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The motion of a fluid induced by thermal gradients in the absence of driving forces is known as thermo-osmosis. The physical explanation of this phenomenon stems from the emergence of gradients in the tangential pressure due to the presence of a confining surface. The microscopic origin of the effect was recently elucidated in the framework of linear response theory. Here, by use of conservation laws, we provide an explicit solution of the equations governing the fluid flow at stationarity in slab geometry, expressing the thermo-osmotic coefficient as the integrated mass current-heat current correlation function (which vanishes in the bulk). A very simple expression for the pressure gradient in terms of equilibrium properties is also derived. To test the theoretical predictions in a controlled setting, we performed extensive nonequilibrium molecular dynamics simulations in two dimensions. Few simple models of wall-particle interactions are examined and the resulting pressure drop and velocity profile are compared with the theoretical predictions both in the liquid and in the gas regime., Comment: 20 pages, 10 figures

The $U(1)$ Dirac spin liquid might realize an exotic phase of matter whose low-energy properties are described by quantum electrodynamics in $2+1$ dimensions, where gapless modes exists but spinons and gauge fields are strongly coupled. Its existence has been proposed in frustrated Heisenberg models in presence of frustrating super-exchange interactions, by the (Abrikosov) fermionic representation of the spin operators [X.-G. Wen, Phys. Rev. B 65, 165113 (2002)}], supplemented by the Gutzwiller projection. Here, we construct charge-$Q$ monopole excitations in the Heisenberg model on the triangular lattice with nearest- ($J_1$) and next-neighbor ($J_2$) couplings. In the highly frustrated regime, singlet and triplet monopoles with $Q=1$ become gapless in the thermodynamic limit; in addition, the energies for generic $Q$ agree with field-theoretical predictions, obtained for a large number of gapless fermion modes. Finally, we consider localized gauge excitations, in which magnetic $\pi$-fluxes are concentrated in the triangular plaquettes (in analogy with $\mathbb{Z}_2$ visons), showing that these kind of states do not play a relevant role at low-energies. All our findings lend support to a stable $U(1)$ Dirac spin liquid in the $J_1-J_2$ Heisenberg model on the triangular lattice., Comment: 9 pages, 7 figures

We consider emergence from the perspective of dynamics: states of a system evolving with time. We focus on the role of a decomposition of wholes into parts, and attempt to characterize relationships between levels without reference to whether higher-level properties are "novel" or "unexpected." We offer a classification of different varieties of emergence, with and without new ontological elements at higher levels., Comment: Submitted to a volume on Real Patterns (Tyler Milhouse, ed.), to be published by MIT Press

Due to the recent increase in interest in Financial Technology (FinTech), applications like credit default prediction (CDP) are gaining significant industrial and academic attention. In this regard, CDP plays a crucial role in assessing the creditworthiness of individuals and businesses, enabling lenders to make informed decisions regarding loan approvals and risk management. In this paper, we propose a workflow-based approach to improve CDP, which refers to the task of assessing the probability that a borrower will default on his or her credit obligations. The workflow consists of multiple steps, each designed to leverage the strengths of different techniques featured in machine learning pipelines and, thus best solve the CDP task. We employ a comprehensive and systematic approach starting with data preprocessing using Weight of Evidence encoding, a technique that ensures in a single-shot data scaling by removing outliers, handling missing values, and making data uniform for models working with different data types. Next, we train several families of learning models, introducing ensemble techniques to build more robust models and hyperparameter optimization via multi-objective genetic algorithms to consider both predictive accuracy and financial aspects. Our research aims at contributing to the FinTech industry in providing a tool to move toward more accurate and reliable credit risk assessment, benefiting both lenders and borrowers.

Understanding quantum magnetism in two-dimensional systems represents a lively branch in modern condensed-matter physics. In the presence of competing super-exchange couplings, magnetic order is frustrated and can be suppressed down to zero temperature. Still, capturing the correct nature of the exact ground state is a highly complicated task, since energy gaps in the spectrum may be very small and states with different physical properties may have competing energies. Here, we introduce a variational Ansatz for two-dimensional frustrated magnets by leveraging the power of representation learning. The key idea is to use a particular deep neural network with real-valued parameters, a so-called Transformer, to map physical spin configurations into a high-dimensional feature space. Within this abstract space, the determination of the ground-state properties is simplified and requires only a shallow output layer with complex-valued parameters. We illustrate the efficacy of this variational Ansatz by studying the ground-state phase diagram of the Shastry-Sutherland model, which captures the low-temperature behavior of SrCu$_2$(BO$_3$)$_2$ with its intriguing properties. With highly accurate numerical simulations, we provide strong evidence for the stabilization of a spin-liquid between the plaquette and antiferromagnetic phases. In addition, a direct calculation of the triplet excitation at the $\Gamma$ point provides compelling evidence for a gapless spin liquid. Our findings underscore the potential of Neural-Network Quantum States as a valuable tool for probing uncharted phases of matter, and open up new possibilities for establishing the properties of many-body systems., Comment: 14 pages, 16 figures and 1 table

