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In the 2022-23 school year, Arizona began implementation of a "universal voucher" program through which all school-age students are eligible for a voucher, and families can use public funding to underwrite private or homeschool education for their children. Universal vouchers in Arizona are an expansion of the existing Empowerment Scholarship Account (ESA) program, which was targeted to students with special education needs or in specific circumstances. To better understand this program's impact on Arizona public schools, this report undertook a financial review of the expanded Arizona ESA program using publicly available data from the state budget, which details total state expenditures and student enrollment; Arizona Department of Education (ADE) quarterly reports to the State Board of Education, which detail the number of ESA applications received, approved, and denied; and ADE enrollment figures. The authors analyzed student enrollment in the program, the combined cost of the earlier enacted ESA program and the new universal voucher program, and their effects on education funding in the state. This report outlines the cost estimates and calculations in detail.

For over 25 years, the origin of long-duration gamma-ray bursts (lGRBs) has been linked to the collapse of rotating massive stars. However, we have yet to pinpoint the stellar progenitor powering these transients. Moreover, the dominant engine powering the explosions remains open to debate. Observations of both lGRBs, supernovae associated with these GRBs, such as broad-line (BL) stripped-envelope (type Ic) supernovae (hereafter, Ic-BL) supernovae (SNe) and perhaps superluminous SNe, fast blue optical transients, and fast x-ray transients, may provide clues to both engines and progenitors. In this paper, we conduct a detailed study of the tight-binary formation scenario for lGRBs, comparing this scenario to other leading progenitor models. Combining this progenitor scenario with different lGRB engines, we can compare to existing data and make predictions for future observational tests. We find that the combination of the tight-binary progenitor scenario with the black hole accretion disk (BHAD) engine can explain lGRBs, low-luminosity GRBs, ultra-long GRBs, and Ic-BL. We discuss the various progenitor properties required for these different subclasses and note such systems would be future gravitational wave merger sources. We show that the current literature on other progenitor-engine scenarios cannot explain all of these transient classes with a single origin, motivating additional work. We find that the tight-binary progenitor with a magnetar engine is excluded by existing observations. The observations can be used to constrain the properties of stellar evolution, the nature of the GRB and the associated SN engines in lGRBs and Ic-BL. We discuss the future observations needed to constrain our understanding of these rare, but powerful, explosions., Comment: 20 pages, 10 figures, submitted to ApJ

The Axion Dark Matter eXperiment is sensitive to narrow axion flows, given axions compose a fraction of the dark matter with a non-negligible local density. Detecting these low-velocity dispersion flows requires a high spectral resolution and careful attention to the expected signal modulation due to Earth's motion. We report an exclusion on the local axion dark matter density in narrow flows of $\rho_a \gtrsim 0.03\,\mathrm{GeV/cm^3}$ and $\rho_a \gtrsim 0.004\,\mathrm{GeV/cm^3}$ for Dine-Fischler-Srednicki-Zhitnitski and Kim-Shifman-Vainshtein-Zakharov axion-photon couplings, respectively, over the mass range $3.3-4.2\,\mu\text{eV}$. Measurements were made at selected resolving powers to allow for a range of possible velocity dispersions., Comment: 7 pages, 3 figures

This paper presents the NSX front-end ASIC, being developed to read charge signals from the HEMA and WFM X-ray detectors for the STROBE-X mission. The ASIC reads out signals from up to 64 anodes of linear Silicon Drift Detectors (SDDs). When unloaded, the ASIC channel has a charge resolution, expressed in Equivalent Noise Charge (ENC) of about 2.8 e-. Once connected to the SDD anode we anticipate, for the 80 keV energy range, a ENC of about 10.7 e- at a leakage current of 2 pA, which corresponds to a FWHM of about 145 eV at 6 keV once the Fano-limited statistics from charge generation in Si is included. The acquisition is event-triggered and, for events exceeding the threshold, the ASIC measures the peak amplitude and stores it in an analog memory for subsequent readout. The ASIC can also force the measurement of the sub-threshold channels neighboring the triggered channel, including the ones that belong to neighbor chips by using bi-directional differential inter-chip communication. Alternatively, the ASIC can measure the amplitudes of all channels at the time of the first detected peak. Additional features include a high-resolution option, channel power down and skip function, a low-noise pulse generator, a temperature sensor, and the monitoring of the channel analog output and trimmed threshold. The power consumption of the individual channel is ~590 $\mu$W and, when including all shared circuits, it averages to ~670 $\mu$W / channel., Comment: 16 pages, 12 figures, accepted for publication in JATIS

