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We study Online Convex Optimization (OCO) with adversarial constraints, where an online algorithm must make repeated decisions to minimize both convex loss functions and cumulative constraint violations. We focus on a setting where the algorithm has access to predictions of the loss and constraint functions. Our results show that we can improve the current best bounds of $ O(\sqrt{T}) $ regret and $ \tilde{O}(\sqrt{T}) $ cumulative constraint violations to $ O(\sqrt{E_T(f)}) $ and $ \tilde{O}(\sqrt{E_T(g)}) $, respectively, where $ E_T(f) $ and $ E_T(g) $ represent the cumulative prediction errors of the loss and constraint functions. In the worst case, where $ E_T(f) = O(T) $ and $ E_T(g) = O(T) $ (assuming bounded loss and constraint functions), our rates match the prior $ O(\sqrt{T}) $ results. However, when the loss and constraint predictions are accurate, our approach yields significantly smaller regret and cumulative constraint violations. Notably, if the constraint function remains constant over time, we achieve $ \tilde{O}(1) $ cumulative constraint violation, aligning with prior results., Comment: 12 pages

Probabilistic Ising machines (PIMs) provide a path to solving many computationally hard problems more efficiently than deterministic algorithms on von Neumann computers. Stochastic magnetic tunnel junctions (S-MTJs), which are engineered to be thermally unstable, show promise as entropy sources in PIMs. However, scaling up S-MTJ-PIMs is challenging, as it requires fine control of a small magnetic energy barrier across large numbers of devices. In addition, non-spintronic components of S-MTJ-PIMs to date have been primarily realized using general-purpose processors or field-programmable gate arrays. Reaching the ultimate performance of spintronic PIMs, however, requires co-designed application-specific integrated circuits (ASICs), combining CMOS with spintronic entropy sources. Here we demonstrate an ASIC in 130 nm foundry CMOS, which implements integer factorization as a representative hard optimization problem, using PIM-based invertible logic gates realized with 1143 probabilistic bits. The ASIC uses stochastic bit sequences read from an adjacent voltage-controlled (V-) MTJ chip. The V-MTJs are designed to be thermally stable in the absence of voltage, and generate random bits on-demand in response to 10 ns pulses using the voltage-controlled magnetic anisotropy effect. We experimentally demonstrate the chip's functionality and provide projections for designs in advanced nodes, illustrating a path to millions of probabilistic bits on a single CMOS+V-MTJ chip.

The ubiquitous noise in quantum system hinders the advancement of quantum information processing and has driven the emergence of different hardware-efficient quantum error correction protocols. Among them, qubits with structured noise, especially with biased noise, are one of the most promising platform to achieve fault-tolerance due to the high error thresholds of quantum error correction codes tailored for them. Nevertheless, their quantum operations are challenging and the demonstration of their performance beyond the fault-tolerant threshold remain incomplete. Here, we leverage Schr\"odinger cat states in a scalable planar superconducting nonlinear oscillator to thoroughly characterize the high-fidelity single-qubit quantum operations with systematic quantum tomography and benchmarking tools, demonstrating the state-of-the-art performance of operations crossing the fault-tolerant threshold of the XZZX surface code. These results thus embody a transformative milestone in the exploration of quantum systems with structured error channels. Notably, our framework is extensible to other types of structured-noise systems, paving the way for systematic characterization and validation of novel quantum platforms with structured noise., Comment: 19 pages, 12 figures

This study demonstrates the effectiveness of AFE-PUND, a revisited Positive Up Negative Down (PUND) protocol for characterizing antiferroelectric (AFE) materials, in analyzing $ZrO_2$ films across different thicknesses, revealing key trends. The proposed AFE-PUND method enables the isolation of switching currents from non-switching contributions, allowing precise extraction of remanent polarization and coercive field from hysteresis loops. The remanent polarization increases with film thickness, reflecting enhanced domain stability, while endurance cycles highlight the wake-up effect and its eventual degradation due to fatigue in thicker films. Similarly, coercive fields decrease with thickness, indicating reduced switching barriers and a clearer transition between tetragonal and orthorhombic phases. The method provides valuable insights into micro-structural influences, such as defect accumulation, grain size, and domain wall pinning, which critically affect device performance. AFE-PUND thus establishes itself as an essential tool for advancing the understanding and optimization of antiferroelectric materials.

Given a rigidly-compactly generated tensor-triangulated category whose Balmer spectrum is finite dimensional and Noetherian, we construct a torsion model for it, which is equivalent to the original tensor-triangulated category. The torsion model is determined in an adelic fashion by objects with singleton supports. This categorifies the Cousin complex from algebra, and the process of reconstructing a spectrum from its monochromatic layers in chromatic stable homotopy theory. This model is inspired by work of the second author in rational equivariant stable homotopy theory, and extends previous work of the authors from the one-dimensional setting., Comment: 48pp

Despite being the most common types of stars in the Galaxy, the physical properties of late M dwarfs are often poorly constrained. A trend of radius inflation compared to evolutionary models has been observed for earlier type M dwarfs in eclipsing binaries, possibly caused by magnetic activity. It is currently unclear whether this trend also extends to later type M dwarfs below the convective boundary. This makes the discovery of lower-mass, fully convective, M dwarfs in eclipsing binaries valuable for testing evolutionary models especially in longer-period binaries where tidal interaction between the primary and secondary is negligible. With this context, we present the discovery of the NGTS-EB-7 AB system, an eclipsing binary containing a late M dwarf secondary and an evolved G-type primary star. The secondary star has a radius of $0.125 \pm 0.006 R_\odot$ , a mass of $0.096 \pm 0.004 M_\odot$ and follows a highly eccentric $(e=0.71436 \pm 0.00085)$ orbit every $193.35875 \pm 0.00034$ days. This makes NGTS-EB-7 AB the third longest-period eclipsing binary system with a secondary smaller than $200 M_J$ with the mass and radius constrained to better than $5 \%$. In addition, NGTS-EB-7 is situated near the centre of the proposed LOPS2 southern field of the upcoming PLATO mission, allowing for detection of the secondary eclipse and measurement of the companion`s temperature. With its long-period and well-constrained physical properties - NGTS-EB-7 B will make a valuable addition to the sample of M dwarfs in eclipsing binaries and help in determining accurate empirical mass/radius relations for later M dwarf stars., Comment: Main body: 14 pages, 6 figures, 5 tables. Appendices: 7 pages, 5 figures, 5 tables

The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. Here, we detail the newest version of ASKCOS, an open source software suite for synthesis planning that makes available several research advances in a freely available, practical tool. Four one-step retrosynthesis models form the basis of both interactive planning and automatic planning modes. Retrosynthetic planning is complemented by other modules for feasibility assessment and pathway evaluation, including reaction condition recommendation, reaction outcome prediction, and auxiliary capabilities such as solubility prediction and quantum mechanical descriptor prediction. ASKCOS has assisted hundreds of medicinal, synthetic, and process chemists in their day-to-day tasks, complementing expert decision making. It is our belief that CASP tools like ASKCOS are an important part of modern chemistry research, and that they offer ever-increasing utility and accessibility.

