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In cosmic web analysis, complementary to traditional cosmological probes, the extrema (e.g. peaks and voids) two-point correlation functions (2PCFs) are of particular interest for the study of both astrophysical phenomena and cosmological structure formation. However most previous studies constructed those statistics via N-body simulations without a robust theoretical derivation from first principles. A strong motivation exists for analytically describing the 2PCFs of these local extrema, taking into account the nonlinear gravitational evolution in the late Universe. In this paper, we derive analytical formulae for the power spectra and 2PCFs of 2D critical points, including peaks (maxima), voids (minima) and saddle points, in mildly non-Gaussian weak gravitational lensing fields. We apply a perturbative bias expansion to model the clustering of 2D critical points. We successfully derive the power spectrum of weak lensing critical points up to the next-to-next-to-leading order (NNLO) in gravitational perturbation theory, where trispectrum configurations of the weak lensing field have to be included. We numerically evaluate those power spectra up to the next-to-leading order (NLO), which correspond to the inclusion of bispectrum configurations, and transform them to the corresponding 2PCFs. An exact Monte Carlo (MC) integration is performed assuming a Gaussian distributed density field to validate our theoretical predictions. Overall, we find similar properties in 2D compared to the clustering of 3D critical points previously measured from N-body simulations. Contrary to standard lensing power spectra analysis, we find distinct BAO features in the lensing peak 2PCFs due to the gradient and curvature constraints, and we quantify that non-Gaussianity makes for ~10% of the signal at quasi-linear scales which could be important for current stage-IV surveys., Comment: 26 pages, 11 figures. Comments are welcome

Surfacic networks are structures built upon a two-dimensional manifold. Many systems, including transportation networks and various urban networks, fall into this category. The fluctuations of node elevations imply significant deviations from typical plane networks and require specific tools to understand their impact. Here, we present such tools, including lazy paths that minimize elevation differences, graph arduousness which measures the tiring nature of shortest paths, and the excess effort, which characterizes positive elevation variations along shortest paths. We illustrate these measures using toy models of surfacic networks and empirically examine pedestrian networks in selected cities. Specifically, we examine how changes in elevation affect the spatial distribution of betweenness centrality. We also demonstrate that the excess effort follows a non-trivial power law distribution, with an exponent that is not universal, which illustrates that there is a significant probability of encountering steep slopes along shortest paths, regardless of the elevation difference between the starting point and the destination. These findings highlight the significance of elevation fluctuations in shaping network characteristics. Surfacic networks offer a promising framework for comprehensively analyzing and modeling complex systems that are situated on or constrained to a surface environment., Comment: 11 pages+3 pages Supp. material

Pair trading is a market-neutral quantitative trading strategy that exploits price anomalies between two correlated assets. By taking simultaneous long and short positions, it generates profits based on relative price movements, independent of overall market trends. This study explores the mathematical foundations of pair trading, focusing on identifying cointegrated pairs, constructing trading signals, and optimizing model parameters to maximize returns. The results highlight the strategy's potential for consistent profitability even in volatile market conditions.

The field of textual adversarial defenses has gained considerable attention in recent years due to the increasing vulnerability of natural language processing (NLP) models to adversarial attacks, which exploit subtle perturbations in input text to deceive models. This paper introduces the Defensive Dual Masking (DDM) algorithm, a novel approach designed to enhance model robustness against such attacks. DDM utilizes a unique adversarial training strategy where [MASK] tokens are strategically inserted into training samples to prepare the model to handle adversarial perturbations more effectively. During inference, potentially adversarial tokens are dynamically replaced with [MASK] tokens to neutralize potential threats while preserving the core semantics of the input. The theoretical foundation of our approach is explored, demonstrating how the selective masking mechanism strengthens the model's ability to identify and mitigate adversarial manipulations. Our empirical evaluation across a diverse set of benchmark datasets and attack mechanisms consistently shows that DDM outperforms state-of-the-art defense techniques, improving model accuracy and robustness. Moreover, when applied to Large Language Models (LLMs), DDM also enhances their resilience to adversarial attacks, providing a scalable defense mechanism for large-scale NLP applications., Comment: First version

