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Quantum spin models with a large number of interactions per spin are frequently encountered in modern many-body quantum optical systems like arrays of Rydberg atoms, atom-cavity systems or trapped ion crystals. For theoretical analysis the mean-field (MF) approximation is routinely applied. However, except in the exotic case of infinite connectivity, its results are not quantitatively reliable. Here we present a systematic correction to MF theory based on diagrammatic perturbation theory for quantum spin correlators in thermal equilibrium. Our analytic results are universally applicable for any lattice geometry and spin-length S. We provide pre-computed and easy-to-use building blocks for Ising, Heisenberg and transverse field Ising models in the the symmetry-unbroken regime. We showcase the methods quality and simplicity by computing magnetic phase boundaries and excitations gaps. We also treat the Dicke-Ising model of ground-state superradiance where we show that corrections to the MF phase boundary vanish., Comment: 17 pages, comments welcome!

Many reaction-diffusion systems in various applications exhibit traveling wave solutions that evolve on multiple spatio-temporal scales. These traveling wave solutions are crucial for understanding the underlying dynamics of the system. In this work, we present sixth-order weighted essentially non-oscillatory (WENO) methods within the finite difference framework to solve reaction-diffusion systems. The WENO method allows us to use fewer grid points and larger time steps compared to classical finite difference methods. Our focus is on solving the reaction-diffusion system for the traveling wave solution with the sharp front. Although the WENO method is popular for hyperbolic conservation laws, especially for problems with discontinuity, it can be adapted for the equations of parabolic type, such as reaction-diffusion systems, to effectively handle sharp wave fronts. Thus, we employed the WENO methods specifically developed for equations of parabolic type. We considered various reaction-diffusion equations, including Fisher's, Zeldovich, Newell-Whitehead-Segel, bistable equations, and the Lotka-Volterra competition-diffusion system, all of which yield traveling wave solutions with sharp wave fronts. Numerical examples in this work demonstrate that the central WENO method is highly more accurate and efficient than the commonly used finite difference method. We also provide an analysis related to the numerical speed of the sharp propagating front in the Newell-Whitehead-Segel equation. The overall results confirm that the central WENO method is highly efficient and is recommended for solving reaction-diffusion equations with sharp wave fronts.

Semi-Dirac materials in 2D present an anisotropic dispersion relation, linear along one direction and quadratic along the perpendicular one. This study explores the topological properties and the influence of disorder in a 2D semi-Dirac Hamiltonian. Anisotropic edge states appear only in one direction. Their topological protection can be rigorously founded on the Zak phase of the one-dimensional reduction of the semi-Dirac Hamiltonian, parametrically depending on one of the momenta. In general, only a single value of the momentum is topologically protected so these systems can be considered as high order momentum topological insulators. We explore the dependence on the disorder of the edge states and the robustness of the topological protection in these materials. We also explore the consequences of the high order topological protection in momentum space for the transport properties in a two-terminal configuration., Comment: 10 pages, 10 figures

Mitigating biases in generative AI and, particularly in text-to-image models, is of high importance given their growing implications in society. The biased datasets used for training pose challenges in ensuring the responsible development of these models, and mitigation through hard prompting or embedding alteration, are the most common present solutions. Our work introduces a novel approach to achieve diverse and inclusive synthetic images by learning a direction in the latent space and solely modifying the initial Gaussian noise provided for the diffusion process. Maintaining a neutral prompt and untouched embeddings, this approach successfully adapts to diverse debiasing scenarios, such as geographical biases. Moreover, our work proves it is possible to linearly combine these learned latent directions to introduce new mitigations, and if desired, integrate it with text embedding adjustments. Furthermore, text-to-image models lack transparency for assessing bias in outputs, unless visually inspected. Thus, we provide a tool to empower developers to select their desired concepts to mitigate. The project page with code is available online., Comment: Accepted at CVPR workshop 2024, proceedings of ReGenAI: First Workshop on Responsible Generative AI

Partial differential equations (PDEs) with multiple scales or those defined over sufficiently large domains arise in various areas of science and engineering and often present problems when approximating the solutions numerically. Machine learning techniques are a relatively recent method for solving PDEs. Despite the increasing number of machine learning strategies developed to approximate PDEs, many remain focused on relatively small domains. When scaling the equations, a large domain is naturally obtained, especially when the solution exhibits multiscale characteristics. This study examines two-scale equations whose solution structures exhibit distinct characteristics: highly localized in some regions and significantly flat in others. These two regions must be adequately addressed over a large domain to approximate the solution more accurately. We focus on the vanishing gradient problem given by the diminishing gradient zone of the activation function over large domains and propose a stratified sampling algorithm to address this problem. We compare the uniform random classical sampling method over the entire domain and the proposed stratified sampling method. The numerical results confirm that the proposed method yields more accurate and consistent solutions than classical methods., Comment: 35 pages, 14 figures

