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Reclassification, the process by which English learner (EL) students exit EL classification, often determines ELs' access to mainstream academic coursework. While existing research finds that many students who demonstrate English proficiency do not reclassify, few studies evaluate policies that effectively reclassify eligible students. This study examines the impact of shifting reclassification responsibility from school districts to the state in Michigan. Using a difference-in-regression discontinuities design, we find that state-level responsibility increases reclassification rates by 35 percentage points compared to district-level responsibility. The effects are larger for Spanish speakers, indicating that state procedures may reduce linguistic bias. Our findings contribute to the literature on default policies in K-12 education and provide evidence on policies that promote equity in EL education.

This paper explores opportunities and challenges for data-driven advocacy to support home care workers, an often overlooked group of low-wage, frontline health workers. First, we investigate what data to collect and how to collect it in ways that preserve privacy and avoid burdening workers. Second, we examine how workers and advocates could use collected data to strengthen individual and collective advocacy efforts. Our qualitative study with 11 workers and 15 advocates highlights tensions between workers' desires for individual and immediate benefits and advocates' preferences to prioritize more collective and long-term benefits. We also uncover discrepancies between participants' expectations for how data might transform advocacy and their on-the-ground experiences collecting and using real data. Finally, we discuss future directions for data-driven worker advocacy, including combining different kinds of data to ameliorate challenges, leveraging advocates as data stewards, and accounting for workers' and organizations' heterogeneous goals., Comment: Accepted to CHI 2025

Vision language models have achieved impressive results across various fields. However, adoption in remote sensing remains limited, largely due to the scarcity of paired image-text data. To bridge this gap, synthetic caption generation has gained interest, traditionally relying on rule-based methods that use metadata or bounding boxes. While these approaches provide some description, they often lack the depth needed to capture complex wide-area scenes. Large language models (LLMs) offer a promising alternative for generating more descriptive captions, yet they can produce generic outputs and are prone to hallucination. In this paper, we propose a new method to enhance vision-language datasets for remote sensing by integrating maps as external data sources, enabling the generation of detailed, context-rich captions. Additionally, we present methods to measure and mitigate hallucinations in LLM-generated text. We introduce fMoW-mm, a multimodal dataset incorporating satellite imagery, maps, metadata, and text annotations. We demonstrate its effectiveness for automatic target recognition in few-shot settings, achieving superior performance compared to other vision-language remote sensing datasets.

We study the dynamics of a qubit system interacting with thermalized bath-ancilla spins via a repeated interaction scheme. Considering generic initial conditions for the system and employing a Heisenberg-type interaction between the system and the ancillas, we analytically prove the following: (i) The population and coherences of the system qubit evolve independently toward a nonequilibrium steady-state solution, which is diagonal in the qubit's energy eigenbasis. The population relaxes to this state geometrically, whereas the coherences decay through a more compound behavior. (ii) In the long time limit, the system approaches a steady state that generally differs from the thermal state of the ancilla. We derive this steady-state solution and show its dependence on the interaction parameters and collision frequency. (iii) We bound the number of interaction steps required to achieve the steady state within a specified error tolerance, and we evaluate the energetic cost associated with the process. Our key finding is that deterministic system-ancilla interactions do not typically result in the system thermalizing to the thermal state of the ancilla. Instead, they generate a distinct nonequilibrium steady state, which we explicitly derive. However, we also identify an operational regime that leads to thermalization with a few long and possibly randomized collisions.

We make an application of ideas from partition theory to a problem in multiplicative number theory. We propose a deterministic model of prime number distribution, from first principles related to properties of integer partitions, that predicts the prime number theorem and suggests new conjectures regarding predictable variations in prime gaps. The model posits that, for $n\geq 2$, $$p_{n}\ =\ 1\ +\ 2\sum_{j=1}^{n-1}\left\lceil \frac{d(j)}{2}\right\rceil\ +\ \varepsilon(n),$$ where $p_k$ is the $k$th prime number, $d(k)$ is the divisor function, and $\varepsilon(k)\geq 0$ is an error that is negligible asymptotically; both the main term and error are enumerative functions in our conceptual model. We refine the error to give numerical estimates of $\pi(n)$ similar to those provided by the logarithmic integral, and much more accurate than $\operatorname{li}(n)$ up to $n=10{,}000$ where the estimate is almost exact. We also test a seemingly dubious prediction suggested by the model, that prime-indexed primes $p_2, p_3, p_5, p_7,$ etc., are more likely to be involved in twin prime pairs than are arbitrary primes; surprisingly, computations show prime-indexed primes are more likely to be twin primes up to at least $n=10{,}000{,}000{,}000$, with the bias diminishing but persistent as $n$ increases., Comment: 15 pages, 2 figures, 2 tables, submitted for publication

