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The new era of large-scale data collection and analysis presents an opportunity for diagnosing and understanding the causes of health inequities. In this study, we describe a framework for systematically analyzing health disparities using causal inference. The framework is illustrated by investigating racial and ethnic disparities in intensive care unit (ICU) outcome between majority and minority groups in Australia (Indigenous vs. Non-Indigenous) and the United States (African-American vs. White). We demonstrate that commonly used statistical measures for quantifying inequity are insufficient, and focus on attributing the observed disparity to the causal mechanisms that generate it. We find that minority patients are younger at admission, have worse chronic health, are more likely to be admitted for urgent and non-elective reasons, and have higher illness severity. At the same time, however, we find a protective direct effect of belonging to a minority group, with minority patients showing improved survival compared to their majority counterparts, with all other variables kept equal. We demonstrate that this protective effect is related to the increased probability of being admitted to ICU, with minority patients having an increased risk of ICU admission. We also find that minority patients, while showing improved survival, are more likely to be readmitted to ICU. Thus, due to worse access to primary health care, minority patients are more likely to end up in ICU for preventable conditions, causing a reduction in the mortality rates and creating an effect that appears to be protective. Since the baseline risk of ICU admission may serve as proxy for lack of access to primary care, we developed the Indigenous Intensive Care Equity (IICE) Radar, a monitoring system for tracking the over-utilization of ICU resources by the Indigenous population of Australia across geographical areas.

We present results from a high cadence multi-wavelength observational campaign of the enigmatic changing look AGN 1ES 1927+654 from May 2022- April 2024, coincident with an unprecedented radio flare (an increase in flux by a factor of $\sim 60$ over a few months) and the emergence of a spatially resolved jet at $0.1-0.3$ pc scales (Meyer et al. 2024). Companion work has also detected a recurrent quasi-periodic oscillation (QPO) in the $2-10$ keV band with an increasing frequency ($1-2$ mHz) over the same period (Masterson et al., 2025). During this time, the soft X-rays ($0.3-2$ keV) monotonically increased by a factor of $\sim 8$, while the UV emission remained near-steady with $<30\%$ variation and the $2-10$ keV flux showed variation by a factor $\lesssim 2$. The weak variation of the $2-10$ keV X-ray emission and the stability of the UV emission suggest that the magnetic energy density and accretion rate are relatively unchanged, and that the jet could be launched due to a reconfiguration of the magnetic field (toroidal to poloidal) close to the black hole. Advecting poloidal flux onto the event horizon would trigger the Blandford-Znajek (BZ) mechanism, leading to the onset of the jet. The concurrent softening of the coronal slope (from $\Gamma= 2.70\pm 0.04$ to $\Gamma=3.27\pm 0.04$), the appearance of a QPO, and low coronal temperature ($kT_{e}=8_{-3}^{+8}$ keV) during the radio outburst suggest that the poloidal field reconfiguration can significantly impact coronal properties and thus influence jet dynamics. These extraordinary findings in real time are crucial for coronal and jet plasma studies, particularly as our results are independent of coronal geometry., Comment: Submitted to ApJ after minor referee comments

Recently, arXiv published a work by G. Magli about the eclipse of 1 April 2471 BC and a supposed influence of it on the end of the fourth Egyptian dynasty and the beginning of the fifth one. In Magli's arXiv/2412.13640 paper, the eclipse is defined as the 'Shepseskaf eclipse'. Magli considers that this eclipse happened during the reign of the 'last ruler of the 4th dynasty, Shepseskaf'. Some literature is giving the name of further members of this dynasty. Therefore, here we add some references, besides those given in arXiv. We will show that, in this literature, the same eclipse of 2471 BC is linked to king Userkaf of the fifth dynasty. Some observations about chronological data proposed by Magli are also necessary. Since in arxiv/2412.13640 paper we find mentioned by Magli the pyramid of Cheops (that is Akhet Khufu, a name which is generally translated as the Khufu's horizon) and the symbol of the 'solarized' horizon, N27 of the Gardiner's list, it is necessary to stress that the word Akhet was written with the crested ibis, G25, as clearly shown by the Wadi al-Jarf papyri, which are contemporary to Khufu's reign. Mark Lehner, 2001, recognizing a translation problem, rendered Akhet Khufu as the 'Spirit of Khufu'. The use of the crested ibis, in a locution translated as 'dwellers of the horizon' is attested in a Pepi II letter to his vizier Herkhuf, then three centuries after Khufu., Comment: For supplementary material and a detailed discussion of Akhet, see please my ''The Egyptian Hieroglyph of the Crested Ibis, from the Cheops' pyramid (Akhet Khufu) to the Akhenaten's Glory of Aten'', https://doi.org/10.5281/zenodo.14587114. There I proposed Akhet Khufu as the ''Enduring Forever Glory of Khufu''

