here, we leverage fine-grained temporal data on software development teams to gain insights into the dynamics of online collaborative projects. Our analysis reveals an uneven workload distribution in teams, with stronger heterogeneity correlated with higher success, and the early emergence of a lead developer carrying out the majority of work. Moreover, we find that a sizeable fraction of projects experience a change of lead developer, with such a transition being more likely in projects led by inexperienced users. Finally, we show that leadership change is associated with faster success growth, in particular for the least successful projects. Our work contributes to a deeper understanding of the link between team evolution and success in collaborative processes.

According to traditional balance theory, individual social actors avoid establishing triads with an odd number of negative links. Generalising, mesoscopic balance is realised when the nodes of a signed graph can be grouped into positively connected subsets, mutually connected by negative links. If this prescription is interpreted rigidly without allowing for statistical noise, it quickly dismisses most real graphs as frustrated. As an alternative, a relaxed, yet qualitative, definition of balance has been advanced. After rephrasing both variants in statistically testable terms, we propose an inference scheme to unambiguously assess if a signed graph is traditionally or relaxedly balanced., Comment: 20 pages, 13 figures, 1 table

We present an analysis of the HST COS spectrum of IZw1 aiming to probe the absorbing medium associated with the active galactic nucleus (AGN). We fitted the emission spectrum and performed spectral analysis of the identified absorption features to derive the corresponding ionic column densities and covering fractions of the associated outflows. We employed photoionisation modelling to constrain the total column density and the ionisation parameter of four detected kinematic components. By investigating the implications of the results together with the observed kinematic properties of both emission and absorption features, we derived constraints on the structure and geometry of the absorbing medium in the AGN environment. We find and characterise absorption line systems from outflowing ionised gas in four distinct kinematic components, located at -60, -280, -1950, and -2900 km/s with respect to the source rest frame. While the two slower outflows are consistent with a full covering of the underlying radiation source, the well-constrained doublet line ratios of the faster two, higher column density, outflows suggest partial covering, with a covering fraction of C_f~0.4. The faster outflows show also line-locking in the NV doublet, a signature of acceleration via line absorption. This makes IZw1 possibly the closest object that shows evidence for hosting line-driven winds. The observed -1950 km/s absorption is likely due to the same gas as an X-ray warm absorber. Furthermore, the behaviour in UV and X-ray bands implies that this outflow has a clumpy structure. We find that the highly asymmetric broad emission lines in IZw1, indicative of a collimated, outflowing broad line region, are covered by the absorbing gas. Finally, the strongest UV--X-ray absorber may be connected to some of the blueshifted line emission, indicative of a more spatially extended structure of this ionised medium., Comment: Accepted for publication in A&A

Empirical complex systems are widely assumed to be characterized not only by pairwise interactions, but also by higher-order (group) interactions that affect collective phenomena, from metabolic reactions to epidemics. Nevertheless, higher-order networks' superior descriptive power -- compared to classical pairwise networks -- comes with a much increased model complexity and computational cost. Consequently, it is of paramount importance to establish a quantitative method to determine when such a modeling framework is advantageous with respect to pairwise models, and to which extent it provides a parsimonious description of empirical systems. Here, we propose a principled method, based on information compression, to analyze the reducibility of higher-order networks to lower-order interactions, by identifying redundancies in diffusion processes while preserving the relevant functional information. The analysis of a broad spectrum of empirical systems shows that, although some networks contain non-compressible group interactions, others can be effectively approximated by lower-order interactions -- some technological and biological systems even just by pairwise interactions. More generally, our findings mark a significant step towards minimizing the dimensionality of models for complex systems