We present OLMo 2, the next generation of our fully open language models. OLMo 2 includes dense autoregressive models with improved architecture and training recipe, pretraining data mixtures, and instruction tuning recipes. Our modified model architecture and training recipe achieve both better training stability and improved per-token efficiency. Our updated pretraining data mixture introduces a new, specialized data mix called Dolmino Mix 1124, which significantly improves model capabilities across many downstream task benchmarks when introduced via late-stage curriculum training (i.e. specialized data during the annealing phase of pretraining). Finally, we incorporate best practices from T\"ulu 3 to develop OLMo 2-Instruct, focusing on permissive data and extending our final-stage reinforcement learning with verifiable rewards (RLVR). Our OLMo 2 base models sit at the Pareto frontier of performance to compute, often matching or outperforming open-weight only models like Llama 3.1 and Qwen 2.5 while using fewer FLOPs and with fully transparent training data, code, and recipe. Our fully open OLMo 2-Instruct models are competitive with or surpassing open-weight only models of comparable size, including Qwen 2.5, Llama 3.1 and Gemma 2. We release all OLMo 2 artifacts openly -- models at 7B and 13B scales, both pretrained and post-trained, including their full training data, training code and recipes, training logs and thousands of intermediate checkpoints. The final instruction model is available on the Ai2 Playground as a free research demo., Comment: Model demo available at playground.allenai.org

Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of AI models designed to assist in pediatric mental health screening. Our objectives are: (1) to assess the presence of bias by evaluating outcome parity across sex subgroups, (2) to identify bias sources through textual distribution analysis, and (3) to develop a de-biasing method for mental health text data. Methods: We examined classification parity across demographic groups and assessed how gendered language influences model predictions. A data-centric de-biasing method was applied, focusing on neutralizing biased terms while retaining salient clinical information. This methodology was tested on a model for automatic anxiety detection in pediatric patients. Results: Our findings revealed a systematic under-diagnosis of female adolescent patients, with a 4% lower accuracy and a 9% higher False Negative Rate (FNR) compared to male patients, likely due to disparities in information density and linguistic differences in patient notes. Notes for male patients were on average 500 words longer, and linguistic similarity metrics indicated distinct word distributions between genders. Implementing our de-biasing approach reduced diagnostic bias by up to 27%, demonstrating its effectiveness in enhancing equity across demographic groups. Discussion: We developed a data-centric de-biasing framework to address gender-based content disparities within clinical text. By neutralizing biased language and enhancing focus on clinically essential information, our approach demonstrates an effective strategy for mitigating bias in AI healthcare models trained on text.

Vacancy-cluster color centers in lithium fluoride have been studied in detail both theoretically and experimentally for over a century, giving rise to various applications in solid-state lasers, broadband photonic devices, and radiation dosimeters. These color centers are also attractive candidate platforms for applications in quantum information science, due to their spin properties and strong coupling to the crystal lattice, which allows their properties to be easily tuned. Here we present hybrid functional calculations of common vacancy defects in lithium fluoride, including their energetic, spin, and optical properties. We show that for a wide range of hybrid functional parameters tuned to match the experimental band gap, certain defects have little variation in their predicted optical properties. We further demonstrate that the parameters needed to satisfy the generalized Koopman's theorem and correctly position defect levels within the gap, can vary dramatically, even for different charge states of the same defect. Our work establishes the accuracy of the computationally lightweight hybrid-functional approach for predicting the optical and energetic properties of color centers in polar materials., Comment: 10 pages, 5 figures

Human acceptance of social robots is greatly effected by empathy and perceived understanding. This necessitates accurate and flexible responses to various input data from the user. While systems such as this can become increasingly complex as more states or response types are included, new research in the application of large language models towards human-robot interaction has allowed for more streamlined perception and reaction pipelines. LLM-selected actions and emotional expressions can help reinforce the realism of displayed empathy and allow for improved communication between the robot and user. Beyond portraying empathy in spoken or written responses, this shows the possibilities of using LLMs in actuated, real world scenarios. In this work we extend research in LLM-driven nonverbal behavior for social robots by considering more open-ended emotional response selection leveraging new advances in vision-language models, along with emotionally aligned motion and color pattern selections that strengthen conveyance of meaning and empathy., Comment: This work was presented at the WARN, Weighing the Benefits of Autonomous Robot Personalization, workshop at the 33rd IEEE RO-MAN 2024 conference

Strong anomalous diffusion is {often} characterized by a piecewise-linear spectrum of the moments of displacement. The spectrum is characterized by slopes $\xi$ and $\zeta$ for small and large moments, respectively, and by the critical moment $\alpha$ of the crossover. The exponents $\xi$ and $\zeta$ characterize the asymptotic scaling of the bulk and the tails of the probability distribution function of displacements, respectively. Here, we adopt asymptotic theory to match the behaviors at intermediate scales. The resulting constraint explains how distributions with algebraic tails imply strong anomalous diffusion, and it relates $\alpha$ to the corresponding power law. Our theory provides novel relations between exponents characterizing strong anomalous diffusion, and it yields explicit expressions for the leading-order corrections to the asymptotic power-law behavior of the moments of displacement. They provide the time scale that must be surpassed to clearly discriminate the leading-order power law from its sub-leading corrections. This insight allows us to point out sources of systematic errors in their numerical estimates. Rather than separately fitting an exponent for each moment we devise a robust scheme to determine $\xi$, $\zeta$ and $\alpha$. The findings are supported by numerical and analytical results on five different models exhibiting strong anomalous diffusion.