As large-scale language models increasingly impact safety-critical domains, ensuring their reliable adherence to well-defined principles remains a fundamental challenge. We introduce Deliberative Alignment, a new paradigm that directly teaches the model safety specifications and trains it to explicitly recall and accurately reason over the specifications before answering. We used this approach to align OpenAI's o-series models, and achieved highly precise adherence to OpenAI's safety policies, without requiring human-written chain-of-thoughts or answers. Deliberative Alignment pushes the Pareto frontier by simultaneously increasing robustness to jailbreaks while decreasing overrefusal rates, and also improves out-of-distribution generalization. We demonstrate that reasoning over explicitly specified policies enables more scalable, trustworthy, and interpretable alignment., Comment: 24 pages

Educational stakeholders are often particularly interested in sparse, delayed student outcomes, like end-of-year statewide exams. The rare occurrence of such assessments makes it harder to identify students likely to fail such assessments, as well as making it slow for researchers and educators to be able to assess the effectiveness of particular educational tools. Prior work has primarily focused on using logs from students full usage (e.g. year-long) of an educational product to predict outcomes, or considered predictive accuracy using a few minutes to predict outcomes after a short (e.g. 1 hour) session. In contrast, we investigate machine learning predictors using students' logs during their first few hours of usage can provide useful predictive insight into those students' end-of-school year external assessment. We do this on three diverse datasets: from students in Uganda using a literacy game product, and from students in the US using two mathematics intelligent tutoring systems. We consider various measures of the accuracy of the resulting predictors, including its ability to identify students at different parts along the assessment performance distribution. Our findings suggest that short-term log usage data, from 2-5 hours, can be used to provide valuable signal about students' long-term external performance., Comment: Accepted to the 15th International Learning Analytics and Knowledge Conference (LAK2025)

Active galactic nuclei (AGN) are powerful sources of panchromatic radiation. All AGN emit in X-rays, contributing around $\sim 5-10\%$ of the AGN bolometric luminosity. The X-ray emitting region, popularly known as the corona, is geometrically and radiatively compact with a size typically $\lesssim 10 \, R_{\rm G}$ (gravitational radii). The rapid and extreme variability in X-rays also suggest that the corona must be a dynamic structure. Decades of X-ray studies have shed much light on the topic, but the nature and origin of AGN corona are still not clearly understood. This is mostly due to the complexities involved in several physical processes at play in the high-gravity, high-density and high-temperature region in the vicinity of the supermassive black hole (SMBH). It is still not clear how exactly the corona is energetically and physically sustained near a SMBH. The ubiquity of coronal emission in AGN points to their fundamental role in black hole accretion processes. In this review we discuss the X-ray observational properties of corona in radio quiet AGN., Comment: Review article, accepted for publication in Frontiers in Astronomy and Space Sciences

Public AI benchmark results are widely broadcast by model developers as indicators of model quality within a growing and competitive market. However, these advertised scores do not necessarily reflect the traits of interest to those who will ultimately apply AI models. In this paper, we seek to understand if and how AI benchmarks are used to inform decision-making. Based on the analyses of interviews with 19 individuals who have used, or decided against using, benchmarks in their day-to-day work, we find that across these settings, participants use benchmarks as a signal of relative performance difference between models. However, whether this signal was considered a definitive sign of model superiority, sufficient for downstream decisions, varied. In academia, public benchmarks were generally viewed as suitable measures for capturing research progress. By contrast, in both product and policy, benchmarks -- even those developed internally for specific tasks -- were often found to be inadequate for informing substantive decisions. Of the benchmarks deemed unsatisfactory, respondents reported that their goals were neither well-defined nor reflective of real-world use. Based on the study results, we conclude that effective benchmarks should provide meaningful, real-world evaluations, incorporate domain expertise, and maintain transparency in scope and goals. They must capture diverse, task-relevant capabilities, be challenging enough to avoid quick saturation, and account for trade-offs in model performance rather than relying on a single score. Additionally, proprietary data collection and contamination prevention are critical for producing reliable and actionable results. By adhering to these criteria, benchmarks can move beyond mere marketing tricks into robust evaluative frameworks.