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

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

Matched-filtering searches for gravitational-wave signals from compact binary mergers employ template banks which are a collection of modeled waveforms described by unique intrinsic parameters. We present two banks designed for low-latency and archive sub-solar mass (SSM) searches in data from the fourth observing run of LIGO-Virgo-KAGRA, and demonstrate the efficacy of the banks via simulated signals. Further, we introduce a set of modifications to the geometric, manifold algorithm that allow the method to work for exceedingly low component masses necessary for SSM bank production. The archive search bank contains a total of $3,452,006$ templates, and covers a mass parameter space of $0.2$ to $10\ M_\odot$ in the larger component and $0.2$ to $1.0\ M_\odot$ in the smaller component, the spin parameter space of $-0.9$ to $0.9$ for masses above $0.5$ $M_\odot$ and $-0.1$ to $0.1$ for masses below $0.5$ $M_\odot$, and the mass ratio parameter space of $1$ to $10$. The PSD used was from a week of the first half of the fourth observing run of Advanced LIGO, Advanced Virgo, and KAGRA, and the low frequency cutoff was set to $45$ Hz with a maximum waveform duration of $128$ seconds. The bank simulations performed using SBank have shown that the banks presented in this paper have sufficient efficacy for use in their respective searches.

We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly performant multilingual models that match or surpass the capabilities of monolingual models. By leveraging several years of research at Cohere For AI and Cohere, including advancements in data arbitrage, multilingual preference training, and model merging, Aya Expanse sets a new state-of-the-art in multilingual performance. Our evaluations on the Arena-Hard-Auto dataset, translated into 23 languages, demonstrate that Aya Expanse 8B and 32B outperform leading open-weight models in their respective parameter classes, including Gemma 2, Qwen 2.5, and Llama 3.1, achieving up to a 76.6% win-rate. Notably, Aya Expanse 32B outperforms Llama 3.1 70B, a model with twice as many parameters, achieving a 54.0% win-rate. In this short technical report, we present extended evaluation results for the Aya Expanse model family and release their open-weights, together with a new multilingual evaluation dataset m-ArenaHard.

Cultural biases in multilingual datasets pose significant challenges for their effectiveness as global benchmarks. These biases stem not only from language but also from the cultural knowledge required to interpret questions, reducing the practical utility of translated datasets like MMLU. Furthermore, translation often introduces artifacts that can distort the meaning or clarity of questions in the target language. A common practice in multilingual evaluation is to rely on machine-translated evaluation sets, but simply translating a dataset is insufficient to address these challenges. In this work, we trace the impact of both of these issues on multilingual evaluations and ensuing model performances. Our large-scale evaluation of state-of-the-art open and proprietary models illustrates that progress on MMLU depends heavily on learning Western-centric concepts, with 28% of all questions requiring culturally sensitive knowledge. Moreover, for questions requiring geographic knowledge, an astounding 84.9% focus on either North American or European regions. Rankings of model evaluations change depending on whether they are evaluated on the full portion or the subset of questions annotated as culturally sensitive, showing the distortion to model rankings when blindly relying on translated MMLU. We release Global-MMLU, an improved MMLU with evaluation coverage across 42 languages -- with improved overall quality by engaging with compensated professional and community annotators to verify translation quality while also rigorously evaluating cultural biases present in the original dataset. This comprehensive Global-MMLU set also includes designated subsets labeled as culturally sensitive and culturally agnostic to allow for more holistic, complete evaluation.

Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets, where the labels of neighboring nodes are likely to be the same. In recent years, an increasing body of work has been devoted to the development of GNN architectures for heterophilic datasets, where labels do not exhibit this low-pass behavior. In this work, we create a new graph in which nodes are connected if they share structural characteristics, meaning a higher chance of sharing their labels, and then use this new graph in the GNN architecture. To do this, we compute the k-nearest neighbors graph according to distances between structural features, which are either (i) role-based, such as degree, or (ii) global, such as centrality measures. Experiments show that the labels are smoother in this newly defined graph and that the performance of GNN architectures improves when using this alternative structure., Comment: Presented as a conference paper in the Asilomar Conference on Signals, Systems, and Computers 2024

