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Mastery learning -- the process by which students must demonstrate proficiency with a single topic before moving on -- is well recognized as one of the best ways to learn, yet many teachers struggle or remain unsure about how to implement it into a classroom setting. This study leverages two field experiments to test the efficacy of a program designed to encourage greater mastery learning through technology and proactive continuous teacher support. Focusing on elementary and middle school mathematics, teachers receive weekly coaching in how to use Computer Assisted Learning (CAL) for students to follow a customized roadmap of incremental progress. Results indicate significant intent-to-treat effects on math performance of 0.12-0.22 standard deviations. Further analysis shows that these gains are concentrated among students in classrooms with at least an average of 35 minutes of practice per week. Teachers able to achieve high-dosage practice have a high degree of initial buy-in, a clear implementation strategy for when practice occurs, and a willingness to closely monitor progress and follow-up with struggling students. [The Wilson Sheehan Lab for Economic Opportunities (LEO) at Notre Dame provided additional support for this research.]

Stellar streams from disrupted globular clusters are dynamically cold structures that are sensitive to perturbations from dark matter subhalos, allowing them in principle to trace the dark matter substructure in the Milky Way. We model, within the context of $\Lambda$CDM, the likelihood of dark matter subhalos to produce a significant feature in a GD-1-like stream and analyze the properties of such subhalos. We generate a large number of realizations of the subhalo population within a Milky Way mass host halo, accounting for tidal stripping and dynamical friction, using the semi-analytic code SatGen. The subhalo distributions are combined with a GD-1-like stream model, and the impact of subhalos that pass close to the stream are modeled with Gala. We find that subhalos with masses in the range $5\times 10^6 M_{\odot} - 10^8 M_{\odot}$ at the time of the stream-subhalo encounter, corresponding to masses of about $4 \times 10^7 M_{\odot} - 8 \times 10^8 M_{\odot}$ at the time of infall, are the likeliest to produce gaps in a GD-1-like stream. We find that gaps occur on average $\sim$1.8 times per realization of the host system. These gaps have typical widths of $\sim(7 - 27)$ deg and fractional underdensities of $\sim (10 - 30)\%$, with larger gaps being caused by more-massive subhalos. The stream-subhalo encounters responsible for these have impact parameters $(0.1 - 1.5)$ kpc and relative velocities $\sim(170 - 410)$ km/s. For a larger host-halo mass, the number of subhalos increases, as do their typical velocities, inducing a corresponding increase in the number of significant stream-subhalo encounters., Comment: 20 pages, 10 figures

We present phi-4, a 14-billion parameter language model developed with a training recipe that is centrally focused on data quality. Unlike most language models, where pre-training is based primarily on organic data sources such as web content or code, phi-4 strategically incorporates synthetic data throughout the training process. While previous models in the Phi family largely distill the capabilities of a teacher model (specifically GPT-4), phi-4 substantially surpasses its teacher model on STEM-focused QA capabilities, giving evidence that our data-generation and post-training techniques go beyond distillation. Despite minimal changes to the phi-3 architecture, phi-4 achieves strong performance relative to its size -- especially on reasoning-focused benchmarks -- due to improved data, training curriculum, and innovations in the post-training scheme.

We study the rest-frame optical and ultraviolet morphology of galaxies in the first billion years of the Universe. Using JWST data from the UNCOVER and MegaScience surveys targeting the lensing cluster Abell 2744 we present multi-band morphological measurements for a sample of 995 galaxies selected using 20-band NIRCam photometry and 35 using NIRSpec Prism spectroscopy over the redshift range of $4

The Breakthrough Listen program is, to date, the most extensive search for technological life beyond Earth. As part of this goal, over the past nine years it has surveyed thousands of nearby stars, close to 100 nearby galaxies, and a variety of exotic and solar system objects with telescopes around the world, including the Robert C. Byrd Green Bank Telescope (GBT) in West Virginia. The goal is to find evidence of technosignatures of other civilizations, such as narrowband Doppler drifting radio signals. Despite the GBT's location in a radio-quiet zone, the primary challenge of this search continues to be the high quantities of human-generated radio-frequency interference (RFI), and the ability to pick out genuinely promising candidates from it. Here we present a novel search method aimed at finding these `needle-in-a-haystack' type signals, applied to 9,684 observation cadences of 3,077 stars (each observed with one or more of the L, S, C, and X band receivers) from the GBT archive. We implement a low-complexity statistical process to vet out RFI and highlight signals that, upon visual inspection, appear more promising than those from previous analyses. Our work returns candidate signals found previously using both traditional and machine learning algorithms, as well as many promising ones not previously identified. This analysis represents the largest dataset searched for technosignatures to date, and highlights the efficacy that traditional (non-machine-learning) algorithms continue to have in these types of technosignature searches. We find that less than 1% of stars host transmitters brighter than 0.3 Arecibo radar equivalents broadcasting in our direction over the frequency band covered., Comment: 24 pages, 17 figures. Submitted to Astronomy & Astrophysics