The development of larger models for medical image analysis has led to increased performance. However, it also affected our ability to explain and validate model decisions. Models can use non-relevant parts of images, also called spurious correlations or shortcuts, to obtain high performance on benchmark datasets but fail in real-world scenarios. In this work, we challenge the capacity of convolutional neural networks (CNN) to classify chest X-rays and eye fundus images while masking out clinically relevant parts of the image. We show that all models trained on the PadChest dataset, irrespective of the masking strategy, are able to obtain an Area Under the Curve (AUC) above random. Moreover, the models trained on full images obtain good performance on images without the region of interest (ROI), even superior to the one obtained on images only containing the ROI. We also reveal a possible spurious correlation in the Chaksu dataset while the performances are more aligned with the expectation of an unbiased model. We go beyond the performance analysis with the usage of the explainability method SHAP and the analysis of embeddings. We asked a radiology resident to interpret chest X-rays under different masking to complement our findings with clinical knowledge. Our code is available at https://github.com/TheoSourget/MMC_Masking and https://github.com/TheoSourget/MMC_Masking_EyeFundus

Uncertainty quantification is crucial in time series prediction, and quantile regression offers a valuable mechanism for uncertainty quantification which is useful for extreme value forecasting. Although deep learning models have been prominent in multi-step ahead prediction, the development and evaluation of quantile deep learning models have been limited. We present a novel quantile regression deep learning framework for multi-step time series prediction. In this way, we elevate the capabilities of deep learning models by incorporating quantile regression, thus providing a more nuanced understanding of predictive values. We provide an implementation of prominent deep learning models for multi-step ahead time series prediction and evaluate their performance under high volatility and extreme conditions. We include multivariate and univariate modelling, strategies and provide a comparison with conventional deep learning models from the literature. Our models are tested on two cryptocurrencies: Bitcoin and Ethereum, using daily close-price data and selected benchmark time series datasets. The results show that integrating a quantile loss function with deep learning provides additional predictions for selected quantiles without a loss in the prediction accuracy when compared to the literature. Our quantile model has the ability to handle volatility more effectively and provides additional information for decision-making and uncertainty quantification through the use of quantiles when compared to conventional deep learning models.

AI models are increasingly prevalent in high-stakes environments, necessitating thorough assessment of their capabilities and risks. Benchmarks are popular for measuring these attributes and for comparing model performance, tracking progress, and identifying weaknesses in foundation and non-foundation models. They can inform model selection for downstream tasks and influence policy initiatives. However, not all benchmarks are the same: their quality depends on their design and usability. In this paper, we develop an assessment framework considering 46 best practices across an AI benchmark's lifecycle and evaluate 24 AI benchmarks against it. We find that there exist large quality differences and that commonly used benchmarks suffer from significant issues. We further find that most benchmarks do not report statistical significance of their results nor allow for their results to be easily replicated. To support benchmark developers in aligning with best practices, we provide a checklist for minimum quality assurance based on our assessment. We also develop a living repository of benchmark assessments to support benchmark comparability, accessible at betterbench.stanford.edu., Comment: Accepted as a Spotlight Poster to NeurIPS 2024