We show that a two-body Jastrow wave function is able to capture the ground-state properties of the $S=1$ antiferromagnetic Heisenberg chain with the single-ion anisotropy term, in both the topological and trivial phases. Here, the optimized Jastrow pseudo potential assumes a very simple form in Fourier space, i.e., $v_{q} \approx 1/q^2$, which is able to give rise to a finite string-order parameter in the topological regime. The results are analysed by using an exact mapping from the quantum expectation values over the variational state to the classical partition function of the one-dimensional Coulomb gas of particles with charge $q=\pm 1$. Here, two phases are present at low temperatures: the first one is a diluted gas of dipoles (bound states of particles with opposite charges), which are randomly oriented (describing the trivial phase); the other one is a dense liquid of dipoles, which are aligned thanks to the residual dipole-dipole interactions (describing the topological phase, with the finite string order being related to the dipole alignment). Our results provide an insightful interpretation of the ground-state nature of the spin-1 antiferromagnetic Heisenberg model.

Boehmite, an aluminum oxide hydroxide $\gamma$-AlO(OH), is broadly used in the form of particulate dispersions in industrial applications, e.g., for the fabrication of ceramics and catalyst supports or as a binder for extrusion processes. Under acidic conditions, colloidal boehmite dispersions at rest form gels, i.e., space-spanning percolated networks that behave as soft solids at rest, and yet yield and flow like liquids under large enough deformations. Like many other colloidal gels, the solid-like properties of boehmite gels at rest are very sensitive to their previous mechanical history. Our recent work [Sudreau et al., J. Rheol. 66, 91-104 (2022), and Phys. Rev. Material 6, L042601 (2022)] has revealed such \textit{memory effects}, where the shear experienced prior to flow cessation drives the elasticity of boehmite gels: while gels formed following application of a shear rate $\dot\gamma_{\rm p}$ larger than a critical value $\dot\gamma_{\rm c}$ are insensitive to shear history, gels formed after application of $\dot\gamma_{\rm p}<\dot\gamma_{\rm c}$ display reinforced viscoelastic properties and non-negligible residual stresses. Here, we provide a microstructural scenario for these striking observations by coupling rheometry and small-angle X-ray scattering. Time-resolved measurements for $\dot\gamma_{\rm p} <\dot\gamma_{\rm c}$ show that scattering patterns develop an anisotropic shape that persists upon flow cessation, whereas gels exposed to $\dot\gamma_{\rm p}>\dot\gamma_{\rm c}$ display isotropic scattering patterns upon flow cessation. Moreover, as the shear rate applied prior to flow cessation is decreased below $\dot\gamma_{\rm c}$, the level of anisotropy frozen in the sample microstructure grows similarly to the viscoelastic properties, thus providing a direct link between mechanical reinforcement and flow-induced microstructural anisotropy., Comment: 13 pages, 10 figures

Speech patterns have been identified as potential diagnostic markers for neuropsychiatric conditions. However, most studies only compare a single clinical group to healthy controls, whereas clinical practice often requires differentiating between multiple potential diagnoses (multiclass settings). To address this, we assembled a dataset of repeated recordings from 420 participants (67 with major depressive disorder, 106 with schizophrenia and 46 with autism, as well as matched controls), and tested the performance of a range of conventional machine learning models and advanced Transformer models on both binary and multiclass classification, based on voice and text features. While binary models performed comparably to previous research (F1 scores between 0.54-0.75 for autism spectrum disorder, ASD; 0.67-0.92 for major depressive disorder, MDD; and 0.71-0.83 for schizophrenia); when differentiating between multiple diagnostic groups performance decreased markedly (F1 scores between 0.35-0.44 for ASD, 0.57-0.75 for MDD, 0.15-0.66 for schizophrenia, and 0.38-0.52 macro F1). Combining voice and text-based models yielded increased performance, suggesting that they capture complementary diagnostic information. Our results indicate that models trained on binary classification may learn to rely on markers of generic differences between clinical and non-clinical populations, or markers of clinical features that overlap across conditions, rather than identifying markers specific to individual conditions. We provide recommendations for future research in the field, suggesting increased focus on developing larger transdiagnostic datasets that include more fine-grained clinical features, and that can support the development of models that better capture the complexity of neuropsychiatric conditions and naturalistic diagnostic assessment., Comment: 24 pages, 5 figures