Dissipative light-matter systems can display emergent collective behavior. Here, we report a $\mathbb{Z}_2$-symmetry-breaking phase transition in a system of multi-level $^{87}$Rb atoms strongly coupled to a weakly driven two-mode optical cavity. In the symmetry-broken phase, non-ergodic dynamics manifests in the emergence of multiple stationary states with disjoint basins of attraction. This feature enables the amplification of a small atomic population imbalance into a characteristic macroscopic cavity transmission signal. Our experiment does not only showcase strongly dissipative atom-cavity systems as platforms for probing non-trivial collective many-body phenomena, but also highlights their potential for hosting technological applications in the context of sensing, density classification, and pattern retrieval dynamics within associative memories.

Maxwell's equations are solved when the amplitude and phase of the electromagnetic field are determined at all points in space. Generally, the Stokes parameters can only capture the amplitude and polarization state of the electromagnetic field in the radiation (far) zone. Therefore, the measurement of the Stokes parameters is, in general, insufficient to solve Maxwell's equations. In this Letter, we solve Maxwell's equations for a set of objects widely used in Nanophotonics using the Stokes parameters alone. Our method for solving Maxwell's equations endows the Stokes parameters an even more fundamental role in the electromagnetic scattering theory.

Deep Neural Networks (DNN) have shown great promise in many classification applications, yet are widely known to have poorly calibrated predictions when they are over-parametrized. Improving DNN calibration without comprising on model accuracy is of extreme importance and interest in safety critical applications such as in the health-care sector. In this work, we show that decoupling the training of feature extraction layers and classification layers in over-parametrized DNN architectures such as Wide Residual Networks (WRN) and Visual Transformers (ViT) significantly improves model calibration whilst retaining accuracy, and at a low training cost. In addition, we show that placing a Gaussian prior on the last hidden layer outputs of a DNN, and training the model variationally in the classification training stage, even further improves calibration. We illustrate these methods improve calibration across ViT and WRN architectures for several image classification benchmark datasets., Comment: Proceedings of the 41 st International Conference on Machine Learning (ICML) 2024

We establish the concept of topological pumping in one-dimensional systems with long-range interactions and apply it to the transport of a photon in quantum optical systems. In our theoretical investigation, we introduce an extended version of the Rice-Mele model with all-to-all exchange interactions. By analyzing its properties, we identify the general conditions for topological pumping and demonstrate the topologically protected and dispersionless transport of a photon on a one-dimensional emitter chain. As concrete examples, we investigate three different popular quantum optics platforms, namely Rydberg atom lattices, dense lattices of atoms excited to low-lying electronic states, and atoms coupled to waveguides, using experimentally relevant parameters. We observe that despite the long-ranged character of the dipole-dipole interactions, topological pumping facilitates the transport of a photon with a fidelity per cycle which can reach 99.9%. Moreover, we find that the photon pumping process remains topologically protected against local disorder in the coupling rates., Comment: 16 pages, 9 figures, comments welcome

Alzheimer's disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of Mild Cognitive Impairment (MCI) and early-stage AD. This study delves into the potential of utilizing sleep-related electroencephalography (EEG) signals acquired through polysomnography (PSG) for the early detection of AD. Our primary focus is on exploring semi-supervised Deep Learning techniques for the classification of EEG signals due to the clinical scenario characterized by the limited data availability. The methodology entails testing and comparing the performance of semi-supervised SMATE and TapNet models, benchmarked against the supervised XCM model, and unsupervised Hidden Markov Models (HMMs). The study highlights the significance of spatial and temporal analysis capabilities, conducting independent analyses of each sleep stage. Results demonstrate the effectiveness of SMATE in leveraging limited labeled data, achieving stable metrics across all sleep stages, and reaching 90% accuracy in its supervised form. Comparative analyses reveal SMATE's superior performance over TapNet and HMM, while XCM excels in supervised scenarios with an accuracy range of 92 - 94%. These findings underscore the potential of semi-supervised models in early AD detection, particularly in overcoming the challenges associated with the scarcity of labeled data. Ablation tests affirm the critical role of spatio-temporal feature extraction in semi-supervised predictive performance, and t-SNE visualizations validate the model's proficiency in distinguishing AD patterns. Overall, this research contributes to the advancement of AD detection through innovative Deep Learning approaches, highlighting the crucial role of semi-supervised learning in addressing data limitations., Comment: 12 pages, 14 figures. Submitted to IEEE Biomedical and Health Informatics for publication