It is widely assumed that dust opacities in molecular clouds follow a power-law profile with an index, $\beta$. Recent studies of the Orion Molecular Cloud (OMC) 2/3 complex, however, show a flattening in the spectral energy distribution (SED) at $ \lambda > 2$ mm implying non-constant indices on scales $\gtrsim$ 0.08 pc. The origin of this flattening is not yet known but it may be due to the intrinsic properties of the dust grains or contamination from other sources of emission. We investigate the SED slopes in OMC 2/3 further using observations of six protostellar cores with NOEMA from 2.9 mm to 3.6 mm and ALMA-ACA in Band 4 (1.9 -- 2.1 mm) and Band 5 (1.6 -- 1.8 mm) on core and envelope scales of $\sim 0.02 - 0.08$ pc. We confirm flattened opacity indices between 2.9 mm and 3.6 mm for the six cores with $\beta \approx -0.16 - 1.45$, which are notably lower than the $\beta$ values of $> 1.3$ measured for these sources on $0.08$ pc scales from single-dish data. Four sources have consistent SED slopes between the ALMA data and the NOEMA data. We propose that these sources may have a significant fraction of emission coming from large dust grains in embedded disks, which biases the emission more at longer wavelengths. Two sources, however, had inconsistent slopes between the ALMA and NOEMA data, indicating different origins of emission. These results highlight how care is needed when combining multi-scale observations or extrapolating single-band observations to other wavelengths., Comment: Accepted to ApJ, 18 pages, 4 figures, 6 Tables

The discovery of the Diary of Merer (papyri Wadi al-Jarf) allows us to see the Egyptian calendar applied in a logbook. The diary is dated to the 26th year of reign of Khufu and describes Merer and his crew transporting the limestone blocks from the Tura quarries to Akhet Khufu, that is, the pyramid of Khufu (Old Kingdom). We find a calendar with 30-day months, 10-day weeks, five epagomenal days (Tallet, 2017) and Merer's job adapted to the seasons of the year. Seasons and epagomenal days have names according to inscriptions on the east wall of the Tehne tomb of Nj-k'-'nh (Nikaiankh), end of Fourth Dynasty or early Fifth Dynasty. The Diary gives us an overall impression of extreme modernity in the logistics of ancient Egypt. And Merer looks like a person with a reputation for good timekeeping at Akhet Khufu., Comment: In this new layout, it is inserted a section about Pierre Tallet's paper regarding the Papyrus H, which is evidencing the epagomenal days. According to Pierre Tallet, 2017, the Wadi el-Jarf papyrus are the first attestation of the epagomenal days and, consequently, of the Egyptian Civil Calendar. A discussion about Ne-User-Re is also given

We present SimpleQA, a benchmark that evaluates the ability of language models to answer short, fact-seeking questions. We prioritized two properties in designing this eval. First, SimpleQA is challenging, as it is adversarially collected against GPT-4 responses. Second, responses are easy to grade, because questions are created such that there exists only a single, indisputable answer. Each answer in SimpleQA is graded as either correct, incorrect, or not attempted. A model with ideal behavior would get as many questions correct as possible while not attempting the questions for which it is not confident it knows the correct answer. SimpleQA is a simple, targeted evaluation for whether models "know what they know," and our hope is that this benchmark will remain relevant for the next few generations of frontier models. SimpleQA can be found at https://github.com/openai/simple-evals., Comment: Blog post: https://openai.com/index/introducing-simpleqa/

Even if the magnetic field in a stellarator is integrable, phase-space integrability for energetic particle guiding center trajectories is not guaranteed. Both trapped and passing particle trajectories can experience convective losses, caused by wide phase-space island formation, and diffusive losses, caused by phase-space island overlap. By locating trajectories that are closed in the angle coordinate but not necessarily closed in the radial coordinate, we can quantify the magnitude of the perturbation that results in island formation. We characterize island width and island overlap in quasihelical (QH) and quasiaxisymmetric (QA) finite-beta equilibria for both trapped and passing energetic particles. For trapped particles in QH, low-shear toroidal precession frequency profiles near zero result in wide island formation. While QA transit frequencies do not cross through the zero resonance, we observe that island overlap is more likely since higher shear results in the crossing of more low-order resonances.

This study systematically analyzes the vulnerability of 36 large language models (LLMs) to various prompt injection attacks, a technique that leverages carefully crafted prompts to elicit malicious LLM behavior. Across 144 prompt injection tests, we observed a strong correlation between model parameters and vulnerability, with statistical analyses, such as logistic regression and random forest feature analysis, indicating that parameter size and architecture significantly influence susceptibility. Results revealed that 56 percent of tests led to successful prompt injections, emphasizing widespread vulnerability across various parameter sizes, with clustering analysis identifying distinct vulnerability profiles associated with specific model configurations. Additionally, our analysis uncovered correlations between certain prompt injection techniques, suggesting potential overlaps in vulnerabilities. These findings underscore the urgent need for robust, multi-layered defenses in LLMs deployed across critical infrastructure and sensitive industries. Successful prompt injection attacks could result in severe consequences, including data breaches, unauthorized access, or misinformation. Future research should explore multilingual and multi-step defenses alongside adaptive mitigation strategies to strengthen LLM security in diverse, real-world environments.

Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims at developing models that can process instances as they arrive, so models constantly adapt to new concepts and the temporal evolution in the stream. In multi-label data stream environments where instances have the peculiarity of belonging simultaneously to more than one class, the problem becomes even more complex and poses unique challenges such as different concept drifts impacting different labels at simultaneous or distinct times, higher class imbalance, or new labels emerging in the stream. This paper proposes a novel approach to multi-label data stream classification called Multi-Label Hoeffding Adaptive Tree (MLHAT). MLHAT leverages the Hoeffding adaptive tree to address these challenges by considering possible relations and label co-occurrences in the partitioning process of the decision tree, dynamically adapting the learner in each leaf node of the tree, and implementing a concept drift detector that can quickly detect and replace tree branches that are no longer performing well. The proposed approach is compared with other 18 online multi-label classifiers on 41 datasets. The results, validated with statistical analysis, show that MLHAT outperforms other state-of-the-art approaches in 12 well-known multi-label metrics.

Divisible convex sets have long been important in the study of Hilbert geometries. When a divisible convex set is an ellipsoid, the Hilbert geometry it induces is the hyperbolic space. In general, strictly convex divisible domains exhibit negative curvature properties, but only the ellipsoid is a CAT(0) space. The notion of p-uniform convexity from the theory of Banach spaces has been proposed as a generalization of the Alexandrov-Toponogov comparison theorems to Finsler manifolds. We prove that a natural Finsler metric on a strictly convex divisible domain is $\beta$-uniformly convex, where the exact constant $\beta$ is related to the regularity of the boundary.

In this paper, we present a detailed analysis of the IRS 17 filament within the intermediate-mass protocluster IRAS 08448-4343 (of $\sim\,10^3\,\rm L_{\odot}$), using ALMA data from the ATOMS 3-mm and QUARKS 1.3-mm surveys. The IRS 17 filament, which spans $\sim$54000 au ($0.26\,\rm pc$) in length and $\sim$4000 au ($0.02\,\rm pc$) in width, exhibits a complex, multi-component velocity field, and harbours hierarchical substructures. These substructures include three bundles of seven velocity-coherent fibers, and 29 dense ($n\sim 10^8\,\rm cm^{-3}$) condensations. The fibers have a median length of $\sim 4500\,\rm au$ and a median width of $\sim 1400\,\rm au$. Among these fibers, four are identified as ``fertile", each hosting at least three dense condensations, which are regarded as the ``seeds" of star formation. While the detected cores are randomly spaced within the IRS\,17 filament based on the 3-mm dust continuum image, periodic spacing ($\sim1600\,\rm au$) of condensations is observed in the fertile fibers according to the 1.3-mm dust map, consistent with the predictions of linear isothermal cylinder fragmentation models. These findings underscore the crucial role of fibers in star formation and suggest a hierarchical fragmentation process that extends from the filament to the fibers, and ultimately, to the smallest-scale condensations., Comment: 19 pages, 10 figures, 4 tables, accepted by ApJ

This research delves into the development of a fatigue detection system based on modern object detection algorithms, particularly YOLO (You Only Look Once) models, including YOLOv5, YOLOv6, YOLOv7, and YOLOv8. By comparing the performance of these models, we evaluate their effectiveness in real-time detection of fatigue-related behavior in drivers. The study addresses challenges like environmental variability and detection accuracy and suggests a roadmap for enhancing real-time detection. Experimental results demonstrate that YOLOv8 offers superior performance, balancing accuracy with speed. Data augmentation techniques and model optimization have been key in enhancing system adaptability to various driving conditions.

