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Variability in memristive devices based on h-BN dielectrics is studied in depth. Different numerical techniques to extract the reset voltage are described and the corresponding cycle-to-cycle variability is characterized by means of the coefficient of variance. The charge-flux domain was employed to develop one of the extraction techniques, the calculation of the integrals of current and voltage to obtain the charge and flux allows to minimize the effects of electric noise and the inherent stochasticity of resistive switching on the measurement data. A model to reproduce charge versus flux curves has been successfully employed. The device variability is also described by means of the time series analysis to assess the memory effect along a resistive switching series. Finally, we analyzed I-V curves under ramped voltage stress utilizing a simulator based on circuit breakers, the formation and rupture of the percolation paths that constitute the conductive nanofilaments is studied to describe the set and reset processes behind the resistive switching operation.

We develop a numerical scheme for the calculation of tunneling current $I$ and differential conductance $\mathsf{d}I/\mathsf{d}V$ of metal and CO-terminated STM tips on the topological insulators $\mathrm{Bi}_2\mathrm{Se}_3$, $\mathrm{Bi}_2\mathrm{Te}_2\mathrm{Se}$ and $\mathrm{Bi}_2\mathrm{Te}_3$ and find excellent agreement with experiment. The calculation is an application of Chen's derivative rule, whereby the Bloch functions are obtained from Wannier interpolated tight-binding Hamiltonians and maximally localized Wannier functions from first-principle DFT+$GW$ calculations. We observe signatures of the topological boundary modes, their hybridization with bulk bands, Van Hove singularities of the bulk bands and characterize the orbital character of these electronic modes using the high spatial resolution of STM and AFM. Bare DFT calculations are insufficient to explain the experimental data, which are instead accurately reproduced by many-body corrected $GW$ calculations.

This paper is motivated by modeling the cycle-to-cycle variability associated with the resistive switching operation behind memristors. As the data are by nature curves, functional principal component analysis is a suitable candidate to explain the main modes of variability. Taking into account this data-driven motivation, in this paper we propose two new forecasting approaches based on studying the sequential cross-dependence between and within a multivariate functional time series in terms of vector autoregressive modeling of the most explicative functional principal component scores. The main difference between the two methods lies in whether a univariate or multivariate PCA is performed so that we have a different set of principal component scores for each functional time series or the same one for all of them. Finally, the sample performance of the proposed methodologies is illustrated by an application on a bivariate functional time series of reset-set curves.

We introduce exacting cardinals and a strengthening of these, ultraexacting cardinals. These are natural large cardinals defined equivalently as weak forms of rank-Berkeley cardinals, strong forms of J\'onsson cardinals, or in terms of principles of structural reflection. However, they challenge commonly held intuition on strong axioms of infinity. We prove that ultraexacting cardinals are consistent with Zermelo-Fraenkel Set Theory with the Axiom of Choice (ZFC) relative to the existence of an I0 embedding. However, the existence of an ultraexacting cardinal below a measurable cardinal implies the consistency of ZFC with a proper class of I0 embeddings, thus challenging the linear-incremental picture of the large cardinal hierarchy. We show that the existence of an exacting cardinal implies that V is not equal to HOD (G\"odel's universe of Hereditarily Ordinal Definable sets), showing that these cardinals surpass the current hierarchy of large cardinals consistent with ZFC. We prove that the consistency of ZFC with an exacting cardinal above an extendible cardinal refutes Woodin's HOD Conjecture and Ultimate-L Conjecture. Finally, we establish the consistency of ZFC with the existence of an exacting cardinal above an extendible cardinal from the consistency of ZF with certain large cardinals beyond choice., Comment: 39 pages

Let ${\mathcal M}_g$ be the moduli space of compact connected Riemann surfaces of genus $g\geq 2$ and let $\widehat{{\mathcal M}_g}$ be its Deligne-Mumford compactification, which is stratified by the topological type of the stable Riemann surfaces. We consider the equisymmetric loci in $\mathcal M_g$ corresponding to Riemann surfaces whose automorphism group is abelian and determine the topological type of the maximal dimension strata at their boundary. For the particular cases of the hyperelliptic and the cyclic $p$-gonal actions, we describe all the topological strata at the boundary in terms of trees with a fixed number of edges., Comment: 24 pages, 6 figures, 4 tables