Asteroseismic masses of globular cluster (GC) stars are invaluable to investigate stellar evolution. Previously, only two GCs have been seismically studied. We present new detections of solar-like oscillations in the clusters M9 and M19, focusing on two key areas: stellar mass loss and GC multiple populations. Using K2 photometry, we detect solar-like oscillations in stars on the red giant branch and early asymptotic giant branch. We measure an integrated mass-loss for M9 of $0.16\pm0.02$(rand)$\pm0.03$(sys)$M_{\odot}$ and M19 of $0.33\pm0.03$(rand)$^{+0.09}_{-0.07}$(sys)$M_{\odot}$. Comparing these to the mass-loss estimates from previous seismically studied clusters, we derive a preliminary relationship between stellar mass-loss and metallicity for Type I GCs. We find that the mass-loss for M19 -- a Type II GC -- is significantly larger, suggesting Type II clusters follow a different mass-loss-metallicity trend. We also examine the mass distributions in each evolutionary phase for evidence of a bimodality that could indicate mass differences between sub-populations. While no clear bimodality is observed, there is tentative evidence suggesting the presence of two mass populations. Classification through spectroscopic abundances into the sub-populations is needed to verify these findings. This study reinforces that asteroseismology of GC stars provides an excellent testbed for studying stellar evolution. However, to advance the field we need high-quality photometry of more GCs, a goal that could be realised with the upcoming Roman Telescope., Comment: 18 pages, 15 figures

The EdResearch for Action "Overview Series" summarizes the research on key topics to provide K-12 education decision makers and advocates with an evidence base to ground discussions about how to best serve students. This research brief breaks down what is known about multilingual students classified as English Learners (ML-ELs), how ML-ELs perform in K-12 education, and what challenges they face. Key insights provided include: (1) research-based practices--such as bilingual program models--district and school leaders can use to support the academic success and linguistic development of ML-ELs; and (2) one-size-fits-all practices to avoid that can limit many students' opportunities to engage with rigorous content. [This brief was produced in collaboration with the University of Texas at Austin, College of Education.]

Elementary and secondary district leaders seeking to recruit teachers and diversify their teaching workforce will find useful, research-based strategies in this brief from the Region 4 Comprehensive Center. The brief focuses on ways to develop work-based learning programs in secondary schools that engage students in considering teacher careers. Drawing on insights from 14 programs, this brief summarizes five useful strategies of teacher-focused work-based learning programs, including targeted courses, cohort model and peer supports, strategic counseling, dual enrollment, and field experiences. It also provides helpful background into the historic challenges around recruiting and retaining teachers of color.

Predicting team dynamics from personality traits remains a fundamental challenge for the psychological sciences and team-based organizations. Understanding how team composition generates team processes can significantly advance team-based research along with providing practical guidelines for team staffing and training. Although the Input-Process-Output (IPO) model has been useful for studying these connections, the complex nature of team member interactions demands a more dynamic approach. We develop a computational model of conversational turn-taking within self-organized teams that can provide insight into the relationships between team member personality traits and team communication dynamics. We focus on turn-taking patterns between team members, independent of content, which can significantly influence team emergent states and outcomes while being objectively measurable and quantifiable. As our model is trained on conversational data from teams of given trait compositions, it can learn the relationships between individual traits and speaking behaviors and predict group-wide patterns of communication based on team trait composition alone. We first evaluate the performance of our model using simulated data and then apply it to real-world data collected from self-organized student teams. In comparison to baselines, our model is more accurate at predicting speaking turn sequences and can reveal new relationships between team member traits and their communication patterns. Our approach offers a more data-driven and dynamic understanding of team processes. By bridging the gap between individual personality traits and team communication patterns, our model has the potential to inform theories of team processes and provide powerful insights into optimizing team staffing and training., Comment: 64 pages, 9 figures

Environmental and individualistic variables affect the rate of human decomposition in complex ways. These effects complicate the estimation of the postmortem interval (PMI) based on observed decomposition characteristics. In this work, we develop a generative probabilistic model for decomposing human remains based on PMI and a wide range of environmental and individualistic variables. This model explicitly represents the effect of each variable, including PMI, on the appearance of each decomposition characteristic, allowing for direct interpretation of model effects and enabling the use of the model for PMI inference and optimal experimental design. In addition, the probabilistic nature of the model allows for the integration of expert knowledge in the form of prior distributions. We fit this model to a diverse set of 2,529 cases from the GeoFOR dataset. We demonstrate that the model accurately predicts 24 decomposition characteristics with an ROC AUC score of 0.85. Using Bayesian inference techniques, we invert the decomposition model to predict PMI as a function of the observed decomposition characteristics and environmental and individualistic variables, producing an R-squared measure of 71%. Finally, we demonstrate how to use the fitted model to design future experiments that maximize the expected amount of new information about the mechanisms of decomposition using the Expected Information Gain formalism.

The Activated Random Walk (ARW) model is a promising candidate for demonstrating self-organized criticality due to its potential for universality. Recent studies have shown that the ARW model exhibits a well-defined critical density in one dimension, supporting its universality. In this paper, we extend these results by demonstrating that the ARW model on $\mathbb{Z}$, with a single initially active particle and all other particles sleeping, maintains the same critical density. Our findings relax the previous assumption that required all particles to be initially active. This provides further evidence of the ARW model's robustness and universality in depicting self-organized criticality.

A key objective of interpretability research on large language models (LLMs) is to develop methods for robustly steering models toward desired behaviors. To this end, two distinct approaches to interpretability -- ``bottom-up" and ``top-down" -- have been presented, but there has been little quantitative comparison between them. We present a case study comparing the effectiveness of representative vector steering methods from each branch: function vectors (FV; arXiv:2310.15213), as a bottom-up method, and in-context vectors (ICV; arXiv:2311.06668) as a top-down method. While both aim to capture compact representations of broad in-context learning tasks, we find they are effective only on specific types of tasks: ICVs outperform FVs in behavioral shifting, whereas FVs excel in tasks requiring more precision. We discuss the implications for future evaluations of steering methods and for further research into top-down and bottom-up steering given these findings.

Galaxy mergers are a key driver of galaxy formation and evolution, including the triggering of AGN and star formation to a still unknown degree. We thus investigate the impact of galaxy mergers on star formation and AGN activity using a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields (CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, and MACS-0416), collectively covering an unmasked area of 189 arcmin$^2$. We focuses on star formation rate (SFR) enhancement, AGN fraction, and AGN excess in major merger ($\mu > 1/4$) close-pair samples, defined by $\Delta z < 0.3$ and projected separations $r_p < 100$ kpc, compared to non-merger samples. We find that SFR enhancement occurs only at $r_p < 20$ kpc, with values of $0.25 \pm 0.10$ dex and $0.26 \pm 0.11$ dex above the non-merger medians for $z = [4.5, 6.5]$ and $z = [6.5, 8.5]$, respectively. No other statistically significant enhancements in galaxy sSFR or stellar mass are observed at any projected separation or redshift bin. We also compare our observational results with predictions from the SC-SAM simulation and find no evidence of star formation enhancement in the simulations at any separation range. Finally, we examine the AGN fraction and AGN excess, finding that the fraction of AGNs in AGN-galaxy pairs, relative to the total AGN population, is $3.25^{+1.50}_{-1.06}$ times greater than the fraction of galaxy pairs relative to the overall galaxy population at the same redshift. We find that nearly all AGNs have a companion within 100 kpc and observe an excess AGN fraction in close-pair samples compared to non-merger samples. This excess is found to be $1.26 \pm 0.06$ and $1.34 \pm 0.06$ for AGNs identified via the inferred BPT diagram and photometric SED selection, respectively., Comment: 17 Pages, 7 Figures, Submitted to MNRAS

This work presents a low-rank tensor model for multi-dimensional Markov chains. A common approach to simplify the dynamical behavior of a Markov chain is to impose low-rankness on the transition probability matrix. Inspired by the success of these matrix techniques, we present low-rank tensors for representing transition probabilities on multi-dimensional state spaces. Through tensor decomposition, we provide a connection between our method and classical probabilistic models. Moreover, our proposed model yields a parsimonious representation with fewer parameters than matrix-based approaches. Unlike these methods, which impose low-rankness uniformly across all states, our tensor method accounts for the multi-dimensionality of the state space. We also propose an optimization-based approach to estimate a Markov model as a low-rank tensor. Our optimization problem can be solved by the alternating direction method of multipliers (ADMM), which enjoys convergence to a stationary solution. We empirically demonstrate that our tensor model estimates Markov chains more efficiently than conventional techniques, requiring both fewer samples and parameters. We perform numerical simulations for both a synthetic low-rank Markov chain and a real-world example with New York City taxi data, showcasing the advantages of multi-dimensionality for modeling state spaces.

Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally developed system for prostate cancer detection, grading, and workflow optimization and to contrast this with commercial alternatives. From August 2021 to March 2023, we scanned 21,396 slides from 1,147 patients with positive biopsies. We developed models for cancer detection, grading, and screening of equivocal cases for IHC ordering. We compared a task-specific model trained using the PANDA dataset of prostate cancer biopsies with one built using features extracted by the general-purpose histology foundation model, UNI and compare their performance in an unfiltered prospectively collected dataset that reflects our patient population (1737 slides,95 patients). We evaluated the contributions of a bespoke model designed to improve sensitivity in detecting small cancer foci and scoring of broader patterns observed at lower resolution. We found high concordance between the developed systems and pathologist reference in detection (AUC 98.5, sensitivity 95.0, and specificity 97.8), ISUP grading (quadratic Cohen's kappa 0.869), grade group 3 or higher (AUC 97.5, sensitivity 94.9, specificity 96.6) and comparable to published data from commercial systems. Screening could reduce IHC ordering for equivocal cases by 44.5% with an overall error rate of 1.8% (1.4% false positive, 0.4% false negative rates). Institutions like academic medical centers that have high scanning volumes and report abstraction capabilities can develop accurate computational pathology models for internal use. These models have the potential to aid in quality control role and to improve workflow in the pathology lab to help meet future challenges in prostate cancer diagnosis.

The ghost algebra is a two-boundary extension of the Temperley-Lieb algebra, constructed recently via a diagrammatic presentation. The existing two-boundary Temperley-Lieb algebra has a basis of two-boundary string diagrams, where the number of strings connected to each boundary must be even. The ghost algebra is similar, but allows this number to be odd, using bookkeeping dots called ghosts to assign a consistent parity to each string endpoint on each boundary. Equivalently, one can discard the ghosts and label each string endpoint with its parity; the resulting algebra is readily generalised to allow any number of possible labels, instead of just odd or even. We call the generalisation the label algebra, and establish a non-diagrammatic presentation for it. A similar presentation for the ghost algebra follows from this., Comment: 56 pages

A variety of neural networks architectures are being studied to tackle blur in images and videos caused by a non-steady camera and objects being captured. In this paper, we present an overview of these existing networks and perform experiments to remove the blur caused by atmospheric turbulence. Our experiments aim to examine the reusability of existing networks and identify desirable aspects of the architecture in a system that is geared specifically towards atmospheric turbulence mitigation. We compare five different architectures, including a network trained in an end-to-end fashion, thereby removing the need for a stabilization step.

Observational constraints on classical novae are heavily biased to phases near optical peak and later because of the simple fact that novae are not typically discovered until they become bright. The earliest phases of brightening, coming before discovery, are typically missed, but this is changing with the proliferation of wide-field optical monitoring systems including ZTF, ASAS-SN, and Evryscope. Here, we report on unprecedented observations of the fast nova V1674 Her beginning >10 mag below its optical peak and including high-cadence (2 min.) observations that chart a rise of ~8 mag in just 5 hours. Two clear breaks are identified as the light curve transitions first from rising slowly to rising rapidly, followed by a transition to an even faster, nearly linear rate of increasing flux with time. The depths of the observations allow us to place tight constraints on the size of the photosphere under the assumption of blackbody emission from a white dwarf emitting at its Eddington luminosity. We find that the white dwarf was unlikely to have overflowed its Roche lobe prior to the launch of a fast wind, which poses a challenge for explaining the Fermi $\gamma$-ray detections as the interaction of a fast wind with a slow-torus of gas stripped from the inflated white dwarf envelope by the companion. High-cadence observations of novae from Evryscope and the planned Argus Array can record the diversity of rising nova light curves and help resolve how the interplay between thermonuclear fusion, binary interaction, and shocks power their earliest light., Comment: 20 pages, 6 figures, 2 tables, accepted for publication in ApJ

The James Webb Space Telescope is revolutionising our ability to understand the host galaxies and local environments of high-z quasars. Here we obtain a comprehensive understanding of the host galaxy of the z=7.08 quasar J1120+0641 by combining NIRSpec integral field spectroscopy with NIRCam photometry of the host continuum emission. Our emission line maps reveal that this quasar host is undergoing a merger with a bright companion galaxy. The quasar host and the companion have similar dynamical masses of $\sim10^{10}M_\odot$, suggesting that this is a major galaxy interaction. Through detailed quasar subtraction and SED fitting using the NIRCam data, we obtain an estimate of the host stellar mass of $M_{\ast}\simeq2.6\times10^9M_\odot$, with $M_{*}\simeq5.0\times10^9M_\odot$ for the companion galaxy. Using the H$\beta$ Balmer line we estimate a virial black hole mass of $M_{\rm{BH}}\simeq1.4\times10^9 M_\odot$. Thus, J1120+0641 has an extreme black hole - stellar mass ratio of $M_{\rm{BH}}/M_\ast\simeq0.54$, which is ~3 dex larger than expected by the local scaling relations between black hole and stellar mass. J1120+0641 is powered by an overmassive black hole with the highest reported black hole - stellar mass ratio, in a quasar host that is currently undergoing a major merger -- these new insights highlight the power of JWST for measuring and understanding these extreme first quasars., Comment: Submitted to A&A, comments welcome