Neural network (NN) emulators of the global 21 cm signal need emulation error much less than the observational noise in order to be used to perform unbiased Bayesian parameter inference. To this end, we introduce $\texttt{21cmLSTM}$ -- a long short-term memory (LSTM) NN emulator of the global 21 cm signal that leverages the intrinsic correlation between frequency channels to achieve exceptional accuracy compared to previous emulators, which are all feedforward, fully connected NNs. LSTM NNs are a type of recurrent NN designed to capture long-term dependencies in sequential data. When trained and tested on the same simulated set of global 21 cm signals as the best previous emulators, $\texttt{21cmLSTM}$ has average relative rms error of 0.22% -- equivalently 0.39 mK -- and comparably fast evaluation time. We perform seven-dimensional Bayesian parameter estimation analyses using $\texttt{21cmLSTM}$ to fit global 21 cm signal mock data with different adopted observational noise levels, $\sigma_{21}$. The posterior $1\sigma$ rms error is $\approx3\times$ less than $\sigma_{21}$ for each fit and consistently decreases for tighter noise levels, showing that $\texttt{21cmLSTM}$ can sufficiently exploit even very optimistic measurements of the global 21 cm signal. We made the emulator, code, and data sets publicly available so that $\texttt{21cmLSTM}$ can be independently tested and used to retrain and constrain other 21 cm models., Comment: 15 pages, 8 figures, 3 tables. Accepted by ApJ

Type II supernovae (SNeII) mark the endpoint in the lives of hydrogen-rich massive stars. Their large explosion energies and luminosities allow us to measure distances, metallicities, and star formation rates into the distant Universe. To fully exploit their use in answering different astrophysical problems, high-quality low-redshift data sets are required. Such samples are vital to understand the physics of SNeII, but also to serve as calibrators for distinct - and often lower-quality - samples. We present uBgVri optical and YJH near-infrared (NIR) photometry for 94 low-redshift SNeII observed by the Carnegie Supernova Project (CSP). A total of 9817 optical and 1872 NIR photometric data points are released, leading to a sample of high-quality SNII light curves during the first ~150 days post explosion on a well-calibrated photometric system. The sample is presented and its properties are analysed and discussed through comparison to literature events. We also focus on individual SNeII as examples of classically defined subtypes and outlier objects. Making a cut in the plateau decline rate of our sample (s2), a new subsample of fast-declining SNeII is presented. The sample has a median redshift of 0.015, with the nearest event at 0.001 and the most distant at 0.07. At optical wavelengths (V), the sample has a median cadence of 4.7 days over the course of a median coverage of 80 days. In the NIR (J), the median cadence is 7.2 days over the course of 59 days. The fast-declining subsample is more luminous than the full sample and shows shorter plateau phases. Of the non-standard SNeII highlighted, SN2009A particularly stands out with a steeply declining then rising light curve, together with what appears to be two superimposed P-Cygni profiles of H-alpha in its spectra. We outline the significant utility of these data, and finally provide an outlook of future SNII science., Comment: Accepted for publication in A&A. Photometric data will be uploaded to the CDS and the CSP website, and can also be requested from the first author