We present a detailed exploration of the most optically-luminous Little Red Dot ($L_{H\alpha}=10^{44}$erg/s, $L_V=10^{45}$erg/s, F444W=22AB) found to date. Located in the Abell 2744 field, source A744-45924 was observed by NIRSpec/PRISM with ultradeep spectroscopy reaching SNR$\sim$100pix$^{-1}$, high-resolution 3-4 micron NIRCam/Grism spectroscopy, and NIRCam Medium Band imaging. The NIRCam spectra reveal high rest-frame EW $W_{H\alpha,0,broad}>800$\r{A}, broad H$\alpha$ emission (FWHM$\sim$4500 km/s), on top of narrow, complex absorption. NIRSpec data show exceptionally strong rest-frame UV to NIR Fe II emission ($W_{FeII-UV,0}\sim$340\r{A}), N IV]$\lambda\lambda$1483,1486 and N III]$\lambda$1750, and broad NIR O I $\lambda$8446 emission. The spectra unambiguously demonstrate a broad-line region associated with an inferred $M_{BH}\sim10^9M_\odot$ supermassive black hole embedded in dense gas, which might explain a non-detection in ultradeep Chandra X-ray data (>$10\times$ underluminous relative to broad $L_{H\alpha}$). Strong UV Nitrogen lines suggest supersolar N/O ratios due to rapid star formation or intense radiation near the AGN. The continuum shows a clear Balmer break at rest-frame 3650\r{A}, which cannot be accounted for by an AGN power-law alone. A stellar population model produces an excellent fit with a reddened Balmer break and implying a massive ($M_*\sim8\times10^{10}M_\odot$), old $\sim$500 Myr, compact stellar core, among the densest stellar systems known ($\rho\sim3\times10^6M_\odot$/pc$^2$ for $R_{e,opt}=70\pm10$ pc), and AGN emission with extreme intrinsic EW $W_{H\alpha,0}\gg$1000\r{A}. However, although high $M_*$ and $M_{BH}$ are supported by evidence of an overdensity containing 40 galaxies at $z=4.41-4.51$, deep high-resolution spectroscopy is required to confirm stellar absorption and rule out that dense gas around the AGN causes the Balmer break instead., Comment: 28 pages,10 figures, submitted to ApJ

We introduce Best-of-N (BoN) Jailbreaking, a simple black-box algorithm that jailbreaks frontier AI systems across modalities. BoN Jailbreaking works by repeatedly sampling variations of a prompt with a combination of augmentations - such as random shuffling or capitalization for textual prompts - until a harmful response is elicited. We find that BoN Jailbreaking achieves high attack success rates (ASRs) on closed-source language models, such as 89% on GPT-4o and 78% on Claude 3.5 Sonnet when sampling 10,000 augmented prompts. Further, it is similarly effective at circumventing state-of-the-art open-source defenses like circuit breakers. BoN also seamlessly extends to other modalities: it jailbreaks vision language models (VLMs) such as GPT-4o and audio language models (ALMs) like Gemini 1.5 Pro, using modality-specific augmentations. BoN reliably improves when we sample more augmented prompts. Across all modalities, ASR, as a function of the number of samples (N), empirically follows power-law-like behavior for many orders of magnitude. BoN Jailbreaking can also be composed with other black-box algorithms for even more effective attacks - combining BoN with an optimized prefix attack achieves up to a 35% increase in ASR. Overall, our work indicates that, despite their capability, language models are sensitive to seemingly innocuous changes to inputs, which attackers can exploit across modalities.

Segmenting stroke lesions in Magnetic Resonance Imaging (MRI) is challenging due to diverse clinical imaging domains, with existing models struggling to generalise across different MRI acquisition parameters and sequences. In this work, we propose two novel physics-constrained approaches using synthetic quantitative MRI (qMRI) images to enhance the robustness and generalisability of segmentation models. We trained a qMRI estimation model to predict qMRI maps from MPRAGE images, which were used to simulate diverse MRI sequences for segmentation training. A second approach built upon prior work in synthetic data for stroke lesion segmentation, generating qMRI maps from a dataset of tissue labels. The proposed approaches improved over the baseline nnUNet on a variety of out-of-distribution datasets, with the second approach outperforming the prior synthetic data method.