This Letter presents results of a search for the mixing of a sub-eV sterile neutrino with three active neutrinos based on the full data sample of the Daya Bay Reactor Neutrino Experiment, collected during 3158 days of detector operation, which contains $5.55 \times 10^{6}$ reactor \anue candidates identified as inverse beta-decay interactions followed by neutron-capture on gadolinium. The analysis benefits from a doubling of the statistics of our previous result and from improvements of several important systematic uncertainties. No significant oscillation due to mixing of a sub-eV sterile neutrino with active neutrinos was found. Exclusion limits are set by both Feldman-Cousins and CLs methods. Light sterile neutrino mixing with $\sin^2 2\theta_{14} \gtrsim 0.01$ can be excluded at 95\% confidence level in the region of $0.01$ eV$^2 \lesssim |\Delta m^{2}_{41}| \lesssim 0.1 $ eV$^2$. This result represents the world-leading constraints in the region of $2 \times 10^{-4}$ eV$^2 \lesssim |\Delta m^{2}_{41}| \lesssim 0.2 $ eV$^2$., Comment: 7 pages, 4 figures, 1 table

The supermassive black holes ($M_{\rm BH} \sim 10^{6}$$-$$10^{10}~M_\odot$) that power luminous active galactic nuclei (AGNs), i.e., quasars, generally show a correlation between thermal disk emission in the ultraviolet (UV) and coronal emission in hard X-rays. In contrast, some "massive" black holes (mBHs; $M_{\rm BH} \sim 10^{5}$$-$$10^{6}~M_\odot$) in low-mass galaxies present curious X-ray properties with coronal radiative output up to 100$\times$ weaker than expected. To examine this issue, we present a pilot study incorporating Very Large Array radio observations of a sample of 18 high-accretion-rate (Eddington ratios $L_{\rm bol}/L_{\rm Edd} > 0.1$), mBH-powered AGNs ($M_{\rm BH} \sim 10^{6}~M_\odot$) with Chandra X-ray coverage. Empirical correlations previously revealed in samples of radio-quiet, high-Eddington AGNs indicate that the radio$-$X-ray luminosity ratio, $L_{\rm R}/L_{\rm X}$, is approximately constant. Through multiwavelength analysis, we instead find that the X-ray-weaker mBHs in our sample tend toward larger values of $L_{\rm R}/L_{\rm X}$ even though they remain radio-quiet per their optical$-$UV properties. This trend results in a tentative but highly intriguing correlation between $L_{\rm R}/L_{\rm X}$ and X-ray weakness, which we argue is consistent with a scenario in which X-rays may be preferentially obscured from our line of sight by a "slim" accretion disk. We compare this observation to weak emission-line quasars (AGNs with exceptionally weak broad-line emission and a significant X-ray-weak fraction) and conclude by suggesting that our results may offer a new observational signature for finding high-accretion-rate AGNs., Comment: Accepted for publication in ApJ. 26 pages, 8 figures, 6 tables

We introduce QUANTISENC, a fully configurable open-source software-defined digital quantized spiking neural core architecture to advance research in neuromorphic computing. QUANTISENC is designed hierarchically using a bottom-up methodology with multiple neurons in each layer and multiple layers in each core. The number of layers and neurons per layer can be configured via software in a top-down methodology to generate the hardware for a target spiking neural network (SNN) model. QUANTISENC uses leaky integrate and fire neurons (LIF) and current-based excitatory and inhibitory synapses (CUBA). The nonlinear dynamics of a neuron can be configured at run-time via programming its internal control registers. Each neuron performs signed fixed-point arithmetic with user-defined quantization and decimal precision. QUANTISENC supports all-to-all, one-to-one, and Gaussian connections between layers. Its hardware-software interface is integrated with a PyTorch-based SNN simulator. This integration allows to define and train an SNN model in PyTorch and evaluate the hardware performance (e.g., area, power, latency, and throughput) through FPGA prototyping and ASIC design. The hardware-software interface also takes advantage of the layer-based architecture and distributed memory organization of QUANTISENC to enable pipelining by overlapping computations on streaming data. Overall, the proposed software-defined hardware design methodology offers flexibility similar to that of high-level synthesis (HLS), but provides better hardware performance with zero hardware development effort. We evaluate QUANTISENC using three spiking datasets and show its superior performance against state-of the-art designs.