Diffeomorphic deformable image registration ensures smooth invertible transformations across inspiratory and expiratory chest CT scans. Yet, in practice, deep learning-based diffeomorphic methods struggle to capture large deformations between inspiratory and expiratory volumes, and therefore lack inverse consistency. Existing methods also fail to account for model uncertainty, which can be useful for improving performance. We propose an uncertainty-aware test-time adaptation framework for inverse consistent diffeomorphic lung registration. Our method uses Monte Carlo (MC) dropout to estimate spatial uncertainty that is used to improve model performance. We train and evaluate our method for inspiratory-to-expiratory CT registration on a large cohort of 675 subjects from the COPDGene study, achieving a higher Dice similarity coefficient (DSC) between the lung boundaries (0.966) compared to both VoxelMorph (0.953) and TransMorph (0.953). Our method demonstrates consistent improvements in the inverse registration direction as well with an overall DSC of 0.966, higher than VoxelMorph (0.958) and TransMorph (0.956). Paired t-tests indicate statistically significant improvements., Comment: 5 pages, 4 figures

We prove level-by-level upper and lower bounds on the strength of determinacy for finite differences of sets in the hyperarithmetical hierarchy in terms of subsystems of finite-and transfinite-order arithmetic, extending the Montalb\'an-Shore theorem to each of the levels of the Borel hierarchy beyond the one they treated. We also prove equivalences between reflection principles for higher-order arithmetic and quantified determinacy axioms, answering two questions of Pacheco and Yokoyama.

Planets form from the same cloud of molecular gas and dust as their host stars. Confirming if planetary bodies acquire the same refractory element composition as their natal disc during formation, and how efficiently volatile elements are incorporated into growing planets, is key to linking the poorly constrained interior composition of rocky exoplanets to the observationally-constrained composition of their host star. Such comparisons also afford insight into the planet formation process. This work compares planetary composition with host-star composition using observations of a white dwarf that has accreted planetary material and its F-type star wide binary companion as a reference for the composition of the natal molecular gas and dust. Spectroscopic analysis reveals abundances of Fe, Mg, Si, Ca, and Ti in both stars. We use the white dwarf measurements to estimate the composition of the exoplanetary material and the F-type companion to constrain the composition of the material the planet formed from. Comparing planetary material to the composition of its natal cloud, our results reveal that the planetary material is depleted in moderate refractories (Mg, Si, Fe) relative to the refractory material (Ca, Ti). Grouping elements based on their condensation temperatures is key to linking stellar and planetary compositions. Fractionation during formation or subsequent planetary evolution leads to the depletion of moderate refractories from the planetary material accreted by the white dwarf. This signature, as seen for bulk Earth, will likely be present in the composition of many exoplanets relative to their host-stars., Comment: 14 pages (without including appendix). 12 Figures. Accepted for publication in A&A

Isospin symmetry is a fundamental concept in nuclear physics. Even though isospin symmetry is partially broken, it holds approximately for most nuclear systems, which makes exceptions very interesting from the nuclear structure perspective. In this framework, it is expected that the spins and parities of the ground states of mirror nuclei should be the same, in particular for the simplest systems where a proton is exchanged with a neutron or vice versa. In this work, we present evidence that this assumption is broken in the mirror pair $^{71}$Br and $^{71}$Kr system. Our conclusions are based on a high-statistics $\beta$ decay study of $^{71}$Kr and on state-of-the-art shell model calculations. In our work, we also found evidence of a new state in $^{70}$Se, populated in the $\beta$-delayed proton emission process which can be interpreted as the long sought coexisting 0$^+$ state., Comment: 8 pages with references, 3 figures. Supplemental material 4 pages (1 table, 3 figures)

The manganese pnictide CaMn$_2$Bi$_2$, with Mn atoms arranged in a puckered honeycomb structure, exhibits narrow-gap antiferromagnetism, and it is currently a promising candidate for the study of complex electronic and magnetic phenomena, such as magnetotransport effects and potential spin spirals under high pressure. In this paper, we perform a detailed research of the magnetic properties of CaMn$_2$Bi$_2$ using density functional theory (DFT) combined with the Hubbard U correction and spin-orbit coupling, which accurately describe the magnetic interactions. Our results obtained for a large number of magnetic configurations are accurately captured by a modified Heisenberg model that includes on-site magnetization terms to describe magnetic energy excitations. We further investigate the role of the spin-orbit coupling, and find that the magnetic anisotropy of CaMn$_2$Bi$_2$ shows an easy plane, with the preferred magnetization direction being exchanged between axes in the plane by applying small strain values. This strain-tunable magnetization, driven by the interplay between spin-orbit interactions and lattice distortions, highlights the potential for controlling magnetic states in Mn-pnictides for future applications in spintronic and magnetoelectric devices.

