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The COVID-19 pandemic widened existing educational inequities for Black and Latino students, students who are English learners, and students with disabilities. Research shows that family engagement has a positive impact on student outcomes; therefore, forging connections between schools and families as enrollment rebounds will be especially crucial to enhancing student learning and well-being in the wake of the pandemic. Given that context, this report examines the perceptions and experiences of first and second grade parents and teachers with their school's engagement initiatives using a mixed-methods analysis of data from two nationally representative surveys, an online discussion board, and interviews with family engagement coordinators. Using existing research on meaningful and effective family engagement as a guide, the authors examined parents' and teachers' perceptions about several facets of effective engagement: communication; shared decision-making; connections, trust, and mutual respect; and cultural inclusiveness. Where applicable, the authors discuss how responses differed by race or ethnicity, household income, multilingual status, and whether the parent has a child who has been identified as having a disability.

The use of machine learning and AI on electronic health records (EHRs) holds substantial potential for clinical insight. However, this approach faces significant challenges due to data heterogeneity, sparsity, temporal misalignment, and limited labeled outcomes. In this context, we leverage a linked EHR dataset of approximately one million de-identified individuals from Bristol, North Somerset, and South Gloucestershire, UK, to characterize urinary tract infections (UTIs) and develop predictive models focused on data quality, fairness and transparency. A comprehensive data pre-processing and curation pipeline transforms the raw EHR data into a structured format suitable for AI modeling. Given the limited availability and biases of ground truth UTI outcomes, we introduce a UTI risk estimation framework informed by clinical expertise to estimate UTI risk across individual patient timelines. Using this framework, we built pairwise XGBoost models to differentiate UTI risk categories with explainable AI techniques to identify key predictors while ensuring interpretability. Our findings reveal differences in clinical and demographic factors across risk groups, offering insights into UTI risk stratification and progression. This study demonstrates the added value of AI-driven insights into UTI clinical decision-making while prioritizing interpretability, transparency, and fairness, underscoring the importance of sound data practices in advancing health outcomes.

Image registration techniques usually assume that the images to be registered are of a certain type (e.g. single- vs. multi-modal, 2D vs. 3D, rigid vs. deformable) and there lacks a general method that can work for data under all conditions. We propose a registration method that utilizes neural networks for image representation. Our method uses untrained networks with limited representation capacity as an implicit prior to guide for a good registration. Unlike previous approaches that are specialized for specific data types, our method handles both rigid and non-rigid, as well as single- and multi-modal registration, without requiring changes to the model or objective function. We have performed a comprehensive evaluation study using a variety of datasets and demonstrated promising performance.

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.

The Gemini South telescope is now equipped with a new high-resolution spectrograph called GHOST (the Gemini High-resolution Optical SpecTrograph). This instrument provides high-efficiency, high-resolution spectra covering 347-1060 nm in a single exposure of either one or two targets simultaneously, along with precision radial velocity spectroscopy utilizing an internal calibration source. It can operate at a spectral element resolving power of either 76000 or 56000, and can reach a SNR$\sim$5 in a 1hr exposure on a V$\sim$20.8 mag target in median site seeing, and dark skies (per resolution element). GHOST was installed on-site in June 2022, and we report performance after full integration to queue operations in November 2023, in addition to scientific results enabled by the integration observing runs. These results demonstrate the ability to observe a wide variety of bright and faint targets with high efficiency and precision. With GHOST, new avenues to explore high-resolution spectroscopy have opened up to the astronomical community. These are described, along with the planned and potential upgrades to the instrument., Comment: Accepted in the Astronomical Journal; 26 pages, 24 figures and 4 tables