Geological materials are often seen as the antithesis of soft; rocks are hard. However, during the formation of minerals and rocks, all the systems we shall discuss, indeed geological materials in general, pass through a stage where they are soft. This occurs either because they are at a high temperature -- igneous or metamorphic rock -- or because they are at a lower temperature but in the presence of water -- sedimentary rock. For this reason it is useful to introduce soft-matter concepts into the geological domain. There is a universality in the diverse instances of geological patterns that may be appreciated by looking at the common aspect in their formation of having passed through a stage as soft matter.

The EU AI Act (AIA) mandates the implementation of a risk management system (RMS) and a quality management system (QMS) for high-risk AI systems. The ISO/IEC 42001 standard provides a foundation for fulfilling these requirements but does not cover all EU-specific regulatory stipulations. To enhance the implementation of the AIA in Germany, the Federal Office for Information Security (BSI) could introduce the national standard BSI 200-5, which specifies AIA requirements and integrates existing ISMS standards, such as ISO/IEC 27001. This paper examines the interfaces between an information security management system (ISMS) and an AI management system (AIMS), demonstrating that incorporating existing ISMS controls with specific AI extensions presents an effective strategy for complying with Article 15 of the AIA. Four new AI modules are introduced, proposed for inclusion in the BSI IT Grundschutz framework to comprehensively ensure the security of AI systems. Additionally, an approach for adapting BSI's qualification and certification systems is outlined to ensure that expertise in secure AI handling is continuously developed. Finally, the paper discusses how the BSI could bridge international standards and the specific requirements of the AIA through the nationalization of ISO/IEC 42001, creating synergies and bolstering the competitiveness of the German AI landscape.

Recent advancements have underscored the potential of large language model (LLM)-based agents in financial decision-making. Despite this progress, the field currently encounters two main challenges: (1) the lack of a comprehensive LLM agent framework adaptable to a variety of financial tasks, and (2) the absence of standardized benchmarks and consistent datasets for assessing agent performance. To tackle these issues, we introduce \textsc{InvestorBench}, the first benchmark specifically designed for evaluating LLM-based agents in diverse financial decision-making contexts. InvestorBench enhances the versatility of LLM-enabled agents by providing a comprehensive suite of tasks applicable to different financial products, including single equities like stocks, cryptocurrencies and exchange-traded funds (ETFs). Additionally, we assess the reasoning and decision-making capabilities of our agent framework using thirteen different LLMs as backbone models, across various market environments and tasks. Furthermore, we have curated a diverse collection of open-source, multi-modal datasets and developed a comprehensive suite of environments for financial decision-making. This establishes a highly accessible platform for evaluating financial agents' performance across various scenarios.

Persistence effects in HgCdTe infrared detectors cause significant artifacts that can impact the quality of science observations for up to many hours after exposure to bright/saturating sources. This problem will have a substantially greater impact on viable observing modes for infrared cameras on future ELTs due to the leap in sensitivities that is expected. In this paper we present new results from an updated test system that was previously used to prototype ``on-detector guide windows'' to provide fast T/T feedback to AO systems, interleaved with simultaneous (slow) full-frame readouts for science. We now explore the possibility of continuously resetting these small regions of the detector that are illuminated with a compact source as a strategy for mitigating persistence, using two different detectors. While our results generally show promise for this observing strategy, we found for one of our detectors that the combination of fast localized resets with intense illumination can introduce a potentially problematic persistent change in local reset levels., Comment: 12 pages, 9 figures, SPIE (2024) 13103-99

To optimize interventions for improving wellness, it is essential to understand habits, which wearable devices can measure with greater precision. Using high temporal resolution biometric data taken from the Oura Gen3 ring, we examine daily and weekly sleep and activity patterns of a cohort of young adults (N=582) in their first semester of college. A high compliance rate is observed for both daily and nightly wear, with slight dips in wear compliance observed shortly after waking up and also in the evening. Most students have a late-night chronotype with a median midpoint of sleep at 5AM, with males and those with mental health impairment having more delayed sleep periods. Social jetlag, or the difference in sleep times between free days and school days, is prevalent in our sample. While sleep periods generally shift earlier on weekdays and later on weekends, sleep duration on both weekdays and weekends is shorter than during prolonged school breaks, suggesting chronic sleep debt when school is in session. Synchronized spikes in activity consistent with class schedules are also observed, suggesting that walking in between classes is a widespread behavior in our sample that substantially contributes to physical activity. Lower active calorie expenditure is associated with weekends and a delayed but longer sleep period the night before, suggesting that for our cohort, active calorie expenditure is affected less by deviations from natural circadian rhythms and more by the timing associated with activities. Our study shows that regular sleep and activity routines may be inferred from consumer wearable devices if high temporal resolution and long data collection periods are available.

The breakup of a capillary jet into drops is believed to be governed by initial disturbances on the surface of the jet that grow exponentially. The disturbances are often assumed to be due to external sources of noise, to turbulence, or to imperfections of the nozzle. However, even in conditions where external perturbations are minimal, the jet's length cannot grow indefinitely, suggesting that its fragmentation cannot be entirely attributed to such factors. Here we show that the initial disturbances are thermal capillary waves. By extrapolating the observed growth of the instability back in time, we demonstrate that the initiating disturbances must be of the order of an {\aa}ngstr\"om, consistent with fluctuations induced by thermal noise. Further, by performing many experiments with different nozzles, we find no significant variation in breakup length linked to nozzle type, shape or inner roughness. By systematically varying the jet diameter and velocity, and the surface tension, viscosity, and density of the fluid, we validate our thermal disturbance model over four orders of magnitude in jet length; seven orders of magnitude if simulations of nanojets are included., Comment: 6 pages, 5 figures

This paper presents a novel deep learning framework for estimating multivariate joint extremes of metocean variables, based on the Semi-Parametric Angular-Radial (SPAR) model. When considered in polar coordinates, the problem of modelling multivariate extremes is transformed to one of modelling an angular density, and the tail of a univariate radial variable conditioned on angle. In the SPAR approach, the tail of the radial variable is modelled using a generalised Pareto (GP) distribution, providing a natural extension of univariate extreme value theory to the multivariate setting. In this work, we show how the method can be applied in higher dimensions, using a case study for five metocean variables: wind speed, wind direction, wave height, wave period and wave direction. The angular variable is modelled empirically, while the parameters of the GP model are approximated using fully-connected deep neural networks. Our data-driven approach provides great flexibility in the dependence structures that can be represented, together with computationally efficient routines for training the model. Furthermore, the application of the method requires fewer assumptions about the underlying distribution(s) compared to existing approaches, and an asymptotically justified means for extrapolating outside the range of observations. Using various diagnostic plots, we show that the fitted models provide a good description of the joint extremes of the metocean variables considered.