Cooperative optical effects are enabled and controlled by interactions between molecular dipoles, meaning that their mutual orientation is of paramount importance to, for example, superabsorbing light-harvesting antennas. Here we show how to move beyond the possibilities of simple geometric tailoring, demonstrating how a metallic sphere placed within a ring of parallel dipoles engineers an effective Hamiltonian that generates "guide-sliding" states within the ring system. This allows steady-state superabsorption in noisy room temperature environments, outperforming previous designs while being significantly simpler to implement. As exemplified by this showcase, our approach represents a powerful design paradigm for tailoring cooperative light-matter effects in molecular structures that extends beyond superabsorbing systems, to a huge array of quantum energy transport systems.

In the realm of dermatological diagnoses, where the analysis of dermatoscopic and microscopic skin lesion images is pivotal for the accurate and early detection of various medical conditions, the costs associated with creating diverse and high-quality annotated datasets have hampered the accuracy and generalizability of machine learning models. We propose an innovative unsupervised augmentation solution that harnesses Generative Adversarial Network (GAN) based models and associated techniques over their latent space to generate controlled semiautomatically-discovered semantic variations in dermatoscopic images. We created synthetic images to incorporate the semantic variations and augmented the training data with these images. With this approach, we were able to increase the performance of machine learning models and set a new benchmark amongst non-ensemble based models in skin lesion classification on the HAM10000 dataset; and used the observed analytics and generated models for detailed studies on model explainability, affirming the effectiveness of our solution., Comment: This preprint has been submitted to the Workshop on Synthetic Data for Computer Vision (SyntheticData4CV 2024 is a side event on 18th European Conference on Computer Vision 2024). This preprint has not undergone peer review or any post-submission improvements or corrections

Using grid-based hydrodynamics simulations and analytic modeling, we compute the electromagnetic (EM) signatures of gravitational wave (GW) driven inspirals of massive black hole binaries that accrete gas from circumbinary disks, exploring the effects of varying gas temperatures, viscosity laws, and binary mass ratios. Our main finding is that active galactic nuclei (AGN's) that host inspiraling binaries can exhibit two sub-types of long-term secular variability patterns: Type-A events which dim before merger and brighten afterward, and Type-B events which brighten before merger and dim afterward. In both types the merger coincides with a long-lasting chromatic change of the AGN appearance. The sub-types correspond to the direction of angular momentum transfer between the binary and the disk, and could thus have correlated GW signatures if the gas-induced torque can be inferred from GW phase drift measurements by LISA. The long-term brightness trends are caused by steady weakening of the disk-binary torque that accompanies orbital decay, it induces a hysteresis effect whereby the disk "remembers" the history of the binary's contraction. We illustrate the effect using a reduced model problem of an axisymmetric thin disk subjected at its inner edge to the weakening torque of an inspiraling binary. The model problem yields a new class of self-similar disk solutions, which capture salient features of the multi-dimensional hydrodynamics simulations. We use these solutions to derive variable AGN disk emission signatures within years to decades of massive black hole binary mergers in AGN's. Spectral changes of Mrk 1018 might have been triggered by an inspiral-merger event., Comment: Submitted to MNRAS

$\Lambda$CDM cosmology predicts the hierarchical formation of galaxies which build up mass by merger events and accreting smaller systems. The stellar halo of the Milky Way has proven to be useful a tool for tracing this accretion history. However, most of this work has focused on the outer halo where dynamical times are large and the dynamical properties of accreted systems are preserved. In this work, we investigate the inner galaxy regime, where dynamical times are relatively small and systems are generally completely phase-mixed. Using the FIRE-2 and Auriga cosmological zoom-in simulation suites of Milky Way-mass galaxies, we find the stellar density profiles along the minor axis (perpendicular to the galactic disk) within the NFW scale radii (R$\approx$15 kpc) are best described as an exponential disk with scale height <0.3 kpc and a power law component with slope $\alpha\approx$-4. The stellar density amplitude and slope for the power law component is not significantly correlated with metrics of the galaxy's accretion history. Instead, we find the stellar profiles strongly correlate with the dark matter profile. Across simulation suites, the galaxies studied in this work have a stellar to dark matter mass ratio that decreases as $1/r^2$ along the minor axis., Comment: 29 pages, 13 figures, 1 table, Submitted to ApJ, comments welcome!