In the last decade, we have been able to probe further down the galaxy luminosity function than ever before and expand into the regime of ultra-faint dwarfs (UFDs), which are some of the best probes we have of small-scale cosmology and galaxy formation. Digital sky surveys have enabled the discovery and study of these incredibly low-mass, highly dark-matter dominated systems around the Local Group, but it is critical that we expand the satellite census further out to understand if Milky Way and M31 satellites are representative of dwarf populations in the local Universe. Using data from HST/ACS, we present updated characterization of four satellite systems in the M81 group. These systems - D1005+68, D1006+69, DWJ0954+6821, and D1009+68 - were previously discovered using ground-based Subaru HSC data as overdensities in M81's halo and are now confirmed with HST/ACS by this work. These are all faint (M_V >= -7.9) and consistent with old (~13 Gyr), metal-poor ([M/H] < -1.5) populations. Each system possesses relatively unusual features - including one of the most concentrated satellite galaxies with a Sersic index of n ~ 5, one of the most elliptical galaxies outside the Local Group with an e ~ 0.6, and one of the most compact galaxies for its magnitude. Two of the satellites have very low surface brightness, lower than most known galaxies in this absolute magnitude range. This work previews the scientific promise of the upcoming Rubin Observatory and Roman Telescope for illuminating the diversity of UFDs in the Local Volume and beyond., Comment: 24 pages, 12 figures, 1 appendix, accepted to ApJ

Personalized image generation has emerged from the recent advancements in generative models. However, these generated personalized images often suffer from localized artifacts such as incorrect logos, reducing fidelity and fine-grained identity details of the generated results. Furthermore, there is little prior work tackling this problem. To help improve these identity details in the personalized image generation, we introduce a new task: reference-guided artifacts refinement. We present Refine-by-Align, a first-of-its-kind model that employs a diffusion-based framework to address this challenge. Our model consists of two stages: Alignment Stage and Refinement Stage, which share weights of a unified neural network model. Given a generated image, a masked artifact region, and a reference image, the alignment stage identifies and extracts the corresponding regional features in the reference, which are then used by the refinement stage to fix the artifacts. Our model-agnostic pipeline requires no test-time tuning or optimization. It automatically enhances image fidelity and reference identity in the generated image, generalizing well to existing models on various tasks including but not limited to customization, generative compositing, view synthesis, and virtual try-on. Extensive experiments and comparisons demonstrate that our pipeline greatly pushes the boundary of fine details in the image synthesis models.

Observational studies are identifying stars thought to be remnants from the earliest stages of the hierarchical mass assembly of the Milky Way, referred to as the proto-Galaxy. We use red giant stars with kinematics and [$\alpha$/M] and [M/H] estimates from Gaia DR3 data to investigate the relationship between azimuthal velocity and metallicity, aiming to understand the transition from a chaotic proto-Galaxy to a well-ordered, rotating (old) disc-like population. To analyse the structure of the data in [M/H]-v$_\phi$ space for both high- and low-$\alpha$ samples with carefully defined $\alpha$-separation, we develop a model with two Gaussian components in v$_\phi$: one representing a disc-like population and the other a halo-like population. This model is designed to capture the conditional distribution P(v$_\phi$ $\mid$ [M/H]) with a 2-component Gaussian Mixture Model with fixed azimuthal velocities means and standard deviations. To quantify the spin-up of the high-$\alpha$ disc population, we extend this two-component model by allowing the mean velocity and velocity dispersion to vary between the spline knots across the metallicity range used. We also compare our findings with existing literature using traditional Gaussian Mixture Modelling in bins of [M/H] and investigate using orbital circularity instead of azimuthal velocity. Our findings show that the metal-poor high-$\alpha$ disc gradually spins up across [M/H] $\sim$ -1.7 to -1.0, while the low-$\alpha$ sample exhibits a sharp transition at [M/H] $\sim$ -1.0. This latter result is due to the accreted debris dominating the metal-poor end, underscoring the critical role of [$\alpha$/M] selection in studying the (old) disc evolution of the Milky Way. These results indicate that the proto-Galaxy underwent a slow, monotonic spin-up phase rather than a rapid, dramatic spin-up at [M/H] $\sim$ -1.0, as previously inferred., Comment: Submitted to A&A. 19 pages + 11 figures, 6 pages + 5 figures in appendix. Comments are very welcome!