Background: We extend recently proposed design-based capture-recapture methods for prevalence estimation among registry participants, in order to support causal inference among a trial-eligible target population. The proposed design for CRC analysis integrates an observational study cohort with a randomized trial involving a small representative study sample, and enhances the generalizability and transportability of the findings. Methods: We develop a novel CRC-type estimator derived via multinomial distribution-based maximum-likelihood that exploits the design to deliver benefits in terms of validity and efficiency for comparing the effects of two treatments on a binary outcome. Additionally, the design enables a direct standardization-type estimator for efficient estimation of general means (e.g., of biomarker levels) under a specific treatment, and for their comparison across treatments. For inference, we propose a tailored Bayesian credible interval approach to improve coverage properties in conjunction with the proposed CRC estimator for binary outcomes, along with a bootstrap percentile interval approach for use in the case of continuous outcomes. Results: Simulations demonstrate the proposed estimators derived from the CRC design. The multinomial-based maximum-likelihood estimator shows benefits in terms of validity and efficiency in treatment effect comparisons, while the direct standardization-type estimator allows comprehensive comparison of treatment effects within the target population. Conclusion: The extended CRC methods provide a useful framework for causal inference in a trial-eligible target population by integrating observational and randomized trial data. The novel estimators enhance the generalizability and transportability of findings, offering efficient and valid tools for treatment effect comparisons on both binary and continuous outcomes.

With high equilibrium temperatures and tidally locked rotation, ultra-hot Jupiters (UHJs) are unique laboratories within which to probe extreme atmospheric physics and chemistry. In this paper, we present high-resolution dayside spectroscopy of the UHJ WASP-189b obtained with the new Gemini High-resolution Optical SpecTrograph (GHOST) at the Gemini South Observatory. The observations, which cover three hours of post-eclipse orbital phases, were obtained during the instrument's System Verification run. We detect the planet's atmosphere via the Doppler cross-correlation technique, and recover a detection of neutral iron in the planet's dayside atmosphere at a significance of 7.5$\sigma$ in the red-arm of the data, verifying the presence of a thermal inversion. We also investigate the presence of other species in the atmosphere and discuss the implications of model injection/recovery tests. These results represent the first atmospheric characterization of an exoplanet with GHOST's high-resolution mode, and demonstrate the potential of this new instrument in detecting and studying ultra-hot exoplanet atmospheres., Comment: 21 pages, 12 figures, 3 tables. Accepted for publication in The Astronomical Journal

Most modern imaging systems process the data they capture algorithmically before-or instead of-human viewing. As a result, performance depends not on how interpretable the measurements appear, but how effectively they encode details for algorithmic processing. Information theory provides mathematical tools to analyze this, but developing methods that can handle the complexity of real-world measurements yet remain practical enough for widespread use has proven challenging. We introduce a data-driven approach for estimating the information content of imaging system measurements. Our framework requires only experimental measurements and noise characterization, with no need for ground truth data. We demonstrate that these information estimates reliably predict system performance across diverse imaging modalities, including color photography, radio astronomy, lensless imaging, and label-free microscopy. To automate the process of designing imaging systems that maximize information capture we introduce an optimization technique called Information-Driven Encoder Analysis Learning (IDEAL). The tools we develop in this work unlock information theory as a powerful, practical tool for analyzing and designing imaging systems across a broad range of applications. A video summarizing this work can be found at https://waller-lab.github.io/EncodingInformationWebsite/

The Gemini High-resolution Optical SpecTrograph (GHOST) is a new echelle spectrograph available on the Gemini-South telescope as of Semester 2024A. We present the first high resolution spectrum of the quasar J1449-1227 (redshift z_em=3.27) using data taken during the commissioning of GHOST. The observed quasar hosts an intervening iron-poor ([Fe/H] = -2.5) damped Lyman alpha (DLA) system at redshift z=2.904. Taking advantage of the high spectral resolving power of GHOST (R~55000), we are able to accurately model the metal absorption lines of the metal-poor DLA and find a supersolar [Si/Fe], suggesting the DLA gas is in an early stage of chemical enrichment. Using simple ionization models, we find that the large range in the C IV/Si IV column density ratio of individual components within the DLA's high ionization absorption profile can be reproduced by several metal-poor Lyman limit systems surrounding the low-ionization gas of the DLA. It is possible that this metal-poor DLA resides within a complex system of metal-poor galaxies or filaments with inflowing gas. The high spectral resolution, wavelength coverage and sensitivity of GHOST makes it an ideal spectrograph for characterizing the chemistry and kinematics of quasar absorption lines., Comment: Accepted for publication in MNRAS. 8 Pages, 5 figures