Graph representation learning has rapidly emerged as a pivotal field of study. Despite its growing popularity, the majority of research has been confined to embedding single-layer graphs, which fall short in representing complex systems with multifaceted relationships. To bridge this gap, we introduce MPXGAT, an innovative attention-based deep learning model tailored to multiplex graph embedding. Leveraging the robustness of Graph Attention Networks (GATs), MPXGAT captures the structure of multiplex networks by harnessing both intra-layer and inter-layer connections. This exploitation facilitates accurate link prediction within and across the network's multiple layers. Our comprehensive experimental evaluation, conducted on various benchmark datasets, confirms that MPXGAT consistently outperforms state-of-the-art competing algorithms.

We present new, simple analytical formulas to accurately describe light rays in spherically symmetric static spacetimes. These formulas extend those introduced by Beloborodov and refined by Poutanen for the Schwarzschild metric. Our enhanced formulas are designed to be applicable to a broader range of spacetimes, making them particularly valuable for describing phenomena around compact objects like neutron stars and black holes. As an illustration of their application, we present analytical studies of images of thin accretion disks surrounding black holes and explore their associated polarimetry., Comment: Published version. Minor typos in two equations corrected

Changing-look (CL) Active Galactic Nuclei (AGNs), characterized by appearance/disappearance of broad emission lines in the span of a few years, present a challenge for the AGN unified model, whereby the Type 1 vs. Type 2 dichotomy results from orientation effects alone. We present a systematic study of a large sample of spectroscopically classified AGNs, using optical variability data from the Zwicky Transient Facility (ZTF) as well as follow-up spectroscopy data. We demonstrate that Type 1 vs. 2 AGN can be neatly separated on the basis of the variability metric $\sigma_{\rm QSO}$, which quantifies the resemblance of a light curve to a damp random walk model. For a small sub-sample, however, the ZTF light curves are inconsistent with their previous classification, suggesting the occurrence of a CL event. Specifically, we identify 35 (12) turn-on (turn-off) CL AGN candidates at $z < 0.35$. Based on follow-up spectroscopy, we confirm 17 (4) turn-on (turn-off) CL AGNs out of 21 (5) candidates, presenting a high success rate of our method. Our results suggest that the occurrence rate of CL AGNs is $\sim$0.3% over timescales of 5 to 20 years, and confirm that the CL transition typically occurs at the Eddington ratio of $\leq 0.01$., Comment: 23 pages, 12 figures. Accepted by ApJ

Ferrite nanoparticles serve as potent heterogeneous Fenton-like catalysts, producing reactive oxygen species (ROS) for decomposing organic pollutants. We investigated the impact of temperature and copper content on the catalytic activity of nanoparticles with different oxidation states of iron. Via solvothermal synthesis, we fabricated copper-doped magnetite (Cu$_x$Fe$_{3-x}$O$_4$) with a Fe$^{2+}$/Fe ratio ~0.33 for the undoped system. Using a microwave-assisted method, we produced copper-doped oxidized ferrites, yielding a Fe$^{2+}$/Fe ratio of ~0.11 for the undoped nanoparticles. The ROS generated by the catalyst were identified and quantified by electron paramagnetic resonance, while optical spectroscopy allowed us to evaluate its effectiveness for the degradation of a model organic dye. At room temperature, the magnetite nanoparticles exhibited the most $\cdot$OH radical production and achieved almost 90% dye discoloration in 2 hours. This efficiency decreased with increasing Cu concentration, concurrently with a decrease in $\cdot$OH generation. Conversely, above room temperature, Cu-doped nanoparticles significantly enhance the dye degradation, reaching 100% discoloration at 90$^\circ$C. This enhancement is accompanied by a systematic increase in the kinetic constants, obtained from reaction equations, with Cu doping. This study highlights the superior stability and high-temperature catalytic advantages of copper ferrite holding promise for enhancing the performance of nanocatalysts for decomposing organic contaminants., Comment: 40 pages, 11 figures