Improving phosphor photoluminescence efficiency is a key parameter to boost the performances of many optical devices. In this work, colloidal silver nanocubes, homogeneously spread on a luminescent surface, have proved to help both injecting and extracting light in and out of the photoluminescent layer and hence contributed significantly to the enhancement of the fluorescence. This approach has been applied to two materials: the well-known Y$_3$Al$_5$O$_{12}$:Ce yellow phosphor and an optical quartz. The emission efficiency, for sol-gel derived YAG:Ce layers, has increased of 80\% in the presence of an optimal nanoparticle density -- whereas for quartz, a weakly fluorescent material, the photoluminescence signal can be enhanced by a 200-fold factor. A physical analysis based on simulations shows that the disorder is an important factor and that the surface density of Ag nanoparticles is a crucial parameter.

We present the results of the broadband X-ray spectral analysis of simultaneous NuSTAR and XMM-Newton observations of four nearby Compton-thick active galactic nuclei (AGN) candidates selected from the Swift-Burst Alert Telescope (BAT) 150-month catalog. This work is part of a larger effort to identify and characterize all Compton-thick (NH >= 10^24 cm^-2) AGN in the local Universe (z < 0.05). We used three physically motivated models -- MYTorus, borus02, and UXClumpy -- to fit and characterize these sources. Of the four candidates analyzed, 2MASX J02051994-0233055 was found to be an unobscured (NH < 10^22 cm^-2) AGN, 2MASX J04075215-6116126 and IC 2227 to be Compton-thin (10^22 cm^-2 < NH < 10^24 cm^-2) AGN, and one, ESO 362-8, was confirmed to be a Compton-thick AGN. Additionally, every source was found to have a statistically significant difference between their line-of-sight and average torus hydrogen column density, further supporting the idea that the obscuring material in AGN is inhomogeneous. Furthermore, half of the sources in our sample (2MASX J02051994-0233055 and 2MASX J04075215-6116126) exhibited significant luminosity variation in the last decade, suggesting that this might be a common feature of AGN.

The ASTRI Mini-Array (MA) is an INAF project to build and operate a facility to study astronomical sources emitting at very high-energy in the TeV spectral band. The ASTRI MA consists of a group of nine innovative Imaging Atmospheric Cherenkov telescopes. The telescopes will be installed at the Teide Astronomical Observatory of the Instituto de Astrofisica de Canarias (IAC) in Tenerife (Canary Islands, Spain) on the basis of a host agreement with INAF. Thanks to its expected overall performance, better than those of current Cherenkov telescopes' arrays for energies above \sim 5 TeV and up to 100 TeV and beyond, the ASTRI MA will represent an important instrument to perform deep observations of the Galactic and extra-Galactic sky at these energies., Comment: 19 pages, 22 figures

The ASTRI (Astrofisica con Specchi a Tecnologia Replicante Italiana) Mini-Array will be composed of nine imaging atmospheric Cherenkov telescopes at the Observatorio del Teide site. The array will be best suited for astrophysical observations in the 0.3-200 TeV range with an angular resolution of few arc-minutes and an energy resolution of 10-15\%. A core-science programme in the first four years will be devoted to a limited number of key targets, addressing the most important open scientific questions in the very-high energy domain. At the same time, thanks to a wide field of view of about 10 degrees, ASTRI Mini-Array will observe many additional field sources, which will constitute the basis for the long-term observatory programme that will eventually cover all the accessible sky. In this paper, we review different astrophysical Galactic environments, e.g. pulsar wind nebulae, supernova remnants, and gamma-ray binaries, and show the results from a set of ASTRI Mini-Array simulations of some of these field sources made to highlight the expected performance of the array (even at large offset angles) and the important additional observatory science that will complement the core-science program., Comment: Accepted for publication in Journal of High-Energy Astrophysics