General Effect Modelling (GEM) is an umbrella over different methods that utilise effects in the analyses of data with multiple design variables and multivariate responses. To demonstrate the methodology, we here use GEM in gene expression data where we use GEM to combine data from different cohorts and apply multivariate analysis of the effects of the targeted disease across the cohorts. Omics data are by nature multivariate, yet univariate analysis is the dominating approach used for such data. A major challenge in omics data is that the number of features such as genes, proteins and metabolites are often very large, whereas the number of samples is limited. Furthermore, omics research aims to obtain results that are generically valid across different backgrounds. The present publication applies GEM to address these aspects. First, we emphasise the benefit of multivariate analysis for multivariate data. Then we illustrate the use of GEM to combine data from two different cohorts for multivariate analysis across the cohorts, and we highlight that multivariate analysis can detect information that is lost by univariate validation., Comment: 12 pages

Implicit generative models have the capability to learn arbitrary complex data distributions. On the downside, training requires telling apart real data from artificially-generated ones using adversarial discriminators, leading to unstable training and mode-dropping issues. As reported by Zahee et al. (2017), even in the one-dimensional (1D) case, training a generative adversarial network (GAN) is challenging and often suboptimal. In this work, we develop a discriminator-free method for training one-dimensional (1D) generative implicit models and subsequently expand this method to accommodate multivariate cases. Our loss function is a discrepancy measure between a suitably chosen transformation of the model samples and a uniform distribution; hence, it is invariant with respect to the true distribution of the data. We first formulate our method for 1D random variables, providing an effective solution for approximate reparameterization of arbitrary complex distributions. Then, we consider the temporal setting (both univariate and multivariate), in which we model the conditional distribution of each sample given the history of the process. We demonstrate through numerical simulations that this new method yields promising results, successfully learning true distributions in a variety of scenarios and mitigating some of the well-known problems that state-of-the-art implicit methods present., Comment: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) 2024

In this work we explore the propagation of massive scalar fields on some wormhole backgrounds. On one side, we consider the Bronnikov-Ellis wormhole solution and wormhole geometries with a non-constant redshift function by introducing a gravitational mass $M$, which goes over into the Bronnikov-Ellis wormhole when the gravitational mass parameter vanishes. We employ the continued fraction method to calculate accurately the quasinormal frequencies of massive scalar fields, particularly focusing on low values of the angular number, and we show an anomalous behaviour of the decay rate of the quasinormal frequencies, for $n \geq \ell$. Also, we show that for a massive scalar field and $M \neq 0$ the effective potential allows potential wells for some values of the parameters which support bound states, which are obtained using the continued fraction method and they are characterized by having only a frequency of oscillation and they do not decay; however, for the Bronnikov-Ellis wormhole the effective potential do not support bound states. On the other side, we consider a wormhole geometry which is an exact solution of $f(R)$ modified gravity. For this geometry the quasinormal frequencies can be obtained analytically, being the longest-lived modes the ones with lowest angular number $\ell$. So, in this wormhole background the anomalous behaviour is avoided., Comment: Version accepted for publication in PRD. arXiv admin note: text overlap with arXiv:2205.06079

A bimetallic Au-Pd catalyst supported on ceriazirconia with Au:Pd molar ratio 0.8 has been synthesized using a simultaneous deposition-precipitation method and oxidized at 250, 450, and 700 $^\circ$C in order to modify its particle size, nanostructure, and composition. Combined Xray energy dispersive spectroscopy and Xray photoelectron spectroscopy analysis clearly evidence that the bimetallic Au-Pd catalyst oxidized at 250 $^\circ$C is made up of a mixture of monometallic Au and Pd and bimetallic Au-Pd nanoparticles with Au:Pd ratios varying over a wide range. Increasing oxidation temperature leads to a stronger interaction between Au and Pd. Meanwhile, a slight increase of particle size and a narrowing of the Au:Pd ratio in the bimetallic nanoparticles take place. Compared with titania and activated carbon supports, the resistance against sintering at high temperatures of Au-Pd metal particles supported on ceriazirconia is proven to be higher. A synergistic effect has been observed for selective oxidation of benzyl alcohol on these catalysts. The catalytic activity decreases only slightly after oxidation at 450 $^\circ$C. However, oxidation at 700 $^\circ$C results in much lower catalytic activity. Migration of Pd onto Au particles during oxidation of benzyl alcohol enhances the catalytic activity of a physical mixture of monometallic Au and Pd supported on ceriazirconia catalysts. This fact, jointly with an analysis of the intrinsic activity, reveals the influence of the actual nature of Au-Pd interactions in the bimetallic particles, which points to higher activity of Au@Pd or Au@Pd@Pd nanostructures on ceria-zirconia support., Comment: 12 pages, 8 figures