We report a novel approach based on the non-equilibrium self-consistent generalized Langevin equation (NESCGLE) theory that allows for the first principles prediction of the zero-shear viscosity in glass- and- gel-forming materials. This new modulus of the NESCGLE theory facilitates the theoretical description and interpretation of experimental data concerning out-of-equilibrium rheological properties of viscous liquids during their amorphous solidification. The predictive capability of our approach is illustrated here by means of a quantitative comparison between theoretical and experimental results for the zero shear viscosity in suspensions of oligomer-tethered nanoparticles in a polymeric host, finding an almost perfect correspondence between experiments and theory. This comparison also highlights the crucial relevance of including a kinetic perspective, such as that provided by the NESCGLE theory, in the description of dynamic and viscoelastic properties of amorphous states of matter.

Transformers have become the leading choice in natural language processing over other deep learning architectures. This trend has also permeated the field of time series analysis, especially for long-horizon forecasting, showcasing promising results both in performance and running time. In this paper, we introduce Local Attention Mechanism (LAM), an efficient attention mechanism tailored for time series analysis. This mechanism exploits the continuity properties of time series to reduce the number of attention scores computed. We present an algorithm for implementing LAM in tensor algebra that runs in time and memory O(nlogn), significantly improving upon the O(n^2) time and memory complexity of traditional attention mechanisms. We also note the lack of proper datasets to evaluate long-horizon forecast models. Thus, we propose a novel set of datasets to improve the evaluation of models addressing long-horizon forecasting challenges. Our experimental analysis demonstrates that the vanilla transformer architecture magnified with LAM surpasses state-of-the-art models, including the vanilla attention mechanism. These results confirm the effectiveness of our approach and highlight a range of future challenges in long-sequence time series forecasting.

Planning contact-rich interactions for multi-finger manipulation is challenging due to the high-dimensionality and hybrid nature of dynamics. Recent advances in data-driven methods have shown promise, but are sensitive to the quality of training data. Combining learning with classical methods like trajectory optimization and search adds additional structure to the problem and domain knowledge in the form of constraints, which can lead to outperforming the data on which models are trained. We present Diffusion-Informed Probabilistic Contact Search (DIPS), which uses an A* search to plan a sequence of contact modes informed by a diffusion model. We train the diffusion model on a dataset of demonstrations consisting of contact modes and trajectories generated by a trajectory optimizer given those modes. In addition, we use a particle filter-inspired method to reason about variability in diffusion sampling arising from model error, estimating likelihoods of trajectories using a learned discriminator. We show that our method outperforms ablations that do not reason about variability and can plan contact sequences that outperform those found in training data across multiple tasks. We evaluate on simulated tabletop card sliding and screwdriver turning tasks, as well as the screwdriver task in hardware to show that our combined learning and planning approach transfers to the real world.

We study 2d $SU(N)$ QCD with an adjoint Dirac fermion. Assuming that the IR limit of the massless theory is captured by a WZW coset CFT, we show that this CFT can be decomposed into a sum of distinct CFTs, each representing a superselection sector (universe) of the gauge theory corresponding to different flux tube sectors. The CFTs describing each universe are related by non-invertible topological lines that exhibit a mixed anomaly with the $\mathbb{Z}^{(1)}_N$ 1-form symmetry. These symmetries exist along the entire RG flow thereby implying deconfinement of the massless theory. We begin by outlining the general features of the model for arbitrary $N$ and then provide a detailed analysis for $N=2$ and $N=3$. In these specific cases, we explicitly determine the IR partition function, identify the symmetries, and explore relevant deformations. Based on these findings and in alignment with various previous studies, we propose a phase diagram for the massive $SU(2)$ gauge theory and calculate its confining string tension., Comment: 36 pages + appendices; v2: updated references and minor edits

Training machine learning models based on neural networks requires large datasets, which may contain sensitive information. The models, however, should not expose private information from these datasets. Differentially private SGD [DP-SGD] requires the modification of the standard stochastic gradient descent [SGD] algorithm for training new models. In this short paper, a novel regularization strategy is proposed to achieve the same goal in a more efficient manner.