The ASTRI (Astrofisica con Specchi a Tecnologia Replicante Italiana) Project led by the Italian National Institute for Astrophysics (INAF) is developing and will deploy at the Observatorio del Teide a mini-array (ASTRI Mini-Array) composed of nine telescopes similar to the small-size dual-mirror Schwarzschild-Couder telescope (ASTRI-Horn) currently operating on the slopes of Mt. Etna in Sicily. The ASTRI Mini-Array will surpass the current Cherenkov telescope array differential sensitivity above a few tera-electronvolt (TeV), extending the energy band well above hundreds of TeV. This will allow us to explore a new window of the electromagnetic spectrum, by convolving the sensitivity performance with excellent angular and energy resolution figures. In this paper we describe the Core Science that we will address during the first four years of operation, providing examples of the breakthrough results that we will obtain when dealing with current open questions, such as the acceleration of cosmic rays, cosmology and fundamental physics and the new window, for the TeV energy band, of the time-domain astrophysics., Comment: Published in Journal of High Energy Astrophysics. 46 Figures, 7 Tables

Thermal forces drive several nonequilibrium phenomena able to set a fluid in motion without pressure gradients. Although the most celebrated effect is thermophoresis, also known as Ludwig-Soret effect, probably the simplest example where thermal forces are at play is thermo-osmosis: The motion of a {\it confined} fluid exclusively due to the presence of a temperature gradient. We present a concise but complete derivation of the microscopic theory of thermo-osmosis based on linear response theory. This approach is applied to a simple fluid confined in a slab geometry, mimicking the flow through a pore in a membrane separating two fluid reservoirs at different temperatures. We consider both the case of an open channel, where the fluid can flow freely, and that of a closed channel, where mass transport is inhibited and a pressure drop sets in at the boundaries. Quantitative results require the evaluation of generalized transport coefficients, but a preliminary check on a specific prediction of the theory has been successfully performed via nonequilibrium molecular dynamics simulations., Comment: 14 pages, 5 figures

Taking the clue from the modern theory of polarization [R. Resta, Rev. Mod. Phys. {\bf 66}, 899 (1994)], we identify an operator to distinguish between ${\mathbb Z}_2$-even (trivial) and ${\mathbb Z}_2$-odd (topological) insulators in two spatial dimensions. Its definition extends the position operator [R. Resta and S. Sorella, Phys. Rev. Lett. {\bf 82}, 370 (1999)], which was introduced in one-dimensional systems. We first show a few examples of non-interacting models, where single-particle wave functions are defined and allow for a direct comparison with standard techniques on large system sizes. Then, we illustrate its applicability for an interacting model on a small cluster, where exact diagonalizations are available. Its formulation in the Fock space allows a direct computation of expectation values over the ground-state wave function (or any approximation of it), thus allowing us to investigate generic interacting systems, such as strongly-correlated topological insulators., Comment: Published version

We present the combined Chandra and Swift-BAT spectral analysis of nine low-redshift (z <= 0.10), candidate heavily obscured active galactic nuclei (AGN) selected from the Swift-BAT 150-month catalog. We located soft (1-10 keV) X-ray counterparts to these BAT sources and joint fit their spectra with physically motivated models.The spectral analysis in the 1-150 keV energy band determined that all sources are obscured, with a line-of-sight column density NH >= 10^22 cm^-2 at a 90% confidence level. Four of these sources show significant obscuration with NH >= 10^23 cm^-2 and two additional sources are candidate Compton-thick Active Galactic Nuclei (CT-AGNs) with NH >= 10^24 cm^-2.These two sources, 2MASX J02051994-0233055 and IRAS 11058-1131, are the latest addition to the previous 3 CT-AGN candidates found using our strategy for soft X-ray follow-up of BAT sources. In here we present the results of our methodology so far, and analyze the effectiveness of applying different selection criteria to discover CT-AGN in the local Universe. Our selection criteria has a ~20% success rate of discovering heavily obscured AGN whose CT nature is confirmed by follow-up NuSTAR observations. This is much higher than the ~5% found in blind surveys.

The existence and stability of solitonic states in one-dimensional repulsive Bose-Einstein condensates is investigated within a fully many-body framework by considering the limit of infinite repulsion (Tonks-Girardeau gas). A class of stationary, shape-invariant states propagating at constant velocity are explicitly found and compared to the known solution of the Gross-Pitaevskii equation. The typical features attributed to nonlinearity are thus recovered in a purely linear theory, provided the full many-particle physics is correctly accounted for. However, the formation dynamics predicted by the Gross-Pitaevskii approximation considerably differs from the exact many-body evolution., Comment: 10 pages, 9 figures. This version matches the published version