We prove that product kernels and flag kernels on a direct product of graded Lie groups $G_1 \times \cdots \times G_{\nu}$ satisfy so-called \emph{tame algebra estimates}. Tame algebra estimates are central to the study of nonlinear partial differential equations via, for instance, the Nash-Moser inverse function theorem. In addition, the special structure of these estimates generates a new Banach-algebraic proof of an inversion theorem for product kernels and flag kernels., Comment: 40 pages

The notion of defects in crystalline phases of matter has been extremely powerful for understanding crystal growth, deformation and melting. These discontinuities in the periodic order of crystals are mathematically described by the Burgers vector, derived from the particle displacements, which encapsulates the direction and magnitude of slip relative to the undeformed state. Since the reference structure of the crystal is known a priori, the Burgers vector can be determined experimentally using both imaging and diffraction methods to measure the lattice distortion, and thus infer the particle displacements. Glasses have structures that lack the periodicity of crystals, and thus a well-defined reference state. Yet, measurable structural parameters can still be obtained from diffraction from a glass. Here we examine the usefulness of these parameters to probe deformation in glasses. We find that coordinated transformations in the centrosymmetry of local particle arrangements are a strong marker of plastic events. Moreover, we investigate two geometric indicators that can be derived from distortions in local diffraction patterns, namely the continuous Burgers vector and the quadrupolar strain. We find that the Burgers vector again emerges as a robust and sensitive metric for understanding local structural transformations due to mechanical deformation, even in structural glasses., Comment: 13 pages 5 figures

The composition of protoplanetary disks, and hence the initial conditions of planet formation, may be strongly influenced by the infall and thermal processing of material during the protostellar phase. Composition of dust and ice in protostellar envelopes, shaped by energetic processes driven by the protostar, serves as the fundamental building material for planets and complex organic molecules. As part of the JWST GO program, "Investigating Protostellar Accretion" (IPA), we observed an intermediate-mass protostar HOPS 370 (OMC2-FIR3) using NIRSpec/IFU and MIRI/MRS. This study presents the gas and ice phase chemical inventory revealed with the JWST in the spectral range of $\sim$2.9 to 28 $\mu$m and explores the spatial variation of volatile ice species in the protostellar envelope. We find evidence for thermal processing of ice species throughout the inner envelope. We present the first high-spatial resolution ($\sim 80$ au) maps of key volatile ice species H$_{2}$O, CO$_{2}$, $^{13}$CO$_2$, CO, and OCN$^-$, which reveal a highly structured and inhomogeneous density distribution of the protostellar envelope, with a deficiency of ice column density that coincides with the jet/outflow shocked knots. Further, we observe high relative crystallinity of H$_{2}$O ice around the shocked knot seen in the H$_2$ and OH wind/outflow, which can be explained by a lack of outer colder material in the envelope along the line of sight due to the irregular structure of the envelope. These observations show clear evidence of thermal processing of the ices in the inner envelope, close to the outflow cavity walls, heated by the luminous protostar., Comment: Submitted to ApJ. Main text:16 pages with 11 figures

This study investigates the effects of radio-opaque artefacts, such as skin markers, breast implants, and pacemakers, on mammography classification models. After manually annotating 22,012 mammograms from the publicly available EMBED dataset, a robust multi-label artefact detector was developed to identify five distinct artefact types (circular and triangular skin markers, breast implants, support devices and spot compression structures). Subsequent experiments on two clinically relevant tasks $-$ breast density assessment and cancer screening $-$ revealed that these artefacts can significantly affect model performance, alter classification thresholds, and distort output distributions. These findings underscore the importance of accurate automatic artefact detection for developing reliable and robust classification models in digital mammography. To facilitate future research our annotations, code, and model predictions are made publicly available., Comment: Code available at https://github.com/biomedia-mira/mammo-artifacts

The discovery of chiral helical magnetism (CHM) in Cr$_{1/3}$NbS$_2$ and the stabilization of a chiral soliton lattice (CSL) has attracted considerable interest in view of their potential technological applications. However, there is an ongoing debate regarding whether the sister compound, Mn$_{1/3}$NbS$_2$, which shares the same crystal structure, exhibits similar nontrivial properties which rely on the stabilization of the lack of inversion symmetry at the magnetic ion. In this study, we conduct a comprehensive investigation of the magnetically ordered states of both compounds, using $^{53}$Cr, $^{55}$Mn and $^{93}$Nb nuclear magnetic resonance. Our results, supported by density functional calculations, detect in a high-quality single crystal of Cr$_{1/3}$NbS$_2$ all the signatures of the monoaxial CHM in a magnetic field, identifying it as a textbook NMR case. The detailed understanding of this prototypic behavior provides a reference for Mn$_{1/3}$NbS$_2$. Despite the much larger density of specific defects in this second single crystal, we confirm the presence of a CHM phase in the Mn compound, characterized by a very large critical field for the forced ferromagnetic phase ($\approx 5$ T for H$\parallel\hat c$)., Comment: 10 pages, 7 figures, Supplemental MAterial

Hub-filament systems are considered as natural sites for high-mass star formation. Kinematic analysis of the surroundings of hub-filaments is essential to better understand high-mass star formation within such systems. In this work, we present a detailed study of the massive Galactic protocluster IRAS 15394$-$5358, using continuum and molecular line data from the ALMA Three-millimeter Observations of Massive Star-forming Regions (ATOMS) survey. The 3~mm dust continuum map reveals the fragmentation of the massive ($\rm M=843~M_{\odot}$) clump into six cores. The core C-1A is the largest (radius = 0.04~pc), the most massive ($\rm M=157~M_{\odot}$), and lies within the dense central region, along with two smaller cores ($\rm M=7~and~3~M_{\odot}$). The fragmentation process is consistent with the thermal Jeans fragmentation mechanism and virial analysis shows that all the cores have small virial parameter values ($\rm \alpha_{vir}<<2$), suggesting that the cores are gravitationally bound. The mass vs. radius relation indicates that three cores can potentially form at least a single massive star. The integrated intensity map of $\rm H^{13}CO^{+}$ shows that the massive clump is associated with a hub-filament system, where the central hub is linked with four filaments. A sharp velocity gradient is observed towards the hub, suggesting a global collapse where the filaments are actively feeding the hub. We discuss the role of global collapse and the possible driving mechanisms for the massive star formation activity in the protocluster., Comment: 23 pages, 19 figures, accepted for publication in MNRAS

Assessing the severity of stenoses in coronary angiography is critical to the patient's health, as coronary stenosis is an underlying factor in heart failure. Current practice for grading coronary lesions, i.e. fractional flow reserve (FFR) or instantaneous wave-free ratio (iFR), suffers from several drawbacks, including time, cost and invasiveness, alongside potential interobserver variability. In this context, some deep learning methods have emerged to assist cardiologists in automating the estimation of FFR/iFR values. Despite the effectiveness of these methods, their reliance on large datasets is challenging due to the distributed nature of sensitive medical data. Federated learning addresses this challenge by aggregating knowledge from multiple nodes to improve model generalization, while preserving data privacy. We propose the first federated detection transformer approach, FeDETR, to assess stenosis severity in angiography videos based on FFR/iFR values estimation. In our approach, each node trains a detection transformer (DETR) on its local dataset, with the central server federating the backbone part of the network. The proposed method is trained and evaluated on a dataset collected from five hospitals, consisting of 1001 angiographic examinations, and its performance is compared with state-of-the-art federated learning methods., Comment: 9 pages, 9 figures, Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14366. Springer, Cham

The JWST and ALMA have detected emission lines from the ionized interstellar medium (ISM), including [OII], [OIII], and hydrogen Balmer series lines, in some of the first galaxies at z>6. These measurements present an opportunity to better understand galaxy assembly histories and may allow important tests of state-of-the-art galaxy formation simulations. It is challenging, however, to model these lines in their proper cosmological context given the huge dynamic range in spatial scales involved. In order to meet this challenge, we introduce a novel sub-grid line emission modeling framework. The framework uses the high-z zoom-in simulation suite from the Feedback in Realistic Environments (FIRE) collaboration. The line emission signals from HII regions within each simulated FIRE galaxy are modeled using the semi-analytic HIILines code. A machine learning, Mixture Density Network, approach is then used to determine the conditional probability distribution for the line luminosity to stellar-mass ratio from the HII regions around each simulated stellar particle given its age, metallicity, and its galaxy's total stellar mass. This conditional probability distribution can then be applied to predict the line luminosities around stellar particles in lower resolution, yet larger volume cosmological simulations. As an example, we apply this approach to the Illustris-TNG simulations at z=6. The resulting predictions for the [OII], [OIII], and Balmer line luminosities as a function of star-formation rate agree well with current observations. Our predictions differ, however, from related work in the literature which lack detailed sub-grid ISM models. This highlights the importance of our multi-scale simulation modeling framework. Finally, we provide forecasts for future line luminosity function measurements from the JWST and quantify the cosmic variance in such surveys., Comment: 19 pages, 12 figures