We present a structural analysis of 521 galaxy candidates at 6.5 < z < 12.5, with $SNR > 10\sigma$ in the F444W filter, taken from the EPOCHS v1 sample, consisting of uniformly reduced deep JWST NIRCam data, covering the CEERS, JADES GOOD-S, NGDEEP, SMACS0723, GLASS and PEARLS surveys. We use standard software to fit single S\'ersic models to each galaxy in the rest-frame optical and extract their parametric structural parameters (S\'ersic index, half-light radius and axis-ratio), and \texttt{Morfometryka} to measure their non-parametric concentration and asymmetry parameters. We find a wide range of sizes for these early galaxies, but with a strong galaxy-size mass correlation up to $z \sim 12$ such that galaxy sizes continue to get progressively smaller in the high-redshift regime, following $R_{e} = 2.74 \pm 0.49 \left( 1 + z \right) ^{-0.79 \pm 0.08}$ kpc. Using non-parametric methods we find that galaxy merger fractions, classified through asymmetry parameters, at these redshifts remain consistent with those in literature, maintaining a value of $f_{m} \sim 0.12 \pm 0.07$ showing little dependence with redshift when combined with literature at $z > 4$. We find that galaxies which are smaller in size also appear rounder, with an excess of high axis-ratio objects. Finally, we artificially redshift a subsample of our objects to determine how robust the observational trends we see are, determining that observed trends are due to real evolutionary effects, rather than being a consequence of redshift effects., Comment: 35 pages, 13 figures. Submitted to ApJ on 19/12/2024. Comments to corresponding author welcome at lewi.westcott@manchester.ac.uk

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

This paper serves as a reference and introduction to using the R package dapper. dapper encodes a sampling framework which allows exact Markov chain Monte Carlo simulation of parameters and latent variables in a statistical model given privatized data. The goal of this package is to fill an urgent need by providing applied researchers with a flexible tool to perform valid Bayesian inference on data protected by differential privacy, allowing them to properly account for the noise introduced for privacy protection in their statistical analysis. dapper offers a significant step forward in providing general-purpose statistical inference tools for privatized data.

A remarkable characteristic of quantum computing is the potential for reliable computation despite faulty qubits. This can be achieved through quantum error correction, which is typically implemented by repeatedly applying static syndrome checks, permitting correction of logical information. Recently, the development of time-dynamic approaches to error correction has uncovered new codes and new code implementations. In this work, we experimentally demonstrate three time-dynamic implementations of the surface code, each offering a unique solution to hardware design challenges and introducing flexibility in surface code realization. First, we embed the surface code on a hexagonal lattice, reducing the necessary couplings per qubit from four to three. Second, we walk a surface code, swapping the role of data and measure qubits each round, achieving error correction with built-in removal of accumulated non-computational errors. Finally, we realize the surface code using iSWAP gates instead of the traditional CNOT, extending the set of viable gates for error correction without additional overhead. We measure the error suppression factor when scaling from distance-3 to distance-5 codes of $\Lambda_{35,\text{hex}} = 2.15(2)$, $\Lambda_{35,\text{walk}} = 1.69(6)$, and $\Lambda_{35,\text{iSWAP}} = 1.56(2)$, achieving state-of-the-art error suppression for each. With detailed error budgeting, we explore their performance trade-offs and implications for hardware design. This work demonstrates that dynamic circuit approaches satisfy the demands for fault-tolerance and opens new alternative avenues for scalable hardware design., Comment: 11 pages, 5 figures, Supplementary Information

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. Several FL methods are introduced to tackle communication inefficiencies but do not address how to sample participating clients in each round effectively and in a privacy-preserving manner. In this paper, we propose \textit{FedSTaS}, a client and data-level sampling method inspired by \textit{FedSTS} and \textit{FedSampling}. In each federated learning round, \textit{FedSTaS} stratifies clients based on their compressed gradients, re-allocate the number of clients to sample using an optimal Neyman allocation, and sample local data from each participating clients using a data uniform sampling strategy. Experiments on three datasets show that \textit{FedSTaS} can achieve higher accuracy scores than those of \textit{FedSTS} within a fixed number of training rounds., Comment: 6 pages, 3 figures

We present a detailed investigation into the abundance and morphology of high redshift quenched galaxies at $3 < z < 7$ using James Webb Space Telescope data in the NEP, CEERS and JADES fields. Within these fields, we identify 90 candidate passive galaxies using specific star formation rates modelled with the BAGPIPES SED fitting code, which is more effective at identifying recently quenched systems than the classical UVJ method. With this sample of galaxies, we find number densities broadly consistent with other works and a rapidly evolving passive fraction of high mass galaxies ($\log_{10}{(M_{\star}/M_{\odot})} > $ 9.5) between $3 < z < 5$. We find that the fraction of galaxies with low star formation rates and mass 9.5 $ < \log_{10}{(M_{\star}/M_{\odot})} < $ 10.5 decreases from $\sim$25% at $3 < z < 4$ to $\sim$2% at $5 < z < 7$. Our passive sample of galaxies is shown to exhibit more compact light profiles compared to star-forming counterparts and some exhibit traces of AGN activity through detections in either the X-ray or radio. At the highest redshifts ($z > 6.5$) passive selections start to include examples of 'little red dots' which complicates any conclusions until their nature is better understood., Comment: 24 pages, 5 tables, 11 figures, submitted to MNRAS