To implement a good Content Based Image Retrieval (CBIR) system, it is essential to adopt efficient search methods. One way to achieve this results is by exploiting approximate search techniques. In fact, when we deal with very large collections of data, using an exact search method makes the system very slow. In this project, we adopt the Locality Sensitive Hashing (LSH) index to implement a CBIR system that allows us to perform fast similarity search on deep features. Specifically, we exploit transfer learning techniques to extract deep features from images; this phase is done using two famous Convolutional Neural Networks (CNNs) as features extractors: Resnet50 and Resnet50v2, both pre-trained on ImageNet. Then we try out several fully connected deep neural networks, built on top of both of the previously mentioned CNNs in order to fine-tuned them on our dataset. In both of previous cases, we index the features within our LSH index implementation and within a sequential scan, to better understand how much the introduction of the index affects the results. Finally, we carry out a performance analysis: we evaluate the relevance of the result set, computing the mAP (mean Average Precision) value obtained during the different experiments with respect to the number of done comparison and varying the hyper-parameter values of the LSH index.

We assess the ground-state phase diagram of the $J_1$-$J_2$ Heisenberg model on the kagome lattice by employing Gutzwiller-projected fermionic wave functions. Within this framework, different states can be represented, defined by distinct unprojected fermionic Hamiltonians that include hopping and pairing terms, as well as a coupling to local Zeeman fields to generate magnetic order. For $J_2=0$, the so-called U(1) Dirac state, in which only hopping is present (such as to generate a $\pi$-flux in the hexagons), has been shown to accurately describe the exact ground state [Y. Iqbal, F. Becca, S. Sorella, and D. Poilblanc, Phys. Rev. B 87, 060405 (2013); Y.-C. He, M. P. Zaletel, M. Oshikawa, and F. Pollmann, Phys. Rev. X 7, 031020 (2017)]. Here, we show that its accuracy improves in presence of a small antiferromagnetic super-exchange $J_2$, leading to a finite region where the gapless spin liquid is stable; then, for $J_2/J_1=0.11(1)$, a first-order transition to a magnetic phase with pitch vector ${\bf q}=(0,0)$ is detected, by allowing magnetic order within the fermionic Hamiltonian. Instead, for small ferromagnetic values of $|J_2|/J_1$, the situation is more contradictory. While the U(1) Dirac state remains stable against several perturbations in the fermionic part (i.e., dimerization patterns or chiral terms), its accuracy clearly deteriorates on small systems, most notably on $36$ sites where exact diagonalization is possible. Then, upon increasing the ratio $|J_2|/J_1$, a magnetically ordered state with $\sqrt{3} \times \sqrt{3}$ periodicity eventually overcomes the U(1) Dirac spin liquid. Within the ferromagnetic $J_{2}$ regime, evidence is shown in favor of a first-order transition at $J_2/J_1=-0.065(5)$., Comment: 9 pages, 6 figures, 1 table

We show that gapless spin liquids, which are potential candidates to describe the ground state of frustrated Heisenberg models in two dimensions, become trivial insulators on cylindrical geometries with an even number of legs. In particular, we report calculations for Gutzwiller-projected fermionic states on strips of square and kagome lattices. By choosing different boundary conditions for the fermionic degrees of freedom, both gapless and gapped states may be realized, the latter ones having a lower variational energy. The direct evaluation of static and dynamical correlation functions, as well as overlaps between different states, allows us to demonstrate the sharp difference between the ground-state properties obtained within cylinders or directly in the two-dimensional lattice. Our results shed light on the difficulty to detect bona fide gapless spin liquids in such cylindrical geometries., Comment: 9 pages, 10 figures

Indoor light is known to be a new energy source to power uW low consumption wireless sensor networks (WSNs). For wireless electronic devices that consume tens of mW, it is still challenging to harvest this amount of power in a low light indoor environment. The challenge comes from the fact that the irradiance is low, as well the fact that the source is a multi-spectral direct, reflective, and scattered mix of artificial and natural light, with intensity and composition varying in time. This article describes a method providing an evaluation of the potentially harvestable energy in real light environments. Measurements of indoor light spectral composition in real condition and optoelectrical characteristics of photovoltaic converters in a controlled environment are base on the method s calculation model. Real harvester prototypes based on GaAs commercial photovoltaic cells and power management integrated circuit, powering a commercial wireless e-ink display, have been conceived to compare with the model calculations. For two days of experimentation, model calculations achieve a mean absolute percentage error (MAPE) of 2 percents and 6 percents for Day 1 and Day 2, respectively. A method within this accuracy range would be a helpful tool to assist in designing light energy harvesting systems in such ways that they can be best tailored to the indoor lighting conditions to which they will be exposed.