Interactions and reconnections of vortices are fundamental in many areas of physics, including classical and quantum fluids where they are central to understanding phenomena such as turbulence. In three-dimensional (3D) superfluids, quantum vortices are one-dimensional (1D) filaments that can intersect, reconnect, and recoil with irreversible dynamics described by near-universal scaling laws. We explore quantum-vortex reconnections in a four-dimensional (4D) superfluid, where a vortex is a two-dimensional (2D) surface. Using real-time numerical simulations, we find much richer behaviour than in 3D, including stable intersecting vortex surfaces which do not reconnect, and unusual vortex reconnections which occur without significant energy dissipation, suggesting quasi-reversible dynamics. Our work raises many interesting questions about vortex physics in extra-dimensional systems, and may be extended in the future to more realistic experimental settings where the fourth dimension is simulated with techniques such as synthetic dimensions., Comment: 8 pages, 4 figures

Spatially-resolved emission line kinematics are invaluable to investigating fundamental galaxy properties and have become increasingly accessible for galaxies at $z\gtrsim0.5$ through sensitive near-infrared imaging spectroscopy and millimeter interferometry. Kinematic modeling is at the core of the analysis and interpretation of such data sets, which at high-z present challenges due to lower signal-to-noise ratio (S/N) and resolution compared to data of local galaxies. We present and test the 3D fitting functionality of DysmalPy, examining how well it recovers intrinsic disk rotation velocity and velocity dispersion, using a large suite of axisymmetric models, covering a range of galaxy properties and observational parameters typical of $z\sim1$-$3$ star-forming galaxies. We also compare DysmalPy's recovery performance to that of two other commonly used codes, GalPak3D and 3DBarolo, which we use in turn to create additional sets of models to benchmark DysmalPy. Over the ranges of S/N, resolution, mass, and velocity dispersion explored, the rotation velocity is accurately recovered by all tools. The velocity dispersion is recovered well at high S/N, but the impact of methodology differences is more apparent. In particular, template differences for parametric tools and S/N sensitivity for the non-parametric tool can lead to differences up to a factor of 2. Our tests highlight and the importance of deep, high-resolution data and the need for careful consideration of: (1) the choice of priors (parametric approaches), (2) the masking (all approaches) and, more generally, evaluating the suitability of each approach to the specific data at hand. This paper accompanies the public release of DysmalPy., Comment: 38 pages, 21 figures, accepted for publication in ApJ

Among the most puzzling early discoveries of JWST are "Little Red Dots" -- compact red sources that host broad Balmer emission lines and, in many cases, exhibit a "V shaped" change in slope in the rest-optical. The physical properties of Little Red Dots currently have order-of-magnitude uncertainties, because models to explain the continuum of these sources differ immensely. Here, we leverage the complete selection of red sources in the RUBIES program, supplemented with public PRISM spectra, to study the origin of this "V shape". By fitting a broken power law with a flexible inflection point, we find that a large fraction (20/44, nearly all spatially unresolved) of extremely red H$\alpha$ emitters at $2

Powered ankle-foot prostheses can often reduce the energy cost of walking by assisting with push-off. However, focus on providing mechanical work may lead to ignoring or exacerbating common issues with chronic pain, irritation, pressure ulcer development, and eventual osteoarthritis in persons with amputation. This paper presents the design and validation of a novel transtibial prosthesis informed by predictive biomechanical simulations of gait which minimize a combination of user effort and interaction loading from the prosthesis socket. From these findings, the device was designed with a non-biomimetic anterior-posterior translation degree of freedom with a 10 cm range of motion which is primarily position-controlled to change the alignment of the prosthetic foot with the residual limb. The system is both mobile and tethered, with the batteries, actuators, and majority of electronics located in a small backpack. Mechanical loads are transmitted through cables to the prosthesis, minimizing the distal mass carriage required. We measured torque and force sensing accuracy, open loop actuator performance, closed loop torque and position control bandwidth, and torque and position tracking error during walking. The system is capable of producing up to 160 N-m of plantarflexion torque and 394 N of AP translation force with a closed loop control bandwidth of about 7 Hz in both degrees of freedom. Torque tracking during walking was accurate within about 10 N-m but position tracking was substantially affected by phase lag, possibly due to cable slack in the bidirectional mechanism. The prototype was capable of replicating our simulated prosthesis dynamics during gait and offers useful insights into the advantages and the practical considerations of using predictive biomechanical simulation as a design tool for wearable robots., Comment: 9 pages, 8 figures, 1 table

Understanding the nature of dark matter in the Universe is an important goal of modern cosmology. A key method for probing this distribution is via weak gravitational lensing mass-mapping - a challenging ill-posed inverse problem where one infers the convergence field from observed shear measurements. Upcoming stage IV surveys, such as those made by the Vera C. Rubin Observatory and Euclid satellite, will provide a greater quantity and precision of data for lensing analyses, necessitating high-fidelity mass-mapping methods that are computationally efficient and that also provide uncertainties for integration into downstream cosmological analyses. In this work we introduce MMGAN, a novel mass-mapping method based on a regularised conditional generative adversarial network (GAN) framework, which generates approximate posterior samples of the convergence field given shear data. We adopt Wasserstein GANs to improve training stability and apply regularisation techniques to overcome mode collapse, issues that otherwise are particularly acute for conditional GANs. We train and validate our model on a mock COSMOS-style dataset before applying it to true COSMOS survey data. Our approach significantly outperforms the Kaiser-Squires technique and achieves similar reconstruction fidelity as alternative state-of-the-art deep learning approaches. Notably, while alternative approaches for generating samples from a learned posterior are slow (e.g. requiring $\sim$10 GPU minutes per posterior sample), MMGAN can produce a high-quality convergence sample in less than a second.