Analog dynamical accelerators (DXs) are a growing sub-field in computer architecture research, offering order-of-magnitude gains in power efficiency and latency over traditional digital methods in several machine learning, optimization, and sampling tasks. However, limited-capacity accelerators require hybrid analog/digital algorithms to solve real-world problems, commonly using large-neighborhood local search (LNLS) frameworks. Unlike fully digital algorithms, hybrid LNLS has no non-asymptotic convergence guarantees and no principled hyperparameter selection schemes, particularly limiting cross-device training and inference. In this work, we provide non-asymptotic convergence guarantees for hybrid LNLS by reducing to block Langevin Diffusion (BLD) algorithms. Adapting tools from classical sampling theory, we prove exponential KL-divergence convergence for randomized and cyclic block selection strategies using ideal DXs. With finite device variation, we provide explicit bounds on the 2-Wasserstein bias in terms of step duration, noise strength, and function parameters. Our BLD model provides a key link between established theory and novel computing platforms, and our theoretical results provide a closed-form expression linking device variation, algorithm hyperparameters, and performance., Comment: 31 pages, 2 figures

We consider the process of precipitation in binary alloys in the presence of mechanical deformation. It is commonly observed that mechanical deformation prior to or during precipitation leads to microstructure with excess defects, which allows for enhanced precipitate nucleation and growth rates. To investigate this phenomenon, we employ a two-dimensional phase-field crystal alloy model endowed with a temperature dependent mobility, making it capable of recovering isothermal transformation (TTT) diagrams with a characteristic inflection point (nose) about a critical temperature. We examine the variation in the time-scale of precipitation and its connection to the time-scale of the applied deformation, focusing on the roles of atomic defects in the processes involved. Our results indicate that precipitation is initially delayed through application of a deformation until a critical strain is achieved, beyond which precipitation proceeds more rapidly, assisted by plastic deformation such as grain boundary buckling or dislocation nucleation. We show that the evolution of the precipitated fraction, $f(t)$, departs from classical Avrami behaviour. Specifically, $df/dt$ develops two peaks indicative of a ``plateau"-like inflection in $f(t)$, signalling the transition to defect assisted precipitate nucleation. We analyze these plateaus as a function of the deformation rate and demonstrate that the they exhibit a discontinuous bifurcation as the time-scale of applied deformation is increased. These findings are compared to and found to be consistent with experiments., Comment: Main text: 6 pages, 5 figures, Supplemental Material: 2 pages, 1 figure submitted to Physical Review Letters

The advancements of language language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked both excitement and skepticism about the true capabilities of such agents. In this work, we argue that for an agent to fully automate scientific discovery, it must be able to complete all essential tasks in the workflow. Thus, we call for rigorous assessment of agents on individual tasks in a scientific workflow before making bold claims on end-to-end automation. To this end, we present ScienceAgentBench, a new benchmark for evaluating language agents for data-driven scientific discovery. To ensure the scientific authenticity and real-world relevance of our benchmark, we extract 102 tasks from 44 peer-reviewed publications in four disciplines and engage nine subject matter experts to validate them. We unify the target output for every task to a self-contained Python program file and employ an array of evaluation metrics to examine the generated programs, execution results, and costs. Each task goes through multiple rounds of manual validation by annotators and subject matter experts to ensure its annotation quality and scientific plausibility. We also propose two effective strategies to mitigate data contamination concerns. Using our benchmark, we evaluate five open-weight and proprietary LLMs, each with three frameworks: direct prompting, OpenHands, and self-debug. Given three attempts for each task, the best-performing agent can only solve 32.4% of the tasks independently and 34.3% with expert-provided knowledge. These results underscore the limited capacities of current language agents in generating code for data-driven discovery, let alone end-to-end automation for scientific research., Comment: 55 pages

The Time-Domain And MultiMessenger (TDAMM) Communications Science Analysis Group (TDAMMCommSAG) was formulated to describe the unique technical challenges of communicating rapidly to and from NASA astrophysics missions studying the most variable, transient, and extreme objects in the Universe. This report describes the study of if and how the transition from current NASA-operated space and ground relays to commercial services will adequately serve these missions. Depending on the individual mission requirements and Concept of Operations (ConOps), TDAMM missions may utilize a rapid low-rate demand access service, a low-rate continuous contact service, low-latency downlink upon demand, or a higher-latency but regular relay service. The specific implementations can vary via space relay or direct to Earth, but requires flexibility and adaptability using modern software infrastructure. The study team reviewed the current state of NASA communications services and future commercial and NASA communications services under study and in development. We explored the communications capabilities driving from the behavior of the astrophysical objects themselves., Comment: 20 page, 1 figure