Memory disaggregation is an emerging data center architecture that improves resource utilization and scalability. Replication is key to ensure the fault tolerance of applications, but replicating shared data in disaggregated memory is hard. We propose SWARM (Swift WAit-free Replication in disaggregated Memory), the first replication scheme for in-disaggregated-memory shared objects to provide (1) single-roundtrip reads and writes in the common case, (2) strong consistency (linearizability), and (3) strong liveness (wait-freedom). SWARM makes two independent contributions. The first is Safe-Guess, a novel wait-free replication protocol with single-roundtrip operations. The second is In-n-Out, a novel technique to provide conditional atomic update and atomic retrieval of large buffers in disaggregated memory in one roundtrip. Using SWARM, we build SWARM-KV, a low-latency, strongly consistent and highly available disaggregated key-value store. We evaluate SWARM-KV and find that it has marginal latency overhead compared to an unreplicated key-value store, and that it offers much lower latency and better availability than FUSEE, a state-of-the-art replicated disaggregated key-value store., Comment: To appear in the proceedings of SOSP '24

We use Ramsey interferometry to study spin dynamics in the strongly interacting regime of spin-orbit-coupled quantum gases in one-dimensional optical lattices. We observe an intrinsic many-body dephasing mechanism immune to spin-echo in two-component Mott insulators. We ascribe the dephasing to the motion of hole-like defects in an otherwise inert Mott insulator, the spinless nature of the holes explaining the ineffectiveness of spin echo to restore it. We show that a model of spin-orbit-coupled hardcore bosons can explain quantitatively our experimental observations., Comment: Supplementary Material available as ancillary file

Autism Spectrum Disorder (ASD) can affect individuals at varying degrees of intensity, from challenges in overall health, communication, and sensory processing, and this often begins at a young age. Thus, it is critical for medical professionals to be able to accurately diagnose ASD in young children, but doing so is difficult. Deep learning can be responsibly leveraged to improve productivity in addressing this task. The availability of data, however, remains a considerable obstacle. Hence, in this work, we introduce the Video ASD dataset--a dataset that contains video frame convolutional and attention map feature data--to foster further progress in the task of ASD classification. The original videos showcase children reacting to chemo-sensory stimuli, among auditory, touch, and vision This dataset contains the features of the frames spanning 2,467 videos, for a total of approximately 1.4 million frames. Additionally, head pose angles are included to account for head movement noise, as well as full-sentence text labels for the taste and smell videos that describe how the facial expression changes before, immediately after, and long after interaction with the stimuli. In addition to providing features, we also test foundation models on this data to showcase how movement noise affects performance and the need for more data and more complex labels.

Parameterizing finger rolling and finger-object contacts in a differentiable manner is important for formulating dexterous manipulation as a trajectory optimization problem. In contrast to previous methods which often assume simplified geometries of the robot and object or do not explicitly model finger rolling, we propose a method to further extend the capabilities of dexterous manipulation by accounting for non-trivial geometries of both the robot and the object. By integrating the object's Signed Distance Field (SDF) with a sampling method, our method estimates contact and rolling-related variables and includes those in a trajectory optimization framework. This formulation naturally allows for the emergence of finger-rolling behaviors, enabling the robot to locally adjust the contact points. Our method is tested in a peg alignment task and a screwdriver turning task, where it outperforms the baselines in terms of achieving desired object configurations and avoiding dropping the object. We also successfully apply our method to a real-world screwdriver turning task, demonstrating its robustness to the sim2real gap.

In recent years, computational improvements have allowed for more nuanced, data-driven and geographically explicit agent-based simulations. So far, simulations have struggled to adequately represent the attributes that motivate the actions of the agents. In fact, existing population synthesis frameworks generate agent profiles limited to socio-demographic attributes. In this paper, we introduce a novel value-enriched population synthesis framework that integrates a motivational layer with the traditional individual and household socio-demographic layers. Our research highlights the significance of extending the profile of agents in synthetic populations by incorporating data on values, ideologies, opinions and vital priorities, which motivate the agents' behaviour. This motivational layer can help us develop a more nuanced decision-making mechanism for the agents in social simulation settings. Our methodology integrates microdata and macrodata within different Bayesian network structures. This contribution allows to generate synthetic populations with integrated value systems that preserve the inherent socio-demographic distributions of the real population in any specific region.

Higher-order interactions underlie complex phenomena in systems such as biological and artificial neural networks, but their study is challenging due to the lack of tractable standard models. By leveraging the maximum entropy principle in curved statistical manifolds, here we introduce curved neural networks as a class of prototypical models for studying higher-order phenomena. Through exact mean-field descriptions, we show that these curved neural networks implement a self-regulating annealing process that can accelerate memory retrieval, leading to explosive order-disorder phase transitions with multi-stability and hysteresis effects. Moreover, by analytically exploring their memory capacity using the replica trick near ferromagnetic and spin-glass phase boundaries, we demonstrate that these networks enhance memory capacity over the classical associative-memory networks. Overall, the proposed framework provides parsimonious models amenable to analytical study, revealing novel higher-order phenomena in complex network systems.

We analyze a long-lived hyper-massive neutron star merger remnant (post-merger lifetime $>250$ ms) that has been obtained via large eddy simulations with a gradient subgrid-scale model. We find a clear helicoidal magnetic field structure that is governed by the toroidal component of the magnetic field. Although no jet emerges during the simulation time, we observe at late times a significant increase of the poloidal component of the magnetic field at all scales. We also compare with the results of several binary neutron star simulations with moderate resolution of $120$~m, that are evolved up to $50$~ms after the merger, which differ in terms of the initial topology and strength of the magnetic field. We find that the best choice is an isotropic small-scale magnetic field distribution that mimics the turbulent state that generically develops during the merger. This initial configuration reaches a closer agreement with our high-resolution simulation results than the purely dipolar large-scale fields that are commonly employed in these type of simulations. This provides a recipe to perform such simulations avoiding the computationally expensive grids required to faithfully capture the amplification of the magnetic field by Kelvin-Helmholtz instabilities., Comment: 13 pages, 11 figures

Transformer-based models have demonstrated exceptional performance across diverse domains, becoming the state-of-the-art solution for addressing sequential machine learning problems. Even though we have a general understanding of the fundamental components in the transformer architecture, little is known about how they operate or what are their expected dynamics. Recently, there has been an increasing interest in exploring the relationship between attention mechanisms and Hopfield networks, promising to shed light on the statistical physics of transformer networks. However, to date, the dynamical regimes of transformer-like models have not been studied in depth. In this paper, we address this gap by using methods for the study of asymmetric Hopfield networks in nonequilibrium regimes --namely path integral methods over generating functionals, yielding dynamics governed by concurrent mean-field variables. Assuming 1-bit tokens and weights, we derive analytical approximations for the behavior of large self-attention neural networks coupled to a softmax output, which become exact in the large limit size. Our findings reveal nontrivial dynamical phenomena, including nonequilibrium phase transitions associated with chaotic bifurcations, even for very simple configurations with a few encoded features and a very short context window. Finally, we discuss the potential of our analytic approach to improve our understanding of the inner workings of transformer models, potentially reducing computational training costs and enhancing model interpretability.

Data centers increasingly host mutually distrustful users on shared infrastructure. A powerful tool to safeguard such users are digital signatures. Digital signatures have revolutionized Internet-scale applications, but current signatures are too slow for the growing genre of microsecond-scale systems in modern data centers. We propose DSig, the first digital signature system to achieve single-digit microsecond latency to sign, transmit, and verify signatures in data center systems. DSig is based on the observation that, in many data center applications, the signer of a message knows most of the time who will verify its signature. We introduce a new hybrid signature scheme that combines cheap single-use hash-based signatures verified in the foreground with traditional signatures pre-verified in the background. Compared to prior state-of-the-art signatures, DSig reduces signing time from 18.9 to 0.7 us and verification time from 35.6 to 5.1 us, while keeping signature transmission time below 2.5 us. Moreover, DSig achieves 2.5x higher signing throughput and 6.9x higher verification throughput than the state of the art. We use DSig to (a) bring auditability to two key-value stores (HERD and Redis) and a financial trading system (based on Liquibook) for 86% lower added latency than the state of the art, and (b) replace signatures in BFT broadcast and BFT replication, reducing their latency by 73% and 69%, respectively, Comment: To appear in the proceedings of OSDI '24. Authors listed in alphabetical order