The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of 503,261 knee radiographs from 83,011 patients, 40% of which are African American. This dataset provides imaging data in DICOM format along with detailed clinical information, including patient-reported pain scores, diagnostic codes, and procedural codes, which are not commonly available in similar datasets. The MRKR dataset also features imaging metadata such as image laterality, view type, and presence of hardware, enhancing its value for research and model development. MRKR addresses significant gaps in existing datasets by offering a more representative sample for studying osteoarthritis and related outcomes, particularly among minority populations, thereby providing a valuable resource for clinicians and researchers., Comment: 16 pages

We present the results of Phase I of an ongoing review of Monte Carlo tools relevant for low-energy hadronic cross sections. This includes a detailed comparison of Monte Carlo codes for electron-positron scattering into a muon pair, pion pair, and electron pair, for scan and radiative-return experiments. After discussing the various approaches that are used and effects that are included, we show differential cross sections obtained with AfkQed, BabaYaga@NLO, KKMC, MCGPJ, McMule, Phokhara, and Sherpa, for scenarios that are inspired by experiments providing input for the dispersive evaluation of the hadronic vacuum polarisation., Comment: RadioMonteCarLow 2 Working Group report Phase I, 67 pages, 34 figures; references added and other minor modifications

The association between preoperative cognitive status and surgical outcomes is a critical, yet scarcely explored area of research. Linking intraoperative data with postoperative outcomes is a promising and low-cost way of evaluating long-term impacts of surgical interventions. In this study, we evaluated how preoperative cognitive status as measured by the clock drawing test contributed to predicting length of hospital stay, hospital charges, average pain experienced during follow-up, and 1-year mortality over and above intraoperative variables, demographics, preoperative physical status and comorbidities. We expanded our analysis to 6 specific surgical groups where sufficient data was available for cross-validation. The clock drawing images were represented by 10 constructional features discovered by a semi-supervised deep learning algorithm, previously validated to differentiate between dementia and non-dementia patients. Different machine learning models were trained to classify postoperative outcomes in hold-out test sets. The models were compared to their relative performance, time complexity, and interpretability. Shapley Additive Explanations (SHAP) analysis was used to find the most predictive features for classifying different outcomes in different surgical contexts. Relative classification performances achieved by different feature sets showed that the perioperative cognitive dataset which included clock drawing features in addition to intraoperative variables, demographics, and comorbidities served as the best dataset for 12 of 18 possible surgery-outcome combinations...

Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. We summarise new developments, essential features, and ongoing improvements within the Sherpa 3 release series. Physics improvements include higher-order electroweak corrections, simulations of photoproduction and hard diffraction at NLO QCD, heavy-flavour matching in NLO multijet merging, spin-polarised cross section calculations, and a new model of colour reconnections. In addition, the modelling of hadronisation, the underlying event and QED effects in both production and decay has been improved, and the overall event generation efficiency has been enhanced., Comment: We proudly present the new major release of the Sherpa MC generator

Many nucleosynthetic channels create the elements, but two-parameter models characterized by $\alpha$ and Fe nonetheless predict stellar abundances in the Galactic disk to accuracies of 0.02 to 0.05 dex for most measured elements, near the level of current abundance uncertainties. It is difficult to make individual measurements more precise than this to investigate lower-amplitude nucleosynthetic effects, but population studies of mean abundance patterns can reveal more subtle abundance differences. Here we look at the detailed abundances for 67315 stars from APOGEE DR17, but in the form of abundance residuals away from a best-fit two-parameter, data-driven nucleosynthetic model. We find that these residuals show complex structures with respect to age, guiding radius, and vertical action that are not random and are also not strongly correlated with sources of systematic error such as surface gravity, effective temperature, and radial velocity. The residual patterns, especially in Na, C+N, Ni, Mn, and Ce, trace kinematic structures in the Milky Way, such as the inner disk, thick disk, and flared outer disk. A principal component analysis suggests that most of the observed structure is low-dimensional and can be explained by a few eigenvectors. We find that some, but not all, of the effects in the low-$\alpha$ disk can be explained by dilution with fresh gas, so that abundance ratios resemble those of stars with higher metallicity. The patterns and maps we provide could be combined with accurate forward models of nucleosynthesis, star formation, and gas infall to provide a more detailed picture of star and element formation in different Milky Way components.

Stellar streams retain a memory of their gravitational interactions with small-scale perturbations. While perturbative models for streams have been formulated in action-angle coordinates, a direct transformation to these coordinates is only available for static and typically axisymmetric models for the galaxy. The real Milky Way potential is in a state of disequilibrium, complicating the application of perturbative methods around an equilibrium system. Here, we utilize a combination of differentiable simulations and Hamiltonian perturbation theory to model the leading-order effect of dark matter subhalos on stream observables. To obtain a perturbative description of streams, we develop a direct and efficient forward mode differentiation of Hamilton's equations of motion. Our model operates in observable coordinates, allowing us to treat the effects of arbitrary subhalo potentials on streams perturbatively, while simultaneously capturing non-linear effects due to other substructures like the infalling LMC or the rotating bar. The model predicts the velocity dispersion of streams as a function of subhalo statistics, allowing us to constrain the low-mass range of subhalos down to $\sim 10^5~M_\odot$. We forecast the velocity dispersion of the GD-1 stream, and find that observations are in agreement with a CDM subhalo population, with a slight preference for more dense subhalos. The method provides a new approach to characterize streams in the presence of substructure, with significantly more modeling flexibility compared to previous works., Comment: 31 pages (+5 appendix), 17 figures (+4 appendix). Submitted to ApJ. Comments welcome. Code release will accompany publication

We investigate the effect of data-driving on flux rope eruptivity in magnetic field simulations by analysing fully data-driven modelling results of active region (AR) 12473 and AR11176, as well as preforming relaxation runs for AR12473 (found to be eruptive). Here, the driving is switched off systematically at different time steps. We analyse the behaviour of fundamental quantities, essential for understanding the eruptivity of magnetic flux ropes (MFRs). The data-driven simulations are carried out with the time-dependent magnetofrictional model (TMFM) for AR12473 and AR11176. For the relaxation runs, we employ the magnetofrictional method (MFM) and a zero-beta magnetohydrodynamic (MHD) model to investigate how significant the differences between the two relaxation procedures are when started from the same initial conditions. To determine the eruptivity of the MFRs, we calculate characteristic geometric properties, such as the cross-section, MFR height along with stability parameters, such as MFR twist and the decay index. For eruptive cases, we investigate the effect of sustained driving beyond the point of eruptivity on the MFR properties. We find that the fully-driven AR12473 MFR is eruptive while the AR11176 MFR is not. For the relaxation runs, we find that the MFM MFRs are eruptive when the driving is stopped around the flare time or later, while the MHD MFRs show eruptive behaviour even if the driving is switched off one and a half days before the flare occurs. We find that characteristic MFR properties can vary greatly even for the eruptive cases of different relaxation simulations. The results suggest that data driving can significantly influence the evolution of the eruption, with differences appearing even when the relaxation time is set to later stages of the simulation when the MFRs have already entered an eruptive phase., Comment: Accepted for publication at A&A

Late-stage infall onto evolved protoplanetary discs is an important source of material and angular momentum replenishment, and disc substructures. In this paper we used 3D smoothed particle hydrodynamics simulations to model streamer-disc interactions for a prograde streamer. The initially parabolic streamer interacts with the disc material to excite disc eccentricity, which can last on the order of $10^5$ years. We found that the spiral arms the streamer excited in the disc can have a variety of pattern speeds, ranging from stationary to super-Keplerian. Spiral arms with various pattern speeds can exist simultaneously, providing a way to diagnose them in observations. Streamer induced spirals appear similar to those generated by a massive outer companion, where the pitch angle of the spiral increases towards the source of the perturbation. Additionally, the spirals arms can show large and sudden pitch angle changes. Streamer induced spirals are long-lived, lasting approximately $3-4\times$ longer than the initial streamer infall timescale ($\sim$$10^4$ years). After the initial interaction with the disc, a long lasting low $m$ azimuthal mode persists in the disc., Comment: 12 pages, 11 figures, submitted to MNRAS, comments welcomed!

Scientific workflows have become highly heterogenous, leveraging distributed facilities such as High Performance Computing (HPC), Artificial Intelligence (AI), Machine Learning (ML), scientific instruments (data-driven pipelines) and edge computing. As a result, Identity and Access Management (IAM) and Cybersecurity challenges across the diverse hardware and software stacks are growing. Nevertheless, scientific productivity relies on lowering access barriers via seamless, single sign-on (SSO) and federated login while ensuring access controls and compliance. We present an implementation of a federated IAM solution, which is coupled with multiple layers of security controls, multi-factor authentication, cloud-native protocols, and time-limited role-based access controls (RBAC) that has been co-designed and deployed for the Isambard-AI and HPC supercomputing Digital Research Infrastructures (DRIs) in the UK. Isambard DRIs as a national research resource are expected to comply with regulatory frameworks. Implementation details for monitoring, alerting and controls are outlined in the paper alongside selected user stories for demonstrating IAM workflows for different roles., Comment: 9 pages, 2 figures

We introduce a novel pipeline for segmenting and structuring color and depth (RGB-D) data. Existing processing pipelines for RGB-D data have focused on extracting geometric information alone. This approach precludes the development of more advanced robotic navigation and manipulation algorithms, which benefit from a semantic understanding of their environment. Our pipeline can segment RGB-D data into accurate semantic masks. These masks are then used to fuse raw captured point clouds into semantically separated point clouds. We store this information using the Universal Scene Description (USD) file format, a format suitable for easy querying by downstream robotics algorithms, human-friendly visualization, and robotics simulation.

A key hallmark of quantum Hall physics is the existence of topological chiral states at the system boundary. Signatures of these edge states have been experimentally observed in cold atoms by using different approaches, including notably that of ``synthetic dimension'' in which internal states are coupled together and reinterpreted as sites along an artificial spatial dimension. However, previous atomic synthetic dimension implementations have been limited to relatively small system sizes with inflexible boundaries. In this paper, we propose instead how to use a synthetic dimension of atomic trap states to observe chiral edge states in a large quantum Hall system with a tunable edge. We present numerical simulations for relevant experimental parameters, showing how this scheme may be used to probe the properties and robustness of the edge states to defects. Our work opens the way for future experiments in topological physics with synthetic dimensions, while also providing new ways to manipulate and control highly-excited trap states., Comment: 21 Pages, 20 figures

We propose new methods for multivariate linear regression when the regression coefficient matrix is sparse and the error covariance matrix is dense. We assume that the error covariance matrix has equicorrelation across the response variables. Two procedures are proposed: one is based on constant marginal response variance (compound symmetry), and the other is based on general varying marginal response variance. Two approximate procedures are also developed for high dimensions. We propose an approximation to the Gaussian validation likelihood for tuning parameter selection. Extensive numerical experiments illustrate when our procedures outperform relevant competitors as well as their robustness to model misspecification.

The dearth of high quality spectroscopy of dusty star-forming galaxies (DSFGs) -- the main drivers of the assembly of dust and stellar mass at the peak of activity in the Universe -- greatly hinders our ability to interpret their physical processes and evolutionary pathways. We present JWST/NIRSpec observations from RUBIES of four submillimeter-selected, ALMA-detected DSFGs at cosmic noon, $z\sim2.3-2.7$. While photometry uniformly suggests vigorous ongoing star formation for the entire sample in line with canonical DSFGs, the spectra differ: one source has spectroscopic evidence of an evolved stellar population, indicating a recent transition to a post-starburst phase, while the remainder show strong spectroscopic signatures of ongoing starbursts. All four galaxies are infrared-luminous (log$_{10}$$L_{\rm{IR}}$/L$_{\rm \odot}$ $>12.4$), massive (log$_{10}\,M_\star$/M$_{\rm \odot}$ $>11$), and very dust-obscured ($A_V\sim3-4$ ABmag). Leveraging detections of multiple Balmer and Paschen lines, we derive an optical attenuation curve consistent with Calzetti overall, yet an optical extinction ratio $R_V\sim2.5$, potentially indicating smaller dust grains or differences in star-dust geometry. This case study provides some of the first detailed spectroscopic evidence that the DSFGs encompass a heterogeneous sample spanning a range of star formation properties and evolutionary stages, and illustrates the advantages of synergistic JWST and ALMA analysis of DSFGs., Comment: 20 pages, 4 figures; submitted to ApJ

JWST has revealed an abundance of compact, red objects at $z\approx5-8$ dubbed "little red dots" (LRDs), whose SEDs display a faint blue UV continuum followed by a steep rise in the optical. Despite extensive study of their characteristic V-shaped SEDs, the nature of LRDs remains unknown. We present a new analysis of the NIRSpec/PRISM spectrum of A2744-QSO1, a triply imaged LRD at $z=7.04$ from the UNCOVER survey. The spectrum shows a strong Balmer break and broad Balmer emission lines, both of which are difficult to explain with models invoking exclusively AGN or stellar contributions. Our fiducial model decomposes the spectrum into a post-starburst galaxy dominating the UV-optical continuum and a reddened AGN being sub-dominant at all wavelength and contributing at $\sim20\%$ level. However, our most credible model infers a stellar mass of $M_\star\approx 4\times10^9\,\mathrm{M_\odot}$ within a radius of $r_\mathrm{e}<30\,$pc, driving its central density to the highest among observations to date. This high central density could be explained if A2744-QSO-1 is the early-forming core of a modern-day massive elliptical galaxy that later puffed up via the inside-out growth channel. The models also necessitate an unusually steep dust law to preserve the strong break strength, though this steepness may be explained by a deficit of large dust grains. It is also probable that these challenges reflect our ignorance of A2744-QSO1's true nature. Future variability and reverberation mapping studies could help disentangle the galaxy and AGN contribution to the continuum, and deeper redder observations could also unveil the dust properties in LRDs., Comment: 19 pages, 7 figures, 2 tables. Submitted to ApJ. Comments are welcome