We investigate the Lyman-$\alpha$ (Ly$\alpha$) and Lyman continuum (LyC) properties of the Sunburst Arc, a $z=2.37$ gravitationally lensed galaxy with a multiply-imaged, compact region leaking LyC and a triple-peaked Ly$\alpha$ profile indicating direct Ly$\alpha$ escape. Non-LyC-leaking regions show a redshifted Ly$\alpha$ peak, a redshifted and central Ly$\alpha$ peak, or a triple-peaked Ly$\alpha$ profile. We measure the properties of the Ly$\alpha$ profile from different regions of the galaxy using $R\sim5000$ Magellan/MagE spectra. We compare the Ly$\alpha$ spectral properties to LyC and narrowband Ly$\alpha$ maps from Hubble Space Telescope (HST) imaging to explore the subgalactic Ly$\alpha-$LyC connection. We find strong correlations (Pearson correlation coefficient $r>0.6$) between the LyC escape fraction ($f_{\rm esc}^{\rm LyC}$) and Ly$\alpha$ (1) peak separation $v_{\rm{sep}}$, (2) ratio of the minimum flux density between the redshifted and blueshifted Ly$\alpha$ peaks to continuum flux density $f_{\rm{min}}/f_{\rm{cont}}$, and (3) equivalent width. We favor a complex \ion{H}{1} geometry to explain the Ly$\alpha$ profiles from non-LyC-leaking regions and suggest two \ion{H}{1} geometries that could diffuse and/or rescatter the central Ly$\alpha$ peak from the LyC-leaking region into our sightline across transverse distances of several hundred parsecs. Our results emphasize the complexity of Ly$\alpha$ radiative transfer and its sensitivity to the anisotropies of \ion{H}{1} gas on subgalactic scales. Large differences in the physical scales on which we observe spatially variable direct escape Ly$\alpha$, blueshifted Ly$\alpha$, and escaping LyC photons in the Sunburst Arc underscore the importance of resolving the physical scales that govern Ly$\alpha$ and LyC escape., Comment: Submitted to The Astrophysical Journal with revisions from the first referee report. Comments welcome

The prompt spectra of gamma-ray bursts are known to follow broadband continuum behavior over decades in energy. GRB 221009A, given the moniker the brightest of all time (BOAT), is the brightest gamma-ray burst identified in half a century of observations, and was first identified by the Fermi Gamma-ray Burst Monitor (GBM). On behalf of the Fermi-GBM Team, Lesage et al. (2023) described the initial GBM analysis. Ravasio et al. (2024) report the identification of a spectral line in part of the prompt emission of this burst, which they describe as evolving over 80 s from $\sim$12 MeV to 6 MeV. We report a GBM Team analysis on the Ravasio Line: 1) We cannot identify an instrumental effect that could have produced this signal, and 2) our method of calculating the statistical significance of the line shows it easily exceeds the 5$\sigma$ discovery threshold. We additionally comment on the claim of the line beginning at earlier time intervals, up to 37 MeV, as reported in Zhang et al. (2024). We find that it is reasonable to utilize these measurements for characterization of the line evolution, with caution. We encourage theoretical studies exploring this newly discovered gamma-ray burst spectral feature, unless any rigorous alternative explanation unrelated to the emission from GRB 221009A is identified.

In the rapidly rotating limit, we derive a balanced set of reduced equations governing the strongly nonlinear development of the convective wall-mode instability in the interior of a general container. The model illustrates that wall-mode convection is a multiscale phenomenon where the dynamics of the bulk interior diagnostically determine the small-scale dynamics within Stewartson boundary layers at the sidewalls. The sidewall boundary layers feedback on the interior via a nonlinear lateral heat-flux boundary condition, providing a closed system. Outside the asymptotically thin boundary layer, the convective modes connect to a dynamical interior that maintains scales set by the domain geometry. In many ways, the final system of equations resembles boundary-forced planetary geostrophic baroclinic dynamics coupled with barotropic quasi-geostrophic vorticity. The reduced system contains the results from previous linear instability theory but captured in an elementary fashion, providing a new avenue for investigating wall-mode convection in the strongly nonlinear regime. We also derive the dominant Ekman-flux correction to the onset Rayleigh number for large Taylor number, Ra ~ 31.8 Ta^(1/2) - 4.43 Ta^(5/12) for no-slip boundaries. We demonstrate some of the reduced model's nonlinear dynamics with numerical simulations in a cylindrical container., Comment: 38 pages, 13 figures