Scientific data governance should prioritize maximizing the utility of data throughout the research lifecycle. Research software systems that enable analysis reproducibility inform data governance policies and assist administrators in setting clear guidelines for data reuse, data retention, and the management of scientific computing needs. Proactive analysis reproducibility and data governance are integral and interconnected components of research lifecycle management.

How can one assign roommates and rooms when tenants have preferences for both where and with whom they live? In this setting, the usual notions of envy-freeness and maximizing social welfare may not hold; the roommate rent-division problem is assumed to be NP-hard, and even when welfare is maximized, an envy-free price vector may not exist. We first construct a novel greedy algorithm with bipartite matching before exploiting the connection between social welfare maximization and the maximum weighted independent set (MWIS) problem to construct a polynomial-time algorithm that gives a $\frac{3}{4}+\varepsilon$-approximation of maximum social welfare. Further, we present an integer program to find a room envy-free price vector that minimizes envy between any two tenants. We show empirically that a MWIS algorithm returns the optimal allocation in polynomial time and conjecture that this problem, at the forefront of computer science research, may have an exact polynomial algorithm solution. This study not only advances the theoretical framework for roommate rent division but also offers practical algorithmic solutions that can be implemented in real-world applications.

James Webb Space Telescope opens a new window to directly probe luminous quasars powered by billion solar mass black holes in the epoch of reionization and their co-evolution with massive galaxies with unprecedented details. In this paper, we report the first results from the deep NIRSpec integral field spectroscopy study of a quasar at $z = 7.5$. We obtain a bolometric luminosity of $\sim$$1.8\times10^{47}$ erg s$^{-1}$ and a black hole mass of $\sim$0.7--2.5$\times10^{9}$ M$_{\odot}$ based on H$\beta$ emission line from the quasar spectrum. We discover $\sim$2 kpc scale, highly blueshifted ($\sim$$-$870 km/s) and broad ($\sim$1400 km/s) [O III] line emission after the quasar PSF has been subtracted. Such line emission most likely originates from a fast, quasar-driven outflow, the earliest one on galactic-scale known so far. The dynamical properties of this outflow fall within the typical ranges of quasar-driven outflows at lower redshift, and the outflow may be fast enough to reach the circumgalactic medium. Combining both the extended and nuclear outflow together, the mass outflow rate, $\sim$300 M$_{\odot}$yr, is $\sim$60%--380% of the star formation rate of the quasar host galaxy, suggesting that the outflow may expel a significant amount of gas from the inner region of the galaxy. The kinetic energy outflow rate, $\sim$3.6$\times10^{44}$ erg s$^{-1}$, is $\sim$0.2% of the quasar bolometric luminosity, which is comparable to the minimum value required for negative feedback based on simulation predictions. The dynamical timescale of the extended outflow is $\sim$1.7 Myr, consistent with the typical quasar lifetime in this era.

Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneously but also that all nodes can be observed. However, in many real-world scenarios, data can neither be known completely nor obtained all at once. We present a novel method for online graph estimation that accounts for the presence of hidden nodes. We consider signals that are stationary on the underlying graph, which provides a model for the unknown connections to hidden nodes. We then formulate a convex optimization problem for graph learning from streaming, incomplete graph signals. We solve the proposed problem through an efficient proximal gradient algorithm that can run in real-time as data arrives sequentially. Additionally, we provide theoretical conditions under which our online algorithm is similar to batch-wise solutions. Through experimental results on synthetic and real-world data, we demonstrate the viability of our approach for online graph learning in the presence of missing observations.

Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is represented by a homophilic graph. However, recent works have revealed that many relevant applications involve heterophilic data where the performance of GNNs can be notably compromised. To address this challenge, we present a simple yet effective architecture designed to mitigate the limitations of the homophily assumption. The proposed architecture reinterprets the role of graph filters in convolutional GNNs, resulting in a more general architecture while incorporating a stronger inductive bias than GNNs based on filter banks. The proposed convolutional layer enhances the expressive capacity of the architecture enabling it to learn from both homophilic and heterophilic data and preventing the issue of oversmoothing. From a theoretical standpoint, we show that the proposed architecture is permutation equivariant. Finally, we show that the proposed GNNs compares favorably relative to several state-of-the-art baselines in both homophilic and heterophilic datasets, showcasing its promising potential.