HIP 41378 f is a sub-Neptune exoplanet with an anomalously low density. Its long orbital period and deep transit make it an ideal candidate for detecting oblateness photometrically. We present a new cross-platform, GPU-enabled code greenlantern, suitable for computing transit light curves of oblate planets at arbitrary orientations. We then use Markov Chain Monte Carlo to fit K2 data of HIP 41378 b, d, and f, specifically examining HIP 41378 f for possible oblateness and obliquity. We find that the flattening of HIP 41378 f is $f \leq 0.18$ at the 95% confidence level, consistent with a rotation period of $P_\text{rot} \geq 22.2$ hr. In the future, high-precision data from JWST has the potential to tighten such a constraint and can differentiate between spherical and flattened planets., Comment: 14 pages, 10 figures, 2 tables. Submitted to ApJL. Code available at https://github.com/emprice/greenlantern

The existence of high-$\alpha$ stars with inferred ages < 6 Gyr has been confirmed recently with large spectroscopic and photometric surveys. However, stellar mergers or binary interactions can induce properties associated with young ages, such as high mass, rapid rotation, or high activity, even in old populations. Literature studies have confirmed that at least some of these apparently young stars are old merger products. However, none have ruled out the possibility of genuinely young high-$\alpha$ stars. Because cool GKM dwarfs spin down, rapid rotation can be used to indicate youth. In this paper, we provide strong evidence that truly young high-$\alpha$ stars exist by studying high-$\alpha$ rotators in the Kepler and K2 field with abundance measurements from GALAH and APOGEE. After excluding close binaries using radial velocity (RV) measurements from Gaia DR3 and multi-epoch RVs from APOGEE, we find a total of 70 high-$\alpha$ rapid rotators with periods ~10-30 days, 29 of which have lithium measurements from GALAH, indicating that they have not gone through past mass transfer or stellar merger events. We identify 10 young high-$\alpha$ candidates with no signs of merger-induced mixing or close companions. One clear example is a G dwarf with a measurable rotation and an age of 1.98$^{+0.12}_{-0.28}$ Gyr that is likely a single star with multiple RV measurements from APOGEE, has significant lithium detection from GALAH (A(Li) = 1.79), and has no signs of planet engulfment., Comment: 21 pages, 13 figures, submitted to AJ

The infall of the LMC into the Milky Way (MW) has dynamical implications throughout the MW's dark matter halo. We study the impact of this merger on the statistical properties of populations of simulated stellar streams. Specifically, we investigate the radial and on-sky angular dependence of stream perturbations caused by the direct effect of stream--LMC interactions and/or the response of the MW dark matter halo. We use a time-evolving MW--LMC simulation described by basis function expansions to simulate streams. We quantify the degree of perturbation using a set of stream property statistics including the misalignment of proper motions with the stream track. In the outer halo, direct stream--LMC interactions produce a statistically significant effect, boosting the fraction of misaligned proper motions by ~25% relative to the model with no LMC. Moreover, there is on-sky angular dependence of stream perturbations: the highest fractions of perturbed streams coincide with the same on-sky quadrant as the present-day LMC location. In the inner halo, the MW halo dipole response primarily drives stream perturbations, but it remains uncertain whether this is a detectable signature distinct from the LMC's influence. For the fiducial MW--LMC model, we find agreement between the predicted fraction of streams with significantly misaligned proper motions, $\bar{\vartheta}>10^{\circ}$, and Dark Energy Survey data. Finally, we predict this fraction for the Rubin Observatory Legacy Survey of Space and Time (LSST) footprint. Using LSST data will improve our constraints on dark matter models and LMC properties as it is sensitive to both., Comment: ApJ accepted. 24 pages, 9 figures, 2 Tables

The tables in this report provide national-level estimates of the extent to which students ages 12-18 enrolled in grades 6-12 experience bullying during school. The tables show how bullying victimization varies by student and school characteristics such as sex, race/ethnicity, grade, household income, region, school locale, school enrollment size, and the percentage of students eligible for free or reduced-price lunch. The tables also show how rates of bullying victimization vary by crime-related characteristics such as: the presence of gangs, guns, drugs, alcohol, and hate-related graffiti at school; selected school security measures; student criminal victimization; personal fear of attack or harm; avoidance behaviors; fighting; and the carrying of weapons to school.