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly with respect to the input, this is however lost in single-step AT. To address CO in single-step AT, several methods have been proposed to enforce local linearity of the loss via regularization. However, these regularization terms considerably slow down training due to Double Backpropagation. Instead, in this work, we introduce a regularization term, called ELLE, to mitigate CO effectively and efficiently in classical AT evaluations, as well as some more difficult regimes, e.g., large adversarial perturbations and long training schedules. Our regularization term can be theoretically linked to curvature of the loss function and is computationally cheaper than previous methods by avoiding Double Backpropagation. Our thorough experimental validation demonstrates that our work does not suffer from CO, even in challenging settings where previous works suffer from it. We also notice that adapting our regularization parameter during training (ELLE-A) greatly improves the performance, specially in large $\epsilon$ setups. Our implementation is available in https://github.com/LIONS-EPFL/ELLE ., Comment: Accepted in ICLR 2024

The quantization of a massive spin $1/2$ field that satisfies the Klein-Gordon equation is studied. The framework is consistent, provided it is formulated as a pseudo-hermitian quantum field theory by the redefinition of the field dual and the identification of an operator that modifies the inner product of states in Hilbert space to preserve a real energy spectrum and unitary evolution. Since the fermion field has mass dimension one, the theory admits renormalizable fermion self-interactions., Comment: 4 pages

The multipolar expansion of the electromagnetic field plays a key role in the study of light-matter interactions. All the information about the radiation and coupling between the incident wavefield and the object is embodied in the electric and magnetic scattering coefficients $\{a_{\ell m}, b_{\ell m} \}$ of the expansion. However, the experimental determination of $\{a_{\ell m}, b_{\ell m} \}$ requires measuring the components of the scattered field in all directions, something that is exceptionally challenging. Here, we demonstrate that a single measurement of the Stokes vector unlocks access to the quadrivector $ \mathbf{D}_{\ell m} = \left[|a_{\ell m}|^2, |b_{\ell m}|^2, \Re \{ a_{\ell m} b^*_{\ell m} \}, \Im \{ a_{\ell m} b^*_{\ell m} \} \right]$. Thus, our Stokes polarimetry method allows us to capture $|a_{\ell m}|^2$ and $|b_{\ell m}|^2$ separately, a distinction that can not be achieved by measuring the total energy of the scattered field via an integrating sphere. Importantly, we demonstrate the robustness of our Stokes polarimetry method, showing its fidelity with just two measurements of the Stokes vector at different scattering angles. Our findings, supported by analytical theory and exact numerical simulations, can find applications in Nanophotonics and greatly facilitate routine light-scattering measurements in optical laboratories.

The best encoding is the one that is interpretable in nature. In this work, we introduce a novel model that incorporates an interpretable bottleneck-termed the Filter Bank (FB)-at the outset of a Variational Autoencoder (VAE). This arrangement compels the VAE to attend on the most informative segments of the input signal, fostering the learning of a novel encoding ${f_0}$ which boasts enhanced interpretability and clusterability over traditional latent spaces. By deliberately constraining the VAE with this FB, we intentionally constrict its capacity to access broad input domain information, promoting the development of an encoding that is discernible, separable, and of reduced dimensionality. The evolutionary learning trajectory of ${f_0}$ further manifests as a dynamic hierarchical tree, offering profound insights into cluster similarities. Additionally, for handling intricate data configurations, we propose a tailored decoder structure that is symmetrically aligned with FB's architecture. Empirical evaluations highlight the superior efficacy of ISVAE, which compares favorably to state-of-the-art results in clustering metrics across real-world datasets.

The urban spatial structure represents the distribution of public and private spaces in cities and how people move within them. While it usually evolves slowly, it can change fast during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. This work presents an approach to delineate such urban dynamics in quasi-real-time through a human mobility metric, the mobility centrality index $\Delta KS$. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. Results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, $KS_{HBT}$, which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure.