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Mobility is a complex phenomenon encompassing diverse transportation modes, infrastructure elements, and human behaviors. Tackling the persistent challenges of congestion, pollution, and accessibility requires a range of modeling approaches to optimize these systems. While AI offers transformative potential, it should not be the sole solution. Parsimonious models remain crucial in generating innovative concepts and tools, and fostering collaborative efforts among researchers, policymakers, and industry stakeholders., Comment: Perspective paper, 9 pages, 2 figures

Current large open vision models could be useful for one and few-shot object recognition. Nevertheless, gradient-based re-training solutions are costly. On the other hand, open-vocabulary object detection models bring closer visual and textual concepts in the same latent space, allowing zero-shot detection via prompting at small computational cost. We propose a lightweight method to turn the latter into a one-shot or few-shot object recognition models without requiring textual descriptions. Our experiments on the TEgO dataset using the YOLO-World model as a base show that performance increases with the model size, the number of examples and the use of image augmentation.

We introduce a novel metric to quantify the fragility of chess positions using the interaction graph of pieces. This fragility score $F$ captures the tension within a position and serves as a strong indicator of tipping points in a game. In well-known games, maximum fragility often aligns with decisive moments marked by brilliant moves. Analyzing a large dataset of games, we find that fragility typically peaks around move $15$, with pawns ($\approx 60\%$) and knights ($\approx 20\%$) frequently involved in high-tension positions. Remarkably, average fragility curves show a universal pattern across a wide range of players, games, and openings, with a subtle deviation observed in games played by the engine Stockfish. Our analysis reveals a gradual buildup of fragility starting around $8$ moves before the peak, followed by a prolonged fragile state lasting up to $15$ moves. This suggests a gradual intensification of positional tension leading to decisive moments in the game. These insights offer a valuable tool for both players and engines to assess critical moments in chess., Comment: 7 pages, 6 figures, 1 table (Revised version)

Recent studies have showcased remarkable capabilities of decoder-only models in many NLP tasks, including translation. Yet, the machine translation field has been largely dominated by encoder-decoder models based on the Transformer architecture. As a consequence, scaling laws of encoder-decoder models for neural machine translation have already been well studied, but decoder-only models have received less attention. This work explores the scaling laws of decoder-only models on the multilingual and multidomain translation task. We trained a collection of six decoder-only models, ranging from 70M to 7B parameters, on a sentence-level, multilingual and multidomain dataset. We conducted a series of experiments showing that the loss of decoder-only models can be estimated using a scaling law similar to the one discovered for large language models, but we also show that this scaling law has difficulties to generalize to too large models or to a different data distribution. We also study different scaling methods and show that scaling the depth and the width of a model lead to similar test loss improvements, but with different impact on the model's efficiency.

We review measures of street network structure proposed in the recent literature, establish their relevance to practice, and identify open challenges facing researchers. These measures' empirical values vary substantially across world regions and development eras, indicating street networks' geometric and topological heterogeneity., Comment: 7 pages, 3 figures and SI (3 pages)

Contemporary literature on the dynamics of economic activities in growing cities mainly focused on a few years or decades time frames. Using a new geo-historical database constructed from historical directories with about 1 million entries, we present a comprehensive analysis of the dynamics of activities in a major city, Paris, over almost a century (1829-1907). Our analysis suggests that activities that accompany city growth can be classified in different categories according to their dynamics and their scaling with population: (i) linear for everyday needs of residents (food stores, clothing retailers, health care practitioners), (ii) sublinear for public services (legal, administrative, educational), (iii) superlinear for the city's specific features (passing fads, specialization, timely needs). The dynamics of these activities is in addition very sensitive to historical perturbations such as large scale public works or political conflicts. These results shed light on the evolution of activities, a crucial component of growing cities., Comment: 11 pages, 3 figures

Current artificial neural networks are trained with parameters encoded as floating point numbers that occupy lots of memory space at inference time. Due to the increase in the size of deep learning models, it is becoming very difficult to consider training and using artificial neural networks on edge devices. Binary neural networks promise to reduce the size of deep neural network models, as well as to increase inference speed while decreasing energy consumption. Thus, they may allow the deployment of more powerful models on edge devices. However, binary neural networks are still proven to be difficult to train using the backpropagation-based gradient descent scheme. This paper extends the work of \cite{crulis2023alternatives}, which proposed adapting to binary neural networks two promising alternatives to backpropagation originally designed for continuous neural networks, and experimented with them on simple image classification datasets. This paper proposes new experiments on the ImageNette dataset, compares three different model architectures for image classification, and adds two additional alternatives to backpropagation.

Due to its inclined orbit and the complex geometry of the magnetic field of Neptune, Triton experiences a highly variable magnetic environment. As precipitation of magnetospheric electrons is thought to have a large impact on the Triton atmosphere, a better understanding of the interaction between its atmosphere and the magnetosphere of Neptune is important. We aim to couple a model of the Triton atmosphere with an electron transport model to compute the impact of a varying electron precipitation on the atmospheric composition. We coupled a recent photochemical model of the Triton atmosphere with the electron transport model TRANSPlanets. The inputs of this code were determined from Voyager 2 observations and previous studies. The main inputs were the electron precipitation flux, the orbital scaling factor, and the magnetic field strength. The electron-impact ionization and electron-impact dissociation rates computed by TRANSPlanets were then used in the photochemical model. We also analyzed the model uncertainties. The coupling of the two models enabled us to find an electron density profile, as well as N$_2$ and N number densities, that are consistent with the Voyager 2 observations. We found that photoionization and electron-impact ionization are of the same order, in contrast to the results of previous photochemical models. However, we emphasize that this result depends on the hypotheses we used to determine the input variables of TRANSPlanets. Our model would greatly benefit from new measurements of the magnetic environment of Triton, as well as of the electron fluxes in the Neptune magnetosphere.

Quantum mechanics is an integral course for physics students. An understanding of quantum concepts is imperative for enrollment in physics graduate programs, participating in research within physics-fields, and employment at companies developing quantum technologies. This study analyzes 188 US research intensive institutions' course catalogs to determine the role and volume of quantum mechanics in their undergraduate physics programs. All of the institutions required one course on quantum concepts, 92% required two courses, and half required three. For institutions with complete class data (n=56), the quantum curriculum was analyzed using course syllabi. The mean number of classroom hours spent on quantum concepts was found to be 63.5 hours with a standard deviation of 28.1 hours. The most commonly taught themes in the quantum curriculum were the Schr{\"o}dinger equation and three-dimensional quantum mechanics. However, the Stern-Gerlach Experiment was only included in 28% of the course outlines. Despite current efforts to promote a spin-first approach, this study found the traditional position-first approaches were still more common as they were used by 73.7% of instructors., Comment: 9 pages, 8 figures

The modern physics course is a crucial gateway for physics majors, introducing concepts beyond the scope of K-12 education. Despite its significance, content varies widely among institutions. This study analyzes 167 modern physics syllabi from 127 US research intensive institutions, employing emergent coding using both human and Natural Language Processing methods from public sources (51.5%) and private correspondence (48.5%). Public course catalogs were consulted to identify pre- and co-requisites, with 37.1% of students having completed calculus II. Foundational topics like Newtonian mechanics (94%), electricity and magnetism (84.4%), and waves or optics (77.2%) were frequently required. Quantum physics (94%), atomic physics (83%), and relativity (70%) were most commonly taught. The study highlights the lack of uniformity in modern physics curricula, emphasizing the importance of a consistent and comprehensive education for physics majors across universities. This insight contributes to the ongoing discourse on optimizing physics education in higher education., Comment: 17 pages, 14 figures

Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology facilitating the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in biological sector. However, existing methods heavily rely on manually labeled data, which requires highly professional skills, thereby hindering the adoption of fully-supervised approaches for cryo-ET images. Some unsupervised domain adaptation (UDA) approaches have been designed to enhance the segmentation network performance using unlabeled data. However, applying these methods directly to cryo-ET images segmentation tasks remains challenging due to two main issues: 1) the source data, usually obtained through simulation, contain a certain level of noise, while the target data, directly collected from raw-data from real-world scenario, have unpredictable noise levels. 2) the source data used for training typically consists of known macromoleculars, while the target domain data are often unknown, causing the model's segmenter to be biased towards these known macromolecules, leading to a domain shift problem. To address these challenges, in this work, we introduce the first voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, specifically for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation. Additionally, we propose a denoised pseudo-labeling strategy based on improved Bilateral Filter to alleviate the domain shift problem. Experimental results on both simulated and real cryo-ET subtomogram datasets demonstrate the superiority of our proposed approach compared to state-of-the-art UDA methods., Comment: 11 pages

We present InvMSAFold, a method for generating a diverse set of protein sequences that fold into a single structure. For a given structure, InvMSAFold defines a probability distribution over the space of sequences, capturing the amino acid covariances observed in Multiple Sequence Alignments (MSA) of homologous proteins. This allows for the generation of highly diverse protein sequences while preserving structural and functional integrity. We show that the higher diversity of sampled sequences translates into higher diversity in biochemical properties, pointing to exciting prospects for the applicability of our method in fields like protein design by providing diverse starting points.

Weak gravitational lensing, resulting from the bending of light due to the presence of matter along the line of sight, is a potent tool for exploring large-scale structures, particularly in quantifying non-Gaussianities. It stands as a pivotal objective for upcoming surveys. In the realm of current and forthcoming full-sky weak-lensing surveys, the convergence maps, representing a line-of-sight integration of the matter density field up to the source redshift, facilitate field-level inference, providing an advantageous avenue for cosmological exploration. Traditional two-point statistics fall short of capturing non-Gaussianities, necessitating the use of higher-order statistics to extract this crucial information. Among the various higher-order statistics available, the wavelet $\ell_1$-norm has proven its efficiency in inferring cosmology (Ajani et al.2021). However, the lack of a robust theoretical framework mandates reliance on simulations, demanding substantial resources and time. Our novel approach introduces a theoretical prediction of the wavelet $\ell_1$-norm for weak lensing convergence maps, grounded in the principles of Large-Deviation theory. We present, for the first time, a theoretical prediction of the wavelet $\ell_1$-norm for convergence maps, derived from the theoretical prediction of their one-point probability distribution. Additionally, we explore the cosmological dependence of this prediction and validate the results on simulations. A comparison of our predicted wavelet $\ell_1$-norm with simulations demonstrates a high level of accuracy in the weakly non-linear regime. Moreover, we show its ability to capture cosmological dependence, paving the way for a more robust and efficient parameter inference process., Comment: 12 pages, 7 figures, submitted to Astronomy & Astrophysics

We describe laboratory experiments in a 2D wave tank that aim at building up and monitor 2D shallow water soliton gas. The water surface elevation is obtained over a large ($\sim 100\,\text{m}^2$) domain, with centimetre-resolution, by stereoscopic vision using two cameras. Floating particles are seeded to get surface texture and determine the wave field by image correlation. With this set-up, soliton propagation and multiple interactions can be measured with a previously unreachable level of detail. The propagation of an oblique soliton is analysed, the amplitude decay and local incidence are compared to analytical predictions. We further present two cases of 2D soliton gas, emerging from multiple line solitons with random incidence ($|\theta|<30^\circ$) and from irregular random waves forced with a {\sc jonswap} spectrum ($|\theta|<45^\circ$). To our knowledge, those are the first observations of random 2D soliton gas for gravity waves. In both cases Mach reflections and Mach expansions result in solitons that mainly propagate in directions perpendicular to the wave-makers., Comment: Accepted for publication in Experiments in Fluids

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

In this work, we demonstrate the constraining power of the tomographic weak lensing convergence PDF for StageIV-like source galaxy redshift bins and shape noise. We focus on scales of $10$ to $20$ arcmin in the mildly nonlinear regime, where the convergence PDF and its changes with cosmological parameters can be predicted theoretically. We model the impact of reconstructing the convergence from the shear field using the well-known Kaiser-Squires formalism. We cross-validate the predicted and the measured convergence PDF derived from convergence maps reconstructed using simulated shear catalogues. Employing a Fisher forecast, we determine the constraining power for $(\Omega_{m},S_{8},w_{0})$. We find that adding a 5-bin tomography improves the $\kappa-$PDF constraints by a factor of $\{3.8,1.3,1.6\}$ for $(\Omega_{m}, S_{8},w_{0})$ respectively. Additionally, we perform a joint analysis with the shear two-point correlation functions, finding an enhancement of around a factor of $1.5$ on all parameters with respect to the two-point statistics alone. These improved constraints come from disentangling $\Omega_{\rm m}$ from $w_0$ by extracting non-Gaussian information, in particular, including the PDF skewness at different redshift bins. We also study the effect of varying the number of parameters to forecast, in particular we add $h$, finding that the convergence PDF maintains its constraining power while the precision from two-point correlations degrades by a factor of $\{1.7,1.4,1.8\}$ for $\{\Omega_{\rm m},S_8,w_0\}$, respectively., Comment: 23 pages, 21 figures

The breaking of surface gravity waves is a key process contributing to air-sea fluxes and turbulent ocean mixing. The highly nonlinear nature of wave breaking, combined with the challenges of observing this process in a laboratory or field setting, leaves our understanding of the energetic processes underpinning wave breaking incomplete. Progress towards refining this understanding was made in a recent study (D. G. Boettger et. al., An energetic signature for breaking inception in surface gravity waves, Journal of Fluid Mechanics 959, A33 (2023)), which identified an energetic signature in the wave kinetic energy evolution that preceded breaking onset and correlated with the strength of the breaking event. In this study, we examine the influence of wind forcing on this energetic signature. We develop a numerical wave tank that simulates wind flowing over mechanically generated waves and construct an ensemble of cases with varying wave steepness and wind forcing speed. The wind is shown to modulate the wave geometry and elevate kinetic energy at crest tip by up to 35 %. Despite these influences, the energetic inception signature was found to robustly indicate breaking inception in all cases examined, with a threshold value in the kinetic energy growth rate at this instant separating breaking and non-breaking waves. Under wind forcing, the timing of the energetic inception point occurred slightly earlier that unforced breaking waves, giving advance warning of breaking 0.3 wave periods prior to breaking onset., Comment: Accepted for publication

Network analysis has become a well-recognized methodology in physics education research (PER), with study topics including student performance and persistence, faculty change, and the structure of conceptual networks. The social network analysis side of this work has focused on quantitative analysis of whole-network cases, such as the structure of networks in single classrooms. Egocentric or personal network approaches are largely unexplored, and qualitative methods are underdeveloped. In this paper, we outline theoretical and practical differences between two major network paradigms--whole-network and egocentric--and introduce theoretical frameworks and methodological considerations for egocentric studies. We also describe qualitative and mixed-methods approaches that are currently missing from the PER literature. We identify areas where these additional network methods may be of particular interest to physics education researchers, and end by discussing example cases and implications for new PER studies., Comment: 25 pages, 3 figures, 1 table, submitted to Phys. Rev. Physics Education Research

This paper leverages the use of \emph{Gram iteration} an efficient, deterministic, and differentiable method for computing spectral norm with an upper bound guarantee. Designed for circular convolutional layers, we generalize the use of the Gram iteration to zero padding convolutional layers and prove its quadratic convergence. We also provide theorems for bridging the gap between circular and zero padding convolution's spectral norm. We design a \emph{spectral rescaling} that can be used as a competitive $1$-Lipschitz layer that enhances network robustness. Demonstrated through experiments, our method outperforms state-of-the-art techniques in precision, computational cost, and scalability. The code of experiments is available at https://github.com/blaisedelattre/lip4conv.

We propose a simple model for a hypergraph generalization of the stochastic block model, using the clustering connection probability $P_{ij}$ between communities $i$ and $j$, and integrating explicitly the hyperedge formation process. Indeed, hyperedges are groups of nodes and we can expect that different real-world networks correspond to different formation processes of these groups and to different levels of homophily between nodes. We describe a simple model where we can explicitly introduce the hyperedge formation process and study its impact on the composition of hyperedges. We focus on the standard case where $P_{ij}=p\delta_{ij}+q(1-\delta_{ij})$, and when $0\leq q\leq p$, we show that the degree and hyperedge size distributions can be approximated by binomials with effective parameters that depend on the number of communities and on $q/p$. Also, the composition of hyperedges goes for $q=0$ from `pure' hyperedges (comprising nodes belonging to the same community) to `mixed' hyperedges that comprise nodes from different communities for $q=p$. We tested various formation processes and our results suggest that when they depend on the composition of the hyperedge, they tend to favor the dominant community and lead to hyperedges with a smaller diversity. In contrast, for formation processes that are independent from the hyperedge structure, we obtain hyperedges comprising a larger diversity of communities. The advantages of the model proposed here are its simplicity and flexibility that make it a good candidate for testing community-related problems, from their detection to their impact on various dynamics., Comment: 10 pages, 10 figures

For any symmetric monoidal category $\mathcal{D}$, Lauda and Pfeiffer showed the equivalence between the $\mathcal{D}$-valued open-closed 2-dimensional TQFTs and the so-called knowledgeable Frobenius algebras (KFAs) in $\mathcal{D}$. Each KFA in $\mathcal{D}=\mathbf{Vec}_{\mathbb{K}}$ provides a sequence of scalars indexed by the set $\mathbb{N}^2$ of diffeomorphism classes of connected endocobordisms of the empty set, given by evaluation by the associated TQFT on each such cobordism class. From an arbitrary sequence $\chi=(\chi_{g,w})_{g,w\in\mathbb{N}}$, we build a symmetric monoidal category $\mathcal{C}_{\chi}$ -- with unit object $\textbf{1}$ satisfying $\text{End}_{\mathcal{C}_{\chi}}(\textbf{1})\cong \mathbb{K}$ -- generated by a KFA object affording this sequence. We then determine which sequences $\chi$ produce semisimple abelian categories $\mathcal{C}_{\chi}$ with finite-dimensional hom-spaces. These form a family of categories interpolating the categories of representations of automorphism groups of certain KFAs in $\mathbf{Vec}_{\mathbb{K}}$., Comment: 38 pages

Two-dimensional electron gases(2DEGs)based on KTaO3 are emerging as a promising platform for spin-orbitronics due to their high Rashba spin-orbit coupling (SOC) and gate-voltage tunability. The recent discovery of a superconducting state in KTaO3 2DEGs now expands their potential towards topological superconductivity. Although the band structure of KTaO3 surfaces of various crystallographic orientations has already been mapped using angle-resolved photoemission spectroscopy(ARPES), this is not the case for superconducting KTaO3 2DEGs. Here, we reveal the electronic structure of superconducting 2DEGs based on KTaO3 (111) single crystals through ARPES measurements. We fit the data with a tight-binding model and compute the associated spin textures to bring insight into the SOC-driven physics of this fascinating system., Comment: 9 pages, 4 figures

In the popular imagination, science and technology are often seen as fields of knowledge production critical to social progress and a cooperative future. This optimistic portrayal of technological advancement also features prominently in internal discourses amongst scientists, industry leaders, and STEM students alike. Yet, an overwhelming body of research, investigation, and first-person accounts highlight the varying ways modern science, technology, and engineering industries contribute to the degradation of our changing environments and exploit and harm global low-income and marginalized populations. By and large, siloed higher-education STEM curricula provide inadequate opportunities for undergraduate and graduate students to critically analyze the historical and epistemological foundations of scientific knowledge production and even fewer tools to engage with and respond to modern community-based cases. Here, we describe the development of a humanities- and social sciences-informed curriculum designed to address the theory, content, and skill-based needs of traditional STEM students considering technoscientific careers. In essence, this course is designed to foster behavior change, de-center dominant ways of knowing in the sciences, and bolster self-reflection and critical-thinking skills to equip the developing STEM workforce with a more nuanced and accurate understanding of the social, political, and economic role of science and technology. This curriculum has the potential to empower STEM-educated professionals to contribute to a more promising, inclusive future. Our framework foregrounds key insights from science and technology studies, Black and Native feminisms, queer theory, and disability studies, alongside real-world case studies using critical pedagogies., Comment: 25 pages, 1 figure, 4 tables

Despite its importance for practical applications, not much is known about the optimal shape of a network that connects in an efficient way a set of points. This problem can be formulated in terms of a multiplex network with a fast layer embedded in a slow one. To connect a pair of points, one can then use either the fast or slow layer, or both, with a switching cost when going from one layer to the other. We consider here distributions of points in spaces of arbitrary dimension d and search for the fast-layer network of given size that minimizes the average time to reach a central node. We discuss the d = 1 case analytically and the d > 1 case numerically, and show the existence of transitions when we vary the network size, the switching cost and/or the relative speed of the two layers. Surprisingly, there is a transition characterized by a symmetry breaking indicating that it is sometimes better to avoid serving a whole area in order to save on switching costs, at the expense of using more the slow layer. Our findings underscore the importance of considering switching costs while studying optimal network structures, as small variations of the cost can lead to strikingly dissimilar results. Finally, we discuss real-world subways and their efficiency for the cities of Atlanta, Boston, and Toronto. We find that real subways are farther away from the optimal shapes as traffic congestion increases., Comment: 11 pages, 5 figures + Supplementary Material

Y-kapellasite [Y3Cu9(OH)19Cl8] is a frustrated antiferromagnetic insulator which remains paramagnetic down to a remarkably low N\'eel temperature of about 2 K. Having studied this material in the paramagnetic regime, in which phonons are the only possible heat carriers, we report the observation of a planar parallel thermal Hall effect coming unambiguously from phonons. This is an advantage over the Kitaev quantum spin liquid candidates {\alpha}-RuCl3 and Na2Co2TeO6 where in principle other heat carriers can be involved [1-4]. As it happens, Y-kapellasite undergoes a structural transition attributed to the positional freezing of a hydrogen atom below about 33 K. Above this transition, the global crystal symmetry forbids the existence of a planar parallel signal - the same situation as in Na2Co2TeO6 and cuprates [3-5]. This points to the notion of a local symmetry breaking at the root of the phonon Hall effect. In this context, the advantage of Y-kapellasite over Na2Co2TeO6 (with high levels of Na disorder and stacking faults) and cuprates (with high levels of disorder coming from dopants and oxygen vacancies) is its clean structure, where the only degree of freedom available for local symmetry breaking is this hydrogen atom randomly distributed over six equivalent positions above 33 K. This provides a specific and concrete case for the general idea of local symmetry breaking leading to the phonon Hall effect in a wide range of insulators., Comment: 16 pages, 4 figures, 3 tables

The macroscopic fundamental diagram (MFD) is a large scale description of the traffic in a urban area and relates the average car flow to the average car density. This MFD has been observed empirically in several cities but how its properties are related to the structure of the road network has remained unclear so far. The MFD displays in general a maximum flow $q^*$ for an optimal car density $k^*$ which are crucial quantities for practical applications. Here, using numerical modeling and dimensional arguments, we propose scaling laws for these quantities $q^*$ and $k^*$ in terms of the road density, the intersection density, the average car size and the maximum velocity. This framework is able to explain the scaling observed empirically for several cities in the world, such as the scaling of $k^*$ with the road density, the relation between $q^*$ and $k^*$ and the impact of buses on the overall capacity $q^*$. This work opens the way to a better understanding of the traffic on a road network at a large urban scale., Comment: 9 pages, 12 figures, 1 table

The classification of the cosmic web into different environments is both a tool to study in more detail the formation of halos and galaxies via the link between their properties and the large-scale environment and as a class of objects whose statistics contain cosmological information. In this paper, we present an analytical framework to compute the probability of the different environments in the cosmic web based on the T-web formalism that classifies structures in four different classes (voids, walls, filaments, knots) by studying the eigenvalues of the tidal tensor (Hessian of the gravitational potential). This method relies on studying the eigenvalues of the tidal tensor with respect to a given threshold and thus requires the knowledge of the JPDF of those eigenvalues. We perform a change of variables in terms of minimally correlated rotational invariants and we study their distribution in the linear regime of structure formation, and in the quasi-linear regime with the help of a Gram-Charlier expansion and tree-order Eulerian perturbation theory. This expansion allows us to predict the probability of the different environments in the density field at a given smoothing scale as a function of the chosen threshold and redshift. We check the validity of our predictions by comparing those predictions to measurements made in the N-body Quijote simulations. We notably find that scaling the threshold value with the non-linear amplitude of fluctuations allows us to capture almost entirely the redshift evolution of the probability of the environments, even if we assume that the density field is Gaussian (corresponding to the linear regime of structure formation). We also show that adding mild non-Gaussian corrections in the form of third-order cumulants of the field provides even more precise predictions for cosmic web abundances up to scales as small as ~5 Mpc/h and redshifts down to z~0., Comment: 16 pages, 5 figures

Strontium titanate (SrTiO$_3$) two-dimensional electron gases (2DEGs) have broken spatial inversion symmetry and possess a finite Rashba spin-orbit coupling. This enables the interconversion of charge and spin currents through the direct and inverse Edelstein effects, with record efficiencies at low temperature, but more modest effects at room temperature. Here, we show that making these 2DEGs ferromagnetic enhances the conversion efficiency by nearly one order of magnitude. Starting from the experimental band structure of non-magnetic SrTiO$_3$ 2DEGs, we mimic magnetic exchange coupling by introducing an out-of-plane Zeeman term in a tight-binding model. We then calculate the band structure and spin textures for increasing internal magnetic fields and compute the Edelstein effect using a semiclassical Boltzmann approach. We find that the conversion efficiency first increases strongly with increasing magnetic field, then shows a maximum and finally decreases. This field dependence is caused by the competition of the exchange coupling with the effective Rashba interaction. While enhancing the splitting of band pairs amplifies the Edelstein effect, weakening the in-plane Rashba-type spin texture reduces it.

The linear magnetoelectric effect (ME) is the phenomenon by which an electric field produces a magnetization. Its observation requires both time-reversal and space-inversion symmetries to be broken, as in multiferroics. While the ME effect has only been studied in insulating materials, it can actually exist in non-centrosymmetric conductors such as two-dimensional electron gases (2DEGs) with Rashba spin-orbit coupling. It is then coined the Edelstein effect (EE), by which a bias voltage -- generating a charge current -- produces a transverse spin density, i.e. a magnetization. Interestingly, 2D systems are sensitive to voltage gating, which provides an extra handle to control the EE. Here, we show that the sign of the EE in a SrTiO$_3$ 2DEG can be controlled by a gate voltage. We propose various logic devices harnessing the dual control of the spin density by current and gate voltages and discuss the potential of our findings for gate-tunable non-reciprocal electronics.

Kitaev materials are a promising platform for the realization of quantum spin liquid states. The thermal Hall effect has emerged as a potential probe of exotic excitations within such states. In the Kitaev candidate material $\alpha$-RuCl$_{3}$, however, the thermal Hall conductivity $\kappa_{xy}$ has been attributed not only to exotic Majorana fermions or chiral magnons, but also to phonons. It has been shown theoretically that the former two types of heat carriers can generate a "planar" thermal Hall effect, whereby the magnetic field is parallel to the heat current, as observed experimentally, but it is unknown whether phonons also could. Here we show that a planar thermal Hall effect is present in another Kitaev candidate material, Na$_{2}$Co$_{2}$TeO$_{6}$. On the basis of a striking similarity between the temperature and field dependence of $\kappa_{xy}$ and that of the phonon-dominated thermal conductivity $\kappa_{xx}$, we argue that the planar thermal Hall effect in Na$_{2}$Co$_{2}$TeO$_{6}$ is generated by phonons. The phonon contributed planar $\kappa_{xy}$ also shows a strong sample dependence, which indicates an extrinsic origin of the mechanism. By conducting a complete study with different in-plane configurations of heat current $J$ and magnetic field $H$, $i.e.$ $H$ $\|$ $J$ and $H$ $\bot$ $J$, we observe a large difference in $\kappa_{xy}$ between these two configurations, which reveals that the direction of the heat current $J$ may play an important role in determining the planar thermal Hall effect. Our observation calls for a re-evaluation of the planar thermal Hall effect observed in $\alpha$-RuCl$_{3}$.

Real-life applications of deep neural networks are hindered by their unsteady predictions when faced with noisy inputs and adversarial attacks. The certified radius in this context is a crucial indicator of the robustness of models. However how to design an efficient classifier with an associated certified radius? Randomized smoothing provides a promising framework by relying on noise injection into the inputs to obtain a smoothed and robust classifier. In this paper, we first show that the variance introduced by the Monte-Carlo sampling in the randomized smoothing procedure estimate closely interacts with two other important properties of the classifier, \textit{i.e.} its Lipschitz constant and margin. More precisely, our work emphasizes the dual impact of the Lipschitz constant of the base classifier, on both the smoothed classifier and the empirical variance. To increase the certified robust radius, we introduce a different way to convert logits to probability vectors for the base classifier to leverage the variance-margin trade-off. We leverage the use of Bernstein's concentration inequality along with enhanced Lipschitz bounds for randomized smoothing. Experimental results show a significant improvement in certified accuracy compared to current state-of-the-art methods. Our novel certification procedure allows us to use pre-trained models with randomized smoothing, effectively improving the current certification radius in a zero-shot manner.

Quantum emitters in two-dimensional layered hexagonal boron nitride are quickly emerging as a highly promising platform for next-generation quantum technologies. However, precise identification and control of defects are key parameters to achieve the next step in their development. We conducted a comprehensive study by analyzing over 10,000 photoluminescence emission lines, revealing 11 distinct defect families within the 1.6 to 2.2 eV energy range. This challenges hypotheses of a random energy distribution. We also reported averaged defect parameters, including emission linewidths, spatial density, phonon side bands, and the Debye-Waller factors. These findings provide valuable insights to decipher the microscopic origin of emitters in hBN hosts. We also explored the influence of hBN host morphology on defect family formation, demonstrating its crucial impact. By tuning flake size and arrangement we achieve selective control of defect types while maintaining high spatial density. This offers a scalable approach to defect emission control, diverging from costly engineering methods. It highlights the importance of investigating flake morphological control to gain deeper insights into the origins of defects and to expand the spectral tailoring capabilities of defects in hBN.

The last few years have seen the development of a promising theoretical framework for statistics of the cosmic large-scale structure -- the theory of large deviations (LDT) for modelling weak-lensing one-point statistics in the mildly non-linear regime. The goal of this series of papers is to make the leap and lay out the steps to perform an actual data analysis with this theoretical tool. Building upon the LDT framework, in this work (Paper I) we demonstrate how to accurately model the Probability Distribution Function (PDF) of a reconstructed Kaiser-Squires convergence field under a realistic mask, that of the third data release of the Dark Energy Survey (DES). We also present how weak lensing systematics and higher-order lensing corrections due to intrinsic alignments, shear biases, photo-$z$ errors and baryonic feedback can be incorporated in the modelling of the reconstructed convergence PDF. In an upcoming work (Paper II) we will then demonstrate the robustness of our modelling through simulated likelihood analyses, the final step required before applying our method to actual data., Comment: 39 pages, 9 figures and 2 tables. Accepted for publication in JCAP, comments welcome

Stochastic equations constitute a major ingredient in many branches of science, from physics to biology and engineering. Not surprisingly, they appear in many quantitative studies of complex systems. In particular, this type of equation is useful for understanding the dynamics of urban population. Empirically, the population of cities follows a seemingly universal law - called Zipf's law - which was discovered about a century ago and states that when sorted in decreasing order, the population of a city varies as the inverse of its rank. Recent data however showed that this law is only approximate and in some cases not even verified. In addition, the ranks of cities follow a turbulent dynamics: some cities rise while other fall and disappear. Both these aspects - Zipf's law (and deviations around it), and the turbulent dynamics of ranks - need to be explained by the same theoretical framework and it is natural to look for the equation that governs the evolution of urban populations. We will review here the main theoretical attempts based on stochastic equations to describe these empirical facts. We start with the simple Gibrat model that introduces random growth rates, and we will then discuss the Gabaix model that adds friction for allowing the existence of a stationary distribution. Concerning the dynamics of ranks, we will discuss a phenomenological stochastic equation that describes rank variations in many systems - including cities - and displays a noise-induced transition. We then illustrate the importance of exchanges between the constituents of the system with the diffusion with noise equation. We will explicit this in the case of cities where a stochastic equation for populations can be derived from first principles and confirms the crucial importance of inter-urban migrations shocks for explaining the statistics and the dynamics of the population of cities., Comment: Review paper for Reports on Progress in Physics

High dynamic range (HDR) imaging aims to retrieve information from multiple low-dynamic range inputs to generate realistic output. The essence is to leverage the contextual information, including both dynamic and static semantics, for better image generation. Existing methods often focus on the spatial misalignment across input frames caused by the foreground and/or camera motion. However, there is no research on jointly leveraging the dynamic and static context in a simultaneous manner. To delve into this problem, we propose a novel alignment-free network with a Semantics Consistent Transformer (SCTNet) with both spatial and channel attention modules in the network. The spatial attention aims to deal with the intra-image correlation to model the dynamic motion, while the channel attention enables the inter-image intertwining to enhance the semantic consistency across frames. Aside from this, we introduce a novel realistic HDR dataset with more variations in foreground objects, environmental factors, and larger motions. Extensive comparisons on both conventional datasets and ours validate the effectiveness of our method, achieving the best trade-off on the performance and the computational cost., Comment: Accepted to ICCV 2023. Version 2: Corrections are made to the conference proceedings to address issues with the production of our benchmark input. We have now updated Table 3 and Figure 6 to reflect these changes

Since the control of the Lipschitz constant has a great impact on the training stability, generalization, and robustness of neural networks, the estimation of this value is nowadays a real scientific challenge. In this paper we introduce a precise, fast, and differentiable upper bound for the spectral norm of convolutional layers using circulant matrix theory and a new alternative to the Power iteration. Called the Gram iteration, our approach exhibits a superlinear convergence. First, we show through a comprehensive set of experiments that our approach outperforms other state-of-the-art methods in terms of precision, computational cost, and scalability. Then, it proves highly effective for the Lipschitz regularization of convolutional neural networks, with competitive results against concurrent approaches. Code is available at https://github.com/blaisedelattre/lip4conv., Comment: ICML 2023