In this study, we develop an innovative data-driven optimization approach to solve the drone delivery service planning problem with online demand. Drone-based logistics are expected to improve operations by enhancing flexibility and reducing congestion effects induced by last-mile deliveries. With rising digitalization and urbanization, however, logistics service providers are constantly grappling with the challenge of uncertain real-time demand. This study investigates the problem of planning drone delivery service through an urban air traffic network to fulfil online and stochastic demand. Customer requests, if accepted, generate profit and are serviced by individual drone flights as per request origins, destinations and time windows. We cast this stochastic optimization problem as a Markov decision process. We present a novel data-driven optimization approach which generates predictive prescriptions of parameters of a surrogate optimization formulation. Our solution method consists of synthesizing training data via lookahead simulations to train a supervised machine learning model for predicting relative link priority based on the state of the network. This knowledge is then leveraged to selectively create weighted reserve capacity in the network and via a surrogate objective function that controls the trade-off between reserve capacity and profit maximization to maximize the cumulative profit earned. Using numerical experiments based on benchmarking transportation networks, the resulting data-driven optimization policy is shown to outperform a myopic policy. Sensitivity analyses on learning parameters reveal insights into the design of efficient policies for drone delivery service planning with online demand.

Event cameras, also known as dynamic vision sensors, are an emerging modality for measuring fast dynamics asynchronously. Event cameras capture changes of log-intensity over time as a stream of 'events' and generally cannot measure intensity itself; hence, they are only used for imaging dynamic scenes. However, fluctuations due to random photon arrival inevitably trigger noise events, even for static scenes. While previous efforts have been focused on filtering out these undesirable noise events to improve signal quality, we find that, in the photon-noise regime, these noise events are correlated with the static scene intensity. We analyze the noise event generation and model its relationship to illuminance. Based on this understanding, we propose a method, called Noise2Image, to leverage the illuminance-dependent noise characteristics to recover the static parts of a scene, which are otherwise invisible to event cameras. We experimentally collect a dataset of noise events on static scenes to train and validate Noise2Image. Our results provide a novel approach for capturing static scenes in event cameras, solely from noise events, without additional hardware.

Simulators based on neural networks offer a path to orders-of-magnitude faster electromagnetic wave simulations. Existing models, however, only address narrowly tailored classes of problems and only scale to systems of a few dozen degrees of freedom (DoFs). Here, we demonstrate a single, unified model capable of addressing scattering simulations with thousands of DoFs, of any wavelength, any illumination wavefront, and freeform materials, within broad configurable bounds. Based on an attentional multi-conditioning strategy, our method also allows non-recurrent supervision on and prediction of intermediate physical states, which provides improved generalization with no additional data-generation cost. Using this O(1)-time intermediate prediction capability, we propose and prove a rigorous, efficiently computable upper bound on prediction error, allowing accuracy guarantees at inference time for all predictions. After training solely on randomized systems, we demonstrate the unified model across a suite of challenging multi-disciplinary inverse problems, finding strong efficacy and speed improvements up to 96% for problems in optical tomography, beam shaping through volumetric random media, and freeform photonic inverse design, with no problem-specific training. Our findings demonstrate a path to universal, verifiably accurate neural surrogates for existing scattering simulators, and our conditioning and training methods are directly applicable to any PDE admitting a time-domain iterative solver.