We present measurements of the neutral atomic hydrogen (HI) mass function (HIMF) and cosmic HI density ($\Omega_{\rm HI}$) at $0 \leq z \leq 0.088$ from the Looking at the Distant Universe with MeerKAT Array (LADUMA) survey. Using LADUMA Data Release 1 (DR1), we analyze the HIMF via a new "recovery matrix" (RM) method that we benchmark against a more traditional Modified Maximum Likelihood (MML) method. Our analysis, which implements a forward modeling approach, corrects for survey incompleteness and uses extensive synthetic source injections to ensure robust estimates of the HIMF parameters and their associated uncertainties. This new method tracks the recovery of sources in mass bins different from those in which they were injected and incorporates a Poisson likelihood in the forward modeling process, allowing it to correctly handle uncertainties in bins with few or no detections. The application of our analysis to a high-purity subsample of the LADUMA DR1 spectral line catalog in turn mitigates any possible biases that could result from the inconsistent treatment of synthetic and real sources. For the surveyed redshift range, the recovered Schechter function normalization, low-mass slope, and "knee" mass are $\phi_\ast = 3.56_{-1.92}^{+0.97} \times 10^{-3}$ Mpc$^{-3}$ dex$^{-1}$, $\alpha = -1.18_{-0.19}^{+0.08}$, and $\log(M_\ast/M_\odot) = 10.01_{-0.12}^{+0.31}$, respectively, which together imply a comoving cosmic HI density of $\Omega_{\rm HI}=3.09_{-0.47}^{+0.65}\times 10^{-4}$. Our results show consistency between RM and MML methods and with previous low-redshift studies, giving confidence that the cosmic volume probed by LADUMA, even at low redshifts, is not an outlier in terms of its HI content., Comment: 25 pages, 10 figures, Accepted to ApJ

To review the lessons learnt from recent deep geothermal case studies and plan strategically the research, development, regulation, and communication work required for the implementation of an Enhanced Geothermal System (EGS) at Cornell University, a group of engineers and scholars convened a two-day workshop on the Ithaca campus, on October 23-24, 2024. The event was funded by Cornell Atkinson Center for Sustainability. This report is a summary of the content of the presentations and discussions that took place during the workshop. The first section focuses on philosophical, sociological, economic, and regulatory questions posed by EGS deployment as a means to mitigate climate change. The second section tackles the scientific and technological research areas associated with EGS. The third section aims to assess the feasibility of developing EGS for heat direct use at Cornell University, based on results and information available to date. The report concludes with a summary of the most salient technological and scientific breakthroughs, and a plan for future technological and academic engagement in EGS projects at Cornell.

We perform transport measurements on proximitized, ballistic, bilayer graphene Josephson junctions (BGJJs) in the intermediate-to-long junction regime ($L>\xi$). We measure the device's differential resistance as a function of bias current and gate voltage for a range of different temperatures. The extracted critical current $I_{C}$ follows an exponential trend with temperature: $ \exp(-k_{B} T/ \delta E)$. Here $\delta E = \hbar \nu_F /2\pi L $: an expected trend for intermediate-to-long junctions. From $\delta E$, we determine the Fermi velocity of the bilayer graphene, which is found to increase with gate voltage. Simultaneously, we show the carrier density dependence of $\delta E$, which is attributed to the quadratic dispersion of bilayer graphene. This is in contrast to single layer graphene Josephson junctions, where $\delta E$ and the Fermi velocity are independent of the carrier density. The carrier density dependence in BGJJs allows for additional tuning parameters in graphene-based Josephson Junction devices.

The increasing reliance on large language models (LLMs) for diverse applications necessitates a thorough understanding of their robustness to adversarial perturbations and out-of-distribution (OOD) inputs. In this study, we investigate the correlation between adversarial robustness and OOD robustness in LLMs, addressing a critical gap in robustness evaluation. By applying methods originally designed to improve one robustness type across both contexts, we analyze their performance on adversarial and out-of-distribution benchmark datasets. The input of the model consists of text samples, with the output prediction evaluated in terms of accuracy, precision, recall, and F1 scores in various natural language inference tasks. Our findings highlight nuanced interactions between adversarial robustness and OOD robustness, with results indicating limited transferability between the two robustness types. Through targeted ablations, we evaluate how these correlations evolve with different model sizes and architectures, uncovering model-specific trends: smaller models like LLaMA2-7b exhibit neutral correlations, larger models like LLaMA2-13b show negative correlations, and Mixtral demonstrates positive correlations, potentially due to domain-specific alignment. These results underscore the importance of hybrid robustness frameworks that integrate adversarial and OOD strategies tailored to specific models and domains. Further research is needed to evaluate these interactions across larger models and varied architectures, offering a pathway to more reliable and generalizable LLMs.

At sub-Kelvin temperatures, two-level systems (TLS) present in amorphous dielectrics source a permittivity noise, degrading the performance of a wide range of devices using superconductive resonators such as qubits or kinetic inductance detectors. We report here on measurements of TLS noise in hydrogenated amorphous silicon (a-Si:H) films deposited by plasma-enhanced chemical vapor deposition (PECVD) in superconductive lumped-element resonators using parallel-plate capacitors (PPCs). The TLS noise results presented in this article for two recipes of a-Si:H improve on the best achieved in the literature by a factor >5 for a-Si:H and other amorphous dielectrics and are comparable to those observed for resonators deposited on crystalline dielectrics.

Fermionic Hamiltonians play a critical role in quantum chemistry, one of the most promising use cases for near-term quantum computers. However, since encoding nonlocal fermionic statistics using conventional qubits results in significant computational overhead, fermionic quantum hardware, such as fermion atom arrays, were proposed as a more efficient platform. In this context, we here study the many-body entanglement structure of fermionic $N$-particle states by concentrating on $M$-body reduced density matrices (DMs) across various bipartitions in Fock space. The von Neumann entropy of the reduced DM is a basis independent entanglement measure which generalizes the traditional quantum chemistry concept of the one-particle DM entanglement, which characterizes how a single fermion is entangled with the rest. We carefully examine upper bounds on the $M$-body entanglement, which are analogous to the volume law of conventional entanglement measures. To this end we establish a connection between $M$-body reduced DM and the mathematical structure of hypergraphs. Specifically, we show that a special class of hypergraphs, known as $t$-designs, corresponds to maximally entangled fermionic states. Finally, we explore fermionic many-body entanglement in random states. We semianalytically demonstrate that the distribution of reduced DMs associated with random fermionic states corresponds to the trace-fixed Wishart-Laguerre random matrix ensemble. In the limit of large single-particle dimension $D$ and a non-zero filling fraction, random states asymptotically become absolutely maximally entangled., Comment: 21+2 pages, 5 figures