Covariance-based data processing is widespread across signal processing and machine learning applications due to its ability to model data interconnectivities and dependencies. However, harmful biases in the data may become encoded in the sample covariance matrix and cause data-driven methods to treat different subpopulations unfairly. Existing works such as fair principal component analysis (PCA) mitigate these effects, but remain unstable in low sample regimes, which in turn may jeopardize the fairness goal. To address both biases and instability, we propose Fair coVariance Neural Networks (FVNNs), which perform graph convolutions on the covariance matrix for both fair and accurate predictions. Our FVNNs provide a flexible model compatible with several existing bias mitigation techniques. In particular, FVNNs allow for mitigating the bias in two ways: first, they operate on fair covariance estimates that remove biases from their principal components; second, they are trained in an end-to-end fashion via a fairness regularizer in the loss function so that the model parameters are tailored to solve the task directly in a fair manner. We prove that FVNNs are intrinsically fairer than analogous PCA approaches thanks to their stability in low sample regimes. We validate the robustness and fairness of our model on synthetic and real-world data, showcasing the flexibility of FVNNs along with the tradeoff between fair and accurate performance.

Globular clusters offer a powerful way to test the properties of stellar populations and the late stages of low-mass stellar evolution. In this paper we study oscillating giant stars and overtone RR Lyrae-type pulsators in the nearest globular cluster, M4, with the help of high-precision, continuous light curves collected by the Kepler space telescope in the K2 mission. We determine the frequency composition of five RRc stars and model their physical parameters with a grid of linear pulsation models. We are able, for the first time, to compare seismic masses of RR Lyrae stars directly to the masses of the very similar red horizontal branch stars in the same stellar population, independently determined from asteroseismic scaling relations. We find a close match, with an average seismic mass of $0.651\pm0.028\,M_\odot$ for RR Lyrae stars and $0.657\pm0.034\,M_\odot$ for red horizontal-branch stars. While the validity of our RR Lyrae masses still relies on the similarity of neighboring horizontal branch subgroups, this result strongly indicates that RRc stars may indeed exhibit high-degree, $l = 8$ and 9 non-radial modes, and modeling these modes can provide realistic mass estimates. We also determine the He content of the cluster to be $Y = 0.266\pm 0.008$, and compare the seismic masses for our sample of RR Lyrae to theoretical mass relations and highlight the limitations of these relations., Comment: 11 pages, 10 figures, submitted to A&A for review

In recent years, children's video viewing has shifted from television to online streaming platforms like YouTube. While such platforms include many educational videos for children, we know little about the educational value of the content in these videos. To understand the learning opportunities available to children when they watch videos online, this paper describes the quality, duration, and popularity of a sample of 1,198 YouTube videos focused on early literacy and math topics. We found that while most of these videos use concrete examples to illustrate learning content, only about half directly address the audience or involve characters, and very few integrate learning content into the narrative. Further, videos in our sample that directly addressed the audience were less popular, as measured by views and likes, as were videos that included a greater variety of math topics. We discuss important ways that user-generated educational videos may differ from educational television programs, including decreased reliance on narratives and characters as compared to educational television programs. Our findings also hint at novel influences on the content and quality of online videos, such as the likelihood that the algorithm will recommend a video or that a viewer will like it. Finally, we discuss implications for parents, content creators, and researchers.

CUNY Transfer Explorer (CUNY T-Rex) was developed to simplify the transfer of credits and represents an at-scale technology tool solution that provides transparent and up-to-date information about transfer credit equivalencies to students, faculty, and advisors. In addition to collaborating with CUNY to develop CUNY T-Rex, ITHAKA S+R undertook a mixed-methods evaluation to understand the process, conditions, and business requirements that were involved in the launch and proliferation of CUNY T-Rex, as well as to understand the behaviors, uses, and implementation of CUNY T-Rex at individual institutions and system wide. This first of two reports examines the use and implementation of CUNY T-Rex and seeks to answer the following questions: (1) How do students use CUNY T-Rex? How might they use it more effectively? (2) How do college faculty and staff use CUNY T-Rex to support students? How might they use it more effectively? and (3) How has CUNY T-Rex been implemented across CUNY colleges? Results provide insights into the use cases and experiences of both stakeholders (institutional and system-wide) and students using CUNY T-Rex and the perspectives of institutional stakeholders regarding implementation of the tool across the CUNY system. The report provides: (1) background on the development of CUNY T-Rex and its core functionalities; (2) context of transfer activity within the CUNY system; and (3) information about what can be gleaned from website analytics about who is using CUNY T-Rex and which features they most commonly access.