Next-generation nano and quantum devices have increasingly complex 3D structure. As the dimensions of these devices shrink to the nanoscale, their performance is often governed by interface quality or precise chemical or dopant composition. Here we present the first phase-sensitive extreme ultraviolet imaging reflectometer. It combines the excellent phase stability of coherent high-harmonic sources, the unique chemical- and phase-sensitivity of extreme ultraviolet reflectometry, and state-of-the-art ptychography imaging algorithms. This tabletop microscope can non-destructively probe surface topography, layer thicknesses, and interface quality, as well as dopant concentrations and profiles. High-fidelity imaging was achieved by implementing variable-angle ptychographic imaging, by using total variation regularization to mitigate noise and artifacts in the reconstructed image, and by using a high-brightness, high-harmonic source with excellent intensity and wavefront stability. We validate our measurements through multiscale, multimodal imaging to show that this technique has unique advantages compared with other techniques based on electron and scanning-probe microscopies., Comment: 47 pages, 16 figures (4 in main text, 12 supplement) 2 tables

We consider the problem of estimating log-determinants of large, sparse, positive definite matrices. A key focus of our algorithm is to reduce computational cost, and it is based on sparse approximate inverses. The algorithm can be implemented to be adaptive, and it uses graph spline approximation to improve accuracy. We illustrate our approach on classes of large sparse matrices., Comment: 5 pages, python code included as an ancillary file

The performance of an imaging system is limited by optical aberrations, which cause blurriness in the resulting image. Digital correction techniques, such as deconvolution, have limited ability to correct the blur, since some spatial frequencies in the scene are not measured adequately (i.e., 'zeros' of the system transfer function). We prove that the addition of a random mask to an imaging system removes its dependence on aberrations, reducing the likelihood of zeros in the transfer function and consequently decreasing the sensitivity to noise during deconvolution. In simulation, we show that this strategy improves image quality over a range of aberration types, aberration strengths, and signal-to-noise ratios., Comment: The first two authors contributed equally

Computational microscopy, in which hardware and algorithms of an imaging system are jointly designed, shows promise for making imaging systems that cost less, perform more robustly, and collect new types of information. Often, the performance of computational imaging systems, especially those that incorporate machine learning, is sample-dependent. Thus, standardized datasets are an essential tool for comparing the performance of different approaches. Here, we introduce the Berkeley Single Cell Computational Microscopy (BSCCM) dataset, which contains over ~12,000,000 images of 400,000 of individual white blood cells. The dataset contains images captured with multiple illumination patterns on an LED array microscope and fluorescent measurements of the abundance of surface proteins that mark different cell types. We hope this dataset will provide a valuable resource for the development and testing of new algorithms in computational microscopy and computer vision with practical biomedical applications.

Monitoring small-area geographical population trends in opioid mortality has large scale implications to informing preventative resource allocation. A common approach to obtain small area estimates of opioid mortality is to use a standard disease mapping approach in which population-at-risk estimates are treated as fixed and known. Assuming fixed populations ignores the uncertainty surrounding small area population estimates, which may bias risk estimates and under-estimate their associated uncertainties. We present a Bayesian Spatial Berkson Error (BSBE) model to incorporate population-at-risk uncertainty within a disease mapping model. We compare the BSBE approach to the naive (treating denominators as fixed) using simulation studies to illustrate potential bias resulting from this assumption. We show the application of the BSBE model to obtain 2020 opioid mortality risk estimates for 159 counties in GA accounting for population-at-risk uncertainty. Utilizing our proposed approach will help to inform interventions in opioid related public health responses, policies, and resource allocation. Additionally, we provide a general framework to improve in the estimation and mapping of health indicators.

With technological advances, mobility has been moving from a product (i.e., traditional modes and vehicles), to a service (i.e., Mobility as a Service, MaaS). However, as observed in other fields (e.g. cloud computing resource management) we argue that mobility will evolve from a service to a resource (i.e., Mobility as a Resource, MaaR). Further, due to increasing scarcity of shared mobility spaces across traditional and emerging modes, the transition must be viewed within the critical need for ethical and equitable solutions for the traveling public (i.e., research is needed to avoid hyper-market driven outcomes for society). The evolution of mobility into a resource requires novel conceptual frameworks, technologies, processes and perspectives of analysis. A key component of the future MaaR system is the technological capacity to observe, allocate and manage (in real-time) the smallest envisionable units of mobility (i.e., atomic units of mobility capacity) while providing prioritized attention to human movement and ethical metrics related to access, consumption and impact. To facilitate research into the envisioned future system, this paper proposes initial frameworks which synthesize and advance methodologies relating to highly dynamic capacity reservation systems. Future research requires synthesis across transport network management, demand behavior, mixed-mode usage, and equitable mobility.