Recent latent-space monitoring techniques have shown promise as defenses against LLM attacks. These defenses act as scanners that seek to detect harmful activations before they lead to undesirable actions. This prompts the question: Can models execute harmful behavior via inconspicuous latent states? Here, we study such obfuscated activations. We show that state-of-the-art latent-space defenses -- including sparse autoencoders, representation probing, and latent OOD detection -- are all vulnerable to obfuscated activations. For example, against probes trained to classify harmfulness, our attacks can often reduce recall from 100% to 0% while retaining a 90% jailbreaking rate. However, obfuscation has limits: we find that on a complex task (writing SQL code), obfuscation reduces model performance. Together, our results demonstrate that neural activations are highly malleable: we can reshape activation patterns in a variety of ways, often while preserving a network's behavior. This poses a fundamental challenge to latent-space defenses., Comment: Project page: https://obfuscated-activations.github.io/

Essential information about the formation and evolution of planetary systems can be found in their architectures -- in particular, in stellar obliquity ($\psi$) -- as they serve as a signature of their dynamical evolution. Here, we present ESPRESSO observations of the Rossiter-Mclaughlin (RM) effect of 8 warm gas giants, revealing that independent of the eccentricities, all of them have relatively aligned orbits. Our 5 warm Jupiters -- WASP-106 b, WASP-130 b, TOI-558 b, TOI-4515 b, and TOI-5027 b -- have sky-projected obliquities $|\lambda|\simeq0-10$ deg while the 2 less massive warm Saturns -- K2-139 b and K2-329 A b -- are slightly misaligned having $|\lambda|\simeq15-25$ deg. Furthermore, for K2-139 b, K2-329 A b, and TOI-4515 b, we also measure true 3D obliquities $\psi\simeq15-30$ deg. We also report a non-detection of the RM effect produced by TOI-2179 b. Through hierarchical Bayesian modeling of the true 3D obliquities of hot and warm Jupiters, we find that around single stars, warm Jupiters are statistically more aligned than hot Jupiters. Independent of eccentricities, 95\% of the warm Jupiters have $\psi\lesssim30$ deg with no misaligned planets, while hot Jupiters show an almost isotropic distribution of misaligned systems. This implies that around single stars, warm Jupiters form in primordially aligned protoplanetary disks and subsequently evolve in a more quiescent way than hot Jupiters. Finally, we find that Saturns may have slightly more misaligned orbits than warm Jupiters, but more obliquity measurements are necessary to be conclusive., Comment: 16 pages, 6 figures. Submitted to AAS journals. Comments are welcomed

In materials with one-dimensional electronic bands, electron-electron interactions can produce intriguing quantum phenomena, including spin-charge separation and charge density waves (CDW). Most of these systems, however, are non-magnetic, motivating a search for anisotropic materials where the coupling of charge and spin may affect emergent quantum states. Here, electron doping the van der Waals magnetic semiconductor CrSBr induces an electronically driven quasi-1D CDW, which survives above room temperature. Lithium intercalation also increases the magnetic ordering temperature to 200 K and changes its interlayer magnetic coupling from antiferromagnetic to ferromagnetic. The spin-polarized nature of the anisotropic bands that give rise to this CDW enforces an intrinsic coupling of charge and spin. The coexistence and interplay of ferromagnetism and charge modulation in this exfoliatable material provides a promising platform for studying tunable quantum phenomena across a range of temperatures and thicknesses., Comment: Main text: 30 pages, 4 figures, 1 table. Supplementary Information: 21 pages, 11 figures, 2 tables

We present TapeAgents, an agent framework built around a granular, structured log tape of the agent session that also plays the role of the session's resumable state. In TapeAgents we leverage tapes to facilitate all stages of the LLM Agent development lifecycle. The agent reasons by processing the tape and the LLM output to produce new thought and action steps and append them to the tape. The environment then reacts to the agent's actions by likewise appending observation steps to the tape. By virtue of this tape-centred design, TapeAgents can provide AI practitioners with holistic end-to-end support. At the development stage, tapes facilitate session persistence, agent auditing, and step-by-step debugging. Post-deployment, one can reuse tapes for evaluation, fine-tuning, and prompt-tuning; crucially, one can adapt tapes from other agents or use revised historical tapes. In this report, we explain the TapeAgents design in detail. We demonstrate possible applications of TapeAgents with several concrete examples of building monolithic agents and multi-agent teams, of optimizing agent prompts and finetuning the agent's LLM. We present tooling prototypes and report a case study where we use TapeAgents to finetune a Llama-3.1-8B form-filling assistant to perform as well as GPT-4o while being orders of magnitude cheaper. Lastly, our comparative analysis shows that TapeAgents's advantages over prior frameworks stem from our novel design of the LLM agent as a resumable, modular state machine with a structured configuration, that generates granular, structured logs and that can transform these logs into training text -- a unique combination of features absent in previous work.

We uncover late-time gravitational-wave tails in fully nonlinear 3+1 dimensional numerical relativity simulations of merging black holes, using the highly accurate SpEC code. We achieve this result by exploiting the strong magnification of late-time tails due to binary eccentricity, recently observed in perturbative evolutions, and showcase here the tail presence in head-on configurations for several mass ratios close to unity. We validate the result through a large battery of numerical tests and detailed comparison with perturbative evolutions, which display striking agreement with full nonlinear ones. Our results offer yet another confirmation of the highly predictive power of black hole perturbation theory in the presence of a source, even when applied to nonlinear solutions. The late-time tail signal is much more prominent than anticipated until recently, and possibly within reach of gravitational-wave detectors measurements, unlocking observational investigations of an additional set of general relativistic predictions on the long-range gravitational dynamics., Comment: 5+7 pages, 2+5 figures, 1+1 tables

We present a world-line effective field theory of compact objects moving relativistically through a viscous fluid. The theory is valid when velocity gradients are small compared to the inverse size of the object. Working within the EFT eliminates the need to solve a boundary value problem by turning all interactions between the fluid and the object into a source term in the action. We use the EFT to derive the relativistic equations of motion for a compact object immersed in a viscous fluid in a curved background., Comment: 22 pages, 1 appendix

Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRAs, aiming to improve diagnostic accuracy and reduce reliance on MRAs. We curated a dataset of 586 shoulder MRIs (335 standard, 251 MRAs) from 558 patients who underwent arthroscopy. Ground truth labels were derived from intraoperative findings, the gold standard for Bankart lesion diagnosis. Separate DL models for MRAs and standard MRIs were trained using the Swin Transformer architecture, pre-trained on a public knee MRI dataset. Predictions from sagittal, axial, and coronal views were ensembled to optimize performance. The models were evaluated on a 20% hold-out test set (117 MRIs: 46 MRAs, 71 standard MRIs). Bankart lesions were identified in 31.9% of MRAs and 8.6% of standard MRIs. The models achieved AUCs of 0.87 (86% accuracy, 83% sensitivity, 86% specificity) and 0.90 (85% accuracy, 82% sensitivity, 86% specificity) on standard MRIs and MRAs, respectively. These results match or surpass radiologist performance on our dataset and reported literature metrics. Notably, our model's performance on non-invasive standard MRIs matched or surpassed the radiologists interpreting MRAs. This study demonstrates the feasibility of using DL to address the diagnostic challenges posed by subtle pathologies like Bankart lesions. Our models demonstrate potential to improve diagnostic confidence, reduce reliance on invasive imaging, and enhance accessibility to care., Comment: Accepted for presentation at SPIE Medical Imaging 2025: Computer-Aided Diagnosis. The manuscript is expected to appear in the conference proceedings

We present successful evolutions of binary black hole mergers using a novel numerical-relativity technique known as Cauchy-characteristic matching (CCM). This approach eliminates systematic errors associated with boundary conditions, effectively extending the computational domain to infinity. As an important application, we use CCM to resolve a late-time power-law tail in the gravitational wave from a head-on collision, and show that the tail is highly suppressed in a quasi-circular binary. Our results for the two extreme cases (orbital eccentricity $=0,1$) support the fact that tails increase with orbital eccentricity. Therefore, CCM paves the way for a detailed understanding of tails in eccentric systems. For the head-on case, we find that the tail behavior is consistent with predictions in the intermediate regime from black hole linear perturbation theory. However, we also raise the possibility that the power-law tail could be generated nonlinearly by quasinormal modes. The nonlinear contribution is expected to decay slower than predicted by Price's law, potentially dominating the signal at late times. If confirmed as nonlinear, this would be an example where nonlinearity prevails over linearity in the late-time regime of black hole dynamics.

We present a joint analysis of TTVs and Doppler data for the transiting exoplanet system TOI-4504. TOI-4504 c is a warm Jupiter-mass planet that exhibits the largest known transit timing variations (TTVs), with a peak-to-node amplitude of $\sim$ 2 days, the largest value ever observed, and a super-period of $\sim$ 930 d. TOI-4504 b and c were identified in public TESS data, while the TTVs observed in TOI-4504 c, together with radial velocity (RV) data collected with FEROS, allowed us to uncover a third, non-transiting planet in this system, TOI-4504 d. We were able to detect transits of TOI-4504 b in the TESS data with a period of 2.4261$\pm 0.0001$ days and derive a radius of 2.69$\pm 0.19$ R$_{\oplus}$. The RV scatter of TOI-4504 was too large to constrain the mass of TOI-4504 b, but the RV signals of TOI-4504 c \& d were sufficiently large to measure their masses. The TTV+RV dynamical model we apply confirms TOI-4504 c as a warm Jupiter planet with an osculating period of 82.54$\pm 0.02$ d, mass of 3.77$\pm 0.18$ M$_{\rm J}$ and a radius of 0.99$\pm 0.05$ R$_{\rm J}$, while the non-transiting planet TOI-4504 d, has an orbital period of 40.56$\pm 0.04$ days and mass of 1.42$_{-0.06}^{+0.07}$ M$_{\rm J}$. We present the discovery of a system with three exoplanets: a hot sub-Neptune and two warm Jupiter planets. The gas giant pair is stable and likely locked in a first-order 2:1 mean-motion resonance (MMR). The TOI-4504 system is an important addition to MMR pairs, whose increasing occurrence supports a smooth migration into a resonant configuration during the protoplanetary disk phase., Comment: 20 pages, 12 figures, accepted in ApJL

In 1977, Lenstra provided a criterion for norm-Euclideanity of number fields and noted that this criterion becomes ineffective for number fields of large enough degrees under the Generalised Riemann Hypothesis (GRH) for the Dedekind zeta-functions. We provide an explicit version of this statement: the criterion becomes ineffective under GRH for all $n \geq 62238$. Moreover, in the unconditional case, we show that the criterion still cannot be used for large enough degrees under an assumption on the lower bounds for the Dedekind zeta functions on the right of $s = 1$. Such lower bounds connect to the bounds for the sphere packings., Comment: 12 pages

Despite large language models (LLMs) being known to exhibit bias against non-mainstream varieties, there are no known labeled datasets for sentiment analysis of English. To address this gap, we introduce BESSTIE, a benchmark for sentiment and sarcasm classification for three varieties of English: Australian (en-AU), Indian (en-IN), and British (en-UK). Using web-based content from two domains, namely, Google Place reviews and Reddit comments, we collect datasets for these language varieties using two methods: location-based and topic-based filtering. Native speakers of the language varieties manually annotate the datasets with sentiment and sarcasm labels. Subsequently, we fine-tune nine large language models (LLMs) (representing a range of encoder/decoder and mono/multilingual models) on these datasets, and evaluate their performance on the two tasks. Our results reveal that the models consistently perform better on inner-circle varieties (i.e., en-AU and en-UK), with significant performance drops for en-IN, particularly in sarcasm detection. We also report challenges in cross-variety generalisation, highlighting the need for language variety-specific datasets such as ours. BESSTIE promises to be a useful evaluative benchmark for future research in equitable LLMs, specifically in terms of language varieties. The BESSTIE datasets, code, and models are currently available on request, while the paper is under review. Please email aditya.joshi@unsw.edu.au., Comment: 10 pages, 7 figures, under review

We report on an educational pilot program for low-cost physics experimentation run in Ecuador, South Africa, and the United States. The program was developed after having needs-based discussions with African educators, researchers, and leaders. It was determined that the need and desire for low-cost, skills-building, and active-learning tools is very high. From this, we developed a 3D-printable, Raspberry Pi-based multispectral camera (15 to 25 spectral channels in the visible and near-IR) for as little as $100. The program allows students to learn 3D modeling, 3D printing, feedback, control, image analysis, Python programming, systems integration and artificial intelligence as well as spectroscopy. After completing their cameras, the students in the program studied plant health, plant stress, post-harvest fruit ripeness, and polarization and spectral analysis of nanostructured insect wings, the latter of which won the ``best-applied research" award at a conference poster session and will be highlighted in this paper. Importantly, these cameras can be an integral part of any developing country's agricultural, recycling, medical, and pharmaceutical infrastructure. Thus, we believe this experiment can play an important role at the intersection of student training and developing countries' capacity building.