Resistive Random Access Memories (RRAMs) are being studied by the industry and academia because it is widely accepted that they are promising candidates for the next generation of high density nonvolatile memories. Taking into account the stochastic nature of mechanisms behind resistive switching, a new technique based on the use of functional data analysis has been developed to accurately model resistive memory device characteristics. Functional principal component analysis (FPCA) based on Karhunen-Loeve expansion is applied to obtain an orthogonal decomposition of the reset process in terms of uncorrelated scalar random variables. Then, the device current has been accurately described making use of just one variable presenting a modeling approach that can be very attractive from the circuit simulation viewpoint. The new method allows a comprehensive description of the stochastic variability of these devices by introducing a probability distribution that allows the simulation of the main parameter that is employed for the model implementation. A rigorous description of the mathematical theory behind the technique is given and its application for a broad set of experimental measurements is explained.

A functional linear discriminant analysis approach to classify a set of kinematic data (human movement curves of individuals performing different physical activities) is performed. Kinematic data, usually collected in linear acceleration or angular rotation format, can be identified with functions in a continuous domain (time, percentage of gait cycle, etc.). Since kinematic curves are measured in the same sample of individuals performing different activities, they are a clear example of functional data with repeated measures. On the other hand, the sample curves are observed with noise. Then, a roughness penalty might be necessary in order to provide a smooth estimation of the discriminant functions, which would make them more interpretable. Moreover, because of the infinite dimension of functional data, a reduction dimension technique should be considered. To solve these problems, we propose a multi-class approach for penalized functional partial least squares (FPLS) regression. Then linear discriminant analysis (LDA) will be performed on the estimated FPLS components. This methodology is motivated by two case studies. The first study considers the linear acceleration recorded every two seconds in 30 subjects, related to three different activities (walking, climbing stairs and down stairs). The second study works with the triaxial angular rotation, for each joint, in 51 children when they completed a cycle walking under three conditions (walking, carrying a backpack and pulling a trolley). A simulation study is also developed for comparing the performance of the proposed functional LDA with respect to the corresponding multivariate and non-penalized approaches.

Functional principal component analysis based on Karhunen Loeve expansion allows to describe the stochastic evolution of the main characteristics associated to multiple systems and devices. Identifying the probability distribution of the principal component scores is fundamental to characterize the whole process. The aim of this work is to consider a family of statistical distributions that could be accurately adjusted to a previous transformation. Then, a new class of distributions, the linear-phase-type, is introduced to model the principal components. This class is studied in detail in order to prove, through the KL expansion, that certain linear transformations of the process at each time point are phase-type distributed. This way, the one-dimensional distributions of the process are in the same linear-phase-type class. Finally, an application to model the reset process associated with resistive memories is developed and explained.

The homogeneity problem for testing if more than two different samples come from the same population is considered for the case of functional data. The methodological results are motivated by the study of homogeneity of electronic devices fabricated by different materials and active layer thicknesses. In the case of normality distribution of the stochastic processes associated with each sample, this problem is known as Functional ANOVA problem and is reduced to test the equality of the mean group functions (FANOVA). The problem is that the current/voltage curves associated with Resistive Random Access Memories (RRAM) are not generated by a Gaussian process so that a different approach is necessary for testing homogeneity. To solve this problem two different parametric and nonparametric approaches based on basis expansion of the sample curves are proposed. The first consists of testing multivariate homogeneity tests on a vector of basis coefficients of the sample curves. The second is based on dimension reduction by using functional principal component analysis of the sample curves (FPCA) and testing multivariate homogeneity on a vector of principal components scores. Different approximation numerical techniques are employed to adapt the experimental data for the statistical study. An extensive simulation study is developed for analyzing the performance of both approaches in the parametric and non-parametric cases. Finally, the proposed methodologies are applied on three samples of experimental reset curves measured in three different RRAM technologies.

In this work, we study the magnetocaloric effect (MCE) in a working substance corresponding to a square lattice of spins with $Q$ possible orientations, known as the ``$Q$-state clock model". When the $Q$-state clock model has $Q\geq 5$ possible configurations, it presents the famous Berezinskii Kosterlitz Thouless (BKT) phase associated with vortices states. We calculate thermodynamic quantities using Monte Carlo simulations for even $Q$ numbers, ranging from $Q=2$ to $Q=8$ spin orientations per site in a lattice. We use lattices of different sizes with $L\times L = 8^{2}, 16^{2}, 32^{2}, 64^{2}, \text{and}\ 128^{2}$ sites, considering free boundary conditions and an external magnetic field varying between $B = 0$ and $B=1$ in natural units of the system. By obtaining the entropy, it is possible to quantify the MCE through an isothermal process in which the external magnetic field on the spin system is varied. In particular, we find the values of $Q$ that maximize the MCE depending on the lattice size and the magnetic phase transitions linked with the process. Given the broader relevance of the $Q$-state clock model in areas such as percolation theory, neural networks, and biological systems, where multi-state interactions are essential, our study provides a robust framework in applied quantum mechanics, statistical mechanics and related fields.

ZrSiS has been identified as an exemplary Dirac nodal-line semimetal, in which the Dirac band crossings extend along a closed loop in momentum space. Recently, the topology of the Fermi surface of ZrSiS was uncovered in great detail by quantum oscillation studies. For a magnetic field along the tetragonal $c$ axis, a rich frequency spectrum was observed stemming from the principal electron and hole pockets, and multiple magnetic breakdown orbits. In this work we use uniaxial strain as a tuning parameter for the Fermi surface and the low energy excitations. We measure the magnetoresistance of a single crystal under tensile (up to 0.34 %) and compressive (up to -0.28 %) strain exerted along the $a$ axis and in magnetic fields up to 30 T. We observe a systematic weakening of the peak structure in the Shubnikov-de Haas frequency spectrum upon changing from compressive to tensile strain. This effect may be explained by a decrease in the effective quantum mobility upon decreasing the $c/a$ ratio, which is corroborated by a concurrent increase in the Dingle temperature., Comment: 18 pages, 11 figures, to be published in Physical Review B

We investigate the loss of spatial coherence of one-dimensional bosonic gases in optical lattices illuminated by a near-resonant excitation laser. Because the atoms recoil in a random direction after each spontaneous emission, the atomic momentum distribution progressively broadens. Equivalently, the spatial correlation function (the Fourier-conjugate quantity of the momentum distribution) progressively narrows down as more photons are scattered. Here we measure the correlation function of the matter field for fixed distances corresponding to nearest-neighbor (n-n) and next-nearest-neighbor (n-n-n) sites of the optical lattice as a function of time, hereafter called n-n and n-n-n correlators. For strongly interacting lattice gases, we find that the n-n correlator $C_1$ decays as a power-law at long times, $C_1\propto 1/t^{\alpha}$, in stark contrast with the exponential decay expected for independent particles. The power-law decay reflects a non-trivial dissipative many-body dynamics, where interactions change drastically the interplay between fluorescence destroying spatial coherence, and coherent tunnelling between neighboring sites restoring spatial coherence at short distances. The observed decay exponent $\alpha \approx 0.54(6) $ is in good agreement with the prediction $\alpha=1/2$ from a dissipative Bose-Hubbard model accounting for the fluorescence-induced decoherence. Furthermore, we find that the n-n correlator $C_1$ controls the n-n-n correlator $C_2$ through the relation $C_2 \approx C_1^2$, also in accordance with the dissipative Bose-Hubbard model., Comment: published in the special issue of Comptes Rendus Physique dedicated to Jean Dalibard's CNRS Gold Medal. Details of calculations, included as an appendix in the published article, are available in the ancillary file

In recent years, the supply and demand of electricity has significantly increased. As a result, the interconnecting grid infrastructure has required (and will continue to require) further expansion, while allowing for rapid resolution of unforeseen failures. Energy grid operators strive for networks that satisfy different levels of security requirements. In the case of N-1 security for medium voltage networks, the goal is to ensure the continued provision of electricity in the event of a single-link failure. However, the process of determining if networks are N-1 secure is known to scale polynomially in the network size. This poses restrictions if we increase our demand of the network. In that case, more computationally hard cases will occur in practice and the computation time also increases significantly. In this work, we explore the potential of quantum computers to provide a more scalable solution. In particular, we consider gate-based quantum computing, quantum annealing, and photonic quantum computing.

The most metal-poor stars provide valuable insights into the early chemical enrichment history of a system, carrying the chemical imprints of the first generations of supernovae. The most metal-poor region of the Sagittarius dwarf galaxy remains inadequately observed and characterised. To date, only $\sim4$ stars with [Fe/H]~$<-2.0$ have been chemically analysed with high-resolution spectroscopy. In this study, we present the most extensive chemical abundance analysis of 12 low-metallicity stars with metallicities down to [Fe/H]~$=-3.26$ and located in the main body of Sagittarius. These targets, selected from the Pristine Inner Galaxy Survey, were observed using the MIKE high-resolution spectrograph at the {\it Magellan-Clay} telescope, which allowed us to measure up to 17 chemical species. The chemical composition of these stars reflects the imprint of a variety of type~II supernovae (SNe~II). A combination of low- to intermediate-mass high-energy SNe and hypernovae ($\sim10-70\msun$) is required to account for the abundance patterns of the lighter elements up to the Fe-peak. The trend of the heavy elements suggests the involvement of compact binary merger events and fast-rotating (up to $\sim300\kms$) intermediate-mass to massive metal-poor stars ($\sim25-120\msun$) that are the sources of rapid and slow processes, respectively. Additionally, asymptotic giant branch stars contribute to a wide dispersion of [Ba/Mg] and [Ba/Eu]. The absence of an $\alpha-$knee in our data indicates that type Ia supernovae did not contribute in the very metal-poor region ([Fe/H]~$\leq-2.0$). However, they might have started to pollute the interstellar medium at [Fe/H]~$>-2.0$, given the relatively low [Co/Fe] in this metallicity region., Comment: Accepted for publication in A&A. New plot on [Eu/Mg]. Some refs updated

Next-generation accelerator concepts which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distributions within 6-dimensional position-momentum spaces. However, the characterization of intricate features within 6-dimensional beam distributions using conventional diagnostic techniques necessitates hundreds of measurements, using many hours of valuable beam time. Novel phase space reconstruction techniques are needed to substantially reduce the number of measurements required to reconstruct detailed, high-dimensional beam features in order to resolve complex beam phenomena, and as feedback in precision beam shaping applications. In this study, we present a novel approach to reconstructing detailed 6-dimensional phase space distributions from experimental measurements using generative machine learning and differentiable beam dynamics simulations. We demonstrate that for a collection of synthetic beam distribution test cases that this approach can be used to resolve 6-dimensional phase space distributions using basic beam manipulations and as few as 20 2-dimensional measurements of the beam profile, without the need for prior data collection or model training. We also demonstrate an application of the reconstruction method in an experimental setting at the Argonne Wakefield Accelerator, where it is able to reconstruct the beam distribution and accurately predict previously unseen measurements 75x faster than previous methods.

In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount. As AI applications continue to expand, the collection and handling of sensitive data raise concerns about individual privacy protection. Federated Learning (FL) emerges as a promising solution to address these challenges by enabling decentralized model training on local devices, thus preserving data privacy. This paper introduces FLEX: a FLEXible Federated Learning Framework designed to provide maximum flexibility in FL research experiments. By offering customizable features for data distribution, privacy parameters, and communication strategies, FLEX empowers researchers to innovate and develop novel FL techniques. The framework also includes libraries for specific FL implementations including: (1) anomalies, (2) blockchain, (3) adversarial attacks and defences, (4) natural language processing and (5) decision trees, enhancing its versatility and applicability in various domains. Overall, FLEX represents a significant advancement in FL research, facilitating the development of robust and efficient FL applications., Comment: Submitted to Information Fusion

In the last decades, there has been a deceleration in the rates of poverty reduction, suggesting that traditional redistributive approaches to poverty mitigation could be losing effectiveness, and alternative insights to advance the number one UN Sustainable Development Goal are required. The criminalization of poor people has been denounced by several NGOs, and an increasing number of voices suggest that discrimination against the poor (a phenomenon known as \emph{aporophobia}) could be an impediment to mitigating poverty. In this paper, we present the novel Aporophobia Agent-Based Model (AABM) to provide evidence of the correlation between aporophobia and poverty computationally. We present our use case built with real-world demographic data and poverty-mitigation public policies (either enforced or under parliamentary discussion) for the city of Barcelona. We classify policies as discriminatory or non-discriminatory against the poor, with the support of specialized NGOs, and we observe the results in the AABM in terms of the impact on wealth inequality. The simulation provides evidence of the relationship between aporophobia and the increase of wealth inequality levels, paving the way for a new generation of poverty reduction policies that act on discrimination and tackle poverty as a societal problem (not only a problem of the poor).