As algorithmic decision-making systems become more prevalent in society, ensuring the fairness of these systems is becoming increasingly important. Whilst there has been substantial research in building fair algorithmic decision-making systems, the majority of these methods require access to the training data, including personal characteristics, and are not transparent regarding which individuals are classified unfairly. In this paper, we propose a novel model-agnostic argumentation-based method to determine why an individual is classified differently in comparison to similar individuals. Our method uses a quantitative argumentation framework to represent attribute-value pairs of an individual and of those similar to them, and uses a well-known semantics to identify the attribute-value pairs in the individual contributing most to their different classification. We evaluate our method on two datasets commonly used in the fairness literature and illustrate its effectiveness in the identification of bias., Comment: 10 pages

We report on the chemo-dynamical analysis of SPLUS J142445.34-254247.1, an extremely metal-poor halo star enhanced in elements formed by the rapid neutron-capture process. This star was first selected as a metal-poor candidate from its narrow-band S-PLUS photometry and followed up spectroscopically in medium-resolution with Gemini South/GMOS, which confirmed its low-metallicity status. High-resolution spectroscopy was gathered with GHOST at Gemini South, allowing for the determination of chemical abundances for 36 elements, from carbon to thorium. At [Fe/H]=-3.39, SPLUS J1424-2542 is one of the lowest metallicity stars with measured Th and has the highest logeps(Th/Eu) observed to date, making it part of the "actinide-boost" category of r-process enhanced stars. The analysis presented here suggests that the gas cloud from which SPLUS J1424-2542 was formed must have been enriched by at least two progenitor populations. The light-element (Z<=30) abundance pattern is consistent with the yields from a supernova explosion of metal-free stars with 11.3-13.4 Msun, and the heavy-element (Z>=38) abundance pattern can be reproduced by the yields from a neutron star merger (1.66Msun and 1.27Msun) event. A kinematical analysis also reveals that SPLUS J1424-2542 is a low-mass, old halo star with a likely in-situ origin, not associated with any known early merger events in the Milky Way., Comment: 26 pages, 11 figures, accepted for publication on ApJ

In this work we generalise the adjoint method of inverse design to nonreciprocal media. As a test case, we use three-dimensional topology optimization via the level-set method to optimise one-way energy transfer for point-like source and observation points. To achieve this we introduce a suite of tools, chiefly what we term the `Faraday-adjoint' method which allows for efficient shape optimization in the presence of magneto-optical media. We carry out an optimization based on a very general equation that we derive for energy transfer in a nonreciprocal medium, and link finite-different time-domain numerics to analytics via a modified Born series generalised to a tensor permittivity. This work represents a stepping stone towards practical nanophotonic optical isolation, often regarded as the `holy grail' of integrated photonics.

Macroscopic link-based flow models are efficient for simulating flow propagation in urban road networks. Existing link-based flow models described traffic states of a link with two state variables of link inflow and outflow and assumed homogeneous traffic states within a whole link. Consequently, the turn-level queue length change within the link can not be captured, resulting in underrepresented queue spillback. Moreover, a constant link free-flow speed was assumed to formulate models, restricting their applicability in modeling phenomena involving time-varying free-flow speed. This study proposed a new link-based flow model by introducing an additional state variable of link queue inflow and adapting the link outflow to be free-flow speed-dependent. In our model, the vehicle propagation within each link is described by the link inflow, queue inflow, and outflow, which depends on the link free-flow speed changes. A node model is further defined to capture the presence of signal control and potential queue spillback, which estimates the constrained flow propagation between adjacent road segments. Simulation experiments were conducted on a single intersection and a network with consecutive intersections to verify the proposed model performance. Results demonstrate the predictive power of the proposed model in predicting traffic operations of intersections with multiple turning movements and time-varying free-flow speed. Our model outperforms the baseline link-based flow model and preserves the computational tractability property of link-based flow models.