Node-based programming languages are increasingly popular in media arts coding domains. These languages are designed to be accessible to users with limited coding experience, allowing them to achieve creative output without an extensive programming background. Using LLM-based code generation to further lower the barrier to creative output is an exciting opportunity. However, the best strategy for code generation for visual node-based programming languages is still an open question. In particular, such languages have multiple levels of representation in text, each of which may be used for code generation. In this work, we explore the performance of LLM code generation in audio programming tasks in visual programming languages at multiple levels of representation. We explore code generation through metaprogramming code representations for these languages (i.e., coding the language using a different high-level text-based programming language), as well as through direct node generation with JSON. We evaluate code generated in this way for two visual languages for audio programming on a benchmark set of coding problems. We measure both correctness and complexity of the generated code. We find that metaprogramming results in more semantically correct generated code, given that the code is well-formed (i.e., is syntactically correct and runs). We also find that prompting for richer metaprogramming using randomness and loops led to more complex code.

Milky Way Analogues (MWAs) provide an alternative insight into the various pathways that lead to the formation of disk galaxies with similar properties to the Milky Way. In this study, we explore different selection techniques for identifying MWAs in the SAMI Galaxy Survey. We utilise a nearest neighbours method to define MWAs using four selection parameters including stellar mass ($M_{\star}$), star formation rate ($SFR$), bulge-to-total ratio ($B/T$) and disk effective radius ($R_{\rm{e}}$). Based on 15 different selection combinations, we find that including $M_{\star}$ and SFR is essential for minimising biases in the average MWA properties as compared to the Milky Way. Furthermore, given the Milky Way's smaller-than-average size, selection combinations without $R_{\rm{e}}$ result in MWAs being too large. Lastly, we find that $B/T$ is the least important parameter out of the four tested parameters. Using all four selection criteria, we define the top 10 most Milky Way-like galaxies in the GAMA and Cluster regions of the SAMI survey. These most Milky-Way-like galaxies are typically barred spirals, with kinematically cold rotating disks and reside in a wide range of environments. Surprisingly, we find no significant differences between the MWAs selected from the GAMA and Cluster regions. Our work highlights the importance of using multiple selection criteria for finding MWAs and also demonstrates potential biases in previous MWA studies., Comment: 26 pages, 16 figures, 5 tables, accepted for publication in MNRAS

The purpose of this study was to evaluate the implementation and impact of the Pack program. The Pack was developed by the New York Hall of Science (NYSCI) and includes a digital game and set of curricular and professional development resources that aim to support computational thinking teaching and learning in middle school science and computer science classrooms. The evaluation of the Pack was funded with an Education Innovation and Research grant from the U.S. Department of Education and was carried out by the American Institutes for Research (AIR) during the 2022-23 school year in New York City middle schools. AIR examined the Pack intervention's classroom implementation by teachers and their Grade 6-8 students as well as outcomes related to students' computational thinking skills and their attitudes toward problem solving. Overall, the study yielded promising results about the feasibility and utility of implementing the Pack in middle school science and computer science classrooms. Based on teacher survey and interview responses, teachers reported that NYSCI's professional development was helpful to their teaching and reported that the Pack digital game and lessons were valuable for computational thinking teaching and learning. Using teacher survey and extant data, AIR found that teachers were able to implement the Pack program components with fidelity. To measure the relationship between students' use of the Pack and outcomes related to students' attitudes toward problem solving and computational thinking skills, AIR administered a pre- and posttest survey of student problem solving attitudes and a computational thinking knowledge assessment. AIR found that there were no significant associations between students' use of the Pack and their beliefs about their ability to apply their problem-solving skills to new situations or their beliefs about problem-solving ability as a fixed trait. Similarly, there were no significant associations between teachers' or students' reported use of the Pack and students' scores on a computational thinking assessment. There were also no significant associations between students' reported participation in computational thinking while using the Pack and their scores on a computational thinking assessment.