Ferromagnetic superconductors are exceptionally rare because the strong ferromagnetic exchange field usually destroys singlet superconductivity. EuFe$_2$(As$_{1-x}$P$_x$)$_2$, an iron-based superconductor with a maximum critical temperature of $\sim$25 K, is a unique material that exhibits full coexistence with ferromagnetic order below $T_\mathrm{FM} \approx 19$ K. The interplay between the two leads to a narrowing of ferromagnetic domains at higher temperatures and the spontaneous nucleation of vortices/antivortices at lower temperatures. Here we demonstrate how the underlying magnetic structure directly controls the superconducting vortex dynamics in applied magnetic fields. Just below $T_\mathrm{FM}$ we observe a pronounced temperature-dependent peak in both the coercivity and the creep activation energy, the latter becoming rapidly suppressed in large applied magnetic fields. We attribute this behaviour to the formation of vortex polarons arising from the unique interaction between free vortices and magnetic stripe domains. We present a theoretical description of the properties of vortex polarons that explains our main observations, showing how they lead to vortex trapping and an attractive vortex-vortex interaction at short distances. In stark contrast, strong magnetic irreversibility at low temperatures is linked to a critical current governed by giant flux creep over an activation barrier for vortex-antivortex annihilation near domain walls. Our work reveals unexplored new routes for the magnetic enhancement of vortex pinning with particularly important applications in high-current conductors for operation at high magnetic fields., Comment: 15 pages, 5 figures

We present the detection and characterization of TOI-4994b (TIC 277128619b), a warm Saturn-sized planet discovered by the NASA Transiting Exoplanet Survey Satellite (TESS). TOI-4994b transits a G-type star (V = 12.6 mag) with a mass, radius, and effective temperature of $M_{\star} =1.005^{+0.064}_{-0.061} M_{\odot}$, $R_{\star} = 1.055^{+0.040}_{-0.037} R_{\odot}$, and $T_{\rm eff} = 5640 \pm 110$ K. We obtained follow-up ground-based photometry from the Las Cumbres Observatory (LCO) and the Antarctic Search for Transiting ExoPlanets (ASTEP) telescopes, and we confirmed the planetary nature of TOI-4994b with multiple radial velocity observations from the PFS, CHIRON, HARPS, FEROS, and CORALIE instruments. From a global fit to the photometry and radial velocities, we determine that TOI-4994b is in a 21.5-day, eccentric orbit ($e = 0.32 \pm 0.04$) and has a mass of $M_{P}= 0.280^{+0.037}_{-0.034} M_{J}$, a radius of $R_{P}= 0.762^{+0.030}_{-0.027}R_{J}$, and a Saturn-like bulk density of $\rho_{p} = 0.78^{+0.16}_{-0.14}$ $\rm g/cm^3$. We find that TOI-4994 is a potentially viable candidate for follow-up stellar obliquity measurements. TOI-4994b joins the small sample of warm Saturn analogs and thus sheds light on our understanding of these rare and unique worlds., Comment: 17 pages, 10 figures. Accepted to AJ

Magnetic reconnection is a ubiquitous process in plasma physics, driving rapid and energetic events such as coronal mass ejections. Reconnection between magnetic fields with arbitrary shear can be decomposed into an anti-parallel, reconnecting component, and a non-reconnecting guide-field component which is parallel to the reconnecting electric field. This guide field modifies the structure of the reconnection layer and the reconnection rate. We present results from experiments on the MAIZE pulsed-power generator (500 kA peak current, 200 ns rise-time) which use two exploding wire arrays, tilted in opposite directions, to embed a guide field in the plasma flows with a relative strength $b\equiv B_g/B_{rec}=\text{0, 0.4, or 1}$. The reconnection layers in these experiments have widths which are less than the ion skin depth, $d_i=c/\omega_{pi}$, indicating the importance of the Hall term, which generates a distinctive quadrupolar magnetic field structure along the separatrices of the reconnection layer. Using laser imaging interferometry, we observe quadrupolar structures in the line-integrated electron density, consistent with the interaction of the embedded guide field with the quadrupolar Hall field. Our measurements extend over much larger length scales ($40 d_i$) at higher $\beta$ ($\sim 1$) than previous experiments, providing an insight into the global structure of the reconnection layer., Comment: 12 pages, 9 figures. Submitted to Physics of Plasmas for review

Recently, the quality of artworks generated using Artificial Intelligence (AI) has increased significantly, resulting in growing difficulties in detecting synthetic artworks. However, limited studies have been conducted on identifying the authenticity of synthetic artworks and their source. This paper introduces AI-ArtBench, a dataset featuring 185,015 artistic images across 10 art styles. It includes 125,015 AI-generated images and 60,000 pieces of human-created artwork. This paper also outlines a method to accurately detect AI-generated images and trace them to their source model. This work proposes a novel Convolutional Neural Network model based on the ConvNeXt model called AttentionConvNeXt. AttentionConvNeXt was implemented and trained to differentiate between the source of the artwork and its style with an F1-Score of 0.869. The accuracy of attribution to the generative model reaches 0.999. To combine the scientific contributions arising from this study, a web-based application named ArtBrain was developed to enable both technical and non-technical users to interact with the model. Finally, this study presents the results of an Artistic Turing Test conducted with 50 participants. The findings reveal that humans could identify AI-generated images with an accuracy of approximately 58%, while the model itself achieved a significantly higher accuracy of around 99%.