High-Performance Computing (HPC) is crucial for performing advanced computational tasks, yet their complexity often challenges users, particularly those unfamiliar with HPC-specific commands and workflows. This paper introduces Hypothetical Command Embeddings (HyCE), a novel method that extends Retrieval-Augmented Generation (RAG) by integrating real-time, user-specific HPC data, enhancing accessibility to these systems. HyCE enriches large language models (LLM) with real-time, user-specific HPC information, addressing the limitations of fine-tuned models on such data. We evaluate HyCE using an automated RAG evaluation framework, where the LLM itself creates synthetic questions from the HPC data and serves as a judge, assessing the efficacy of the extended RAG with the evaluation metrics relevant for HPC tasks. Additionally, we tackle essential security concerns, including data privacy and command execution risks, associated with deploying LLMs in HPC environments. This solution provides a scalable and adaptable approach for HPC clusters to leverage LLMs as HPC expert, bridging the gap between users and the complex systems of HPC., Comment: preprint

The discovery of in-plane anisotropic excitons in two-dimensional layered semiconductors enables state-of-the-art nanophotonic applications. A fundamental yet unknown parameter of these quasiparticles is the coherence time (T_2 ), which governs the quantum dephasing timescale, over which the coherent superposition of excitons can be maintained and manipulated. Here, we report the direct measurement of T_2 within the sub-picosecond range, along with multiple population decay timescales (T_1 ) at resonance for anisotropic excitons in pristine layered rhenium disulfide (ReS2). We observe a notable weak dependence on layer thickness for T_2 , and a quasi-independence for T_1 . The excitonic coherence in few-layer ReS2 exhibits exceptional robustness against optical density and temperature compared to other two-dimensional semiconductors, enabling quantum features even at room temperature. No photon echo fingerprints were observed in pristine ReS2, highlighting the homogeneous character of the anisotropic excitonic transitions and a particularly low level of disorder in exfoliated flakes. Lastly, our results for mono- to bulk-like ReS2 support a direct gap band structure regardless their layer thickness, addressing the ongoing discussion about its nature.

This paper aims to study the functional renormalization group for quantum $(2+p)$-spin dynamics of a $N$-vector $\textbf{x}\in \mathbb{R}^N$. By fixing the gauge symmetry in the construction of the FRG, that breaks the $O(N)$-symmetry and deriving the corresponding non-trivial Ward identity we can: In the first time coarse grain and focus on this study using a more attractive method such as the effective vertex expansion, and in the second time explore this model beyond the symmetry phase. We show finite scale singularities due to the disorder, interpreted as the signal in the perturbation theory. The unconventional renormalization group approach is based on coarse-graining over the eigenvalues of matrix-like disorder, viewed as an effective kinetic term, with an eigenvalue distribution following a deterministic law in the large $N$ limit. As an illustration, the case where $p=3$ is scrutinized., Comment: 65 pages, 19 figures. A few typing errors have been corrected, as well as spelling mistakes

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. Comparing the Stockfish evaluation with the fragility score, we observe that the maximum fragility ply often serves as a critical turning point, where the moves made afterward can determine the outcome of the game. 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: 8 pages, 7 figures, 1 table (Final published 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.

The stochastic block model is widely used to generate graphs with a community structure, but no simple alternative currently exists for hypergraphs, in which more than two nodes can be connected together through a hyperedge. We discuss here such a hypergraph generalization, based on the clustering connection probability $P_{ij}$ between nodes of communities $i$ and $j$, and that uses an explicit and modulable hyperedge formation process. We focus on the standard case where $P_{ij}=p\delta_{ij}+q(1-\delta_{ij})$ when $0\leq q\leq p$. We propose a simple model that satisfies three criteria: it should be as simple as possible, when $p = q$ the model should be equivalent to the standard hypergraph random model, and it should use an explicit and modulable hyperedge formation process so that the model is intuitive and can easily express different real-world formation processes. We first show that for such a model the degree distribution and hyperedge size distribution can be approximated by binomial distributions with effective parameters that depend on the number of communities and $q/p$. 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 test 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, such as their detection, impact on various dynamics, and visualization., Comment: 12 pages, 12 figures, final version

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