We present the detection and analysis of GRB 231115A, a candidate extragalactic magnetar giant flare (MGF) observed by Fermi/GBM and localized by INTEGRAL to the starburst galaxy M82. This burst exhibits distinctive temporal and spectral characteristics that align with known MGFs, including a short duration and a high peak energy. Gamma-ray analyses reveal significant insights into this burst, supporting conclusions already established in the literature: our time-resolved spectral studies provide further evidence that GRB 231115A is indeed a MGF. Significance calculations also suggest a robust association with M82, further supported by a high Bayes factor that minimizes the probability of chance alignment with a neutron star merger. Despite extensive follow-up efforts, no contemporaneous gravitational wave or radio emissions were detected. The lack of radio emission sets stringent upper limits on possible radio luminosity. Constraints from our analysis show no fast radio bursts (FRBs) associated with two MGFs. X-ray observations conducted post-burst by Swift/XRT and XMM/Newton provided additional data, though no persistent counterparts were identified. Our study underscores the importance of coordinated multi-wavelength follow-up and highlights the potential of MGFs to enhance our understanding of short GRBs and magnetar activities in the cosmos. Current MGF identification and follow-up implementation are insufficient for detecting expected counterparts; however, improvements in these areas may allow for the recovery of follow-up signals with existing instruments. Future advancements in observational technologies and methodologies will be crucial in furthering these studies.

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-energy (< 10 MeV) pulse that triggered the Fermi Gamma-Ray Burst Monitor (GBM), as has been observed in other GRBs. The prompt phase of GRB 221009A lasted a few hundred seconds. It was so bright that we identify a Bad Time Interval (BTI) of 64 seconds caused by the extremely high flux of hard X-rays and soft gamma rays, during which the event reconstruction efficiency was poor and the dead time fraction quite high. The late-time emission decayed as a power law, but the extrapolation of the late-time emission during the first 450 seconds suggests that the afterglow started during the prompt emission. We also found that high-energy events observed by the LAT are incompatible with synchrotron origin, and, during the prompt emission, are more likely related to an extra component identified as synchrotron self-Compton (SSC). A remarkable 400 GeV photon, detected by the LAT 33 ks after the GBM trigger and directionally consistent with the location of GRB 221009A, is hard to explain as a product of SSC or TeV electromagnetic cascades, and the process responsible for its origin is uncertain. Because of its proximity and energetic nature, GRB 221009A is an extremely rare event., Comment: 60 pages, 38 figures, 9 tables

Observable estimation is a core primitive in NISQ-era algorithms targeting quantum chemistry applications. To reduce the state preparation overhead required for accurate estimation, recent works have proposed various simultaneous measurement schemes to lower estimator variance. Two primary grouping schemes have been proposed: fully commutativity (FC) and qubit-wise commutativity (QWC), with no compelling means of interpolation. In this work we propose a generalized framework for designing and analyzing context-aware hybrid FC/QWC commutativity relations. We use our framework to propose a noise-and-connectivity aware grouping strategy: Generalized backend-Aware pauLI Commutation (GALIC). We demonstrate how GALIC interpolates between FC and QWC, maintaining estimator accuracy in Hamiltonian estimation while lowering variance by an average of 20% compared to QWC. We also explore the design space of near-term quantum devices using the GALIC framework, specifically comparing device noise levels and connectivity. We find that error suppression has a more than $13\times$ larger impact on device-aware estimator variance than qubit connectivity with even larger correlation differences in estimator biases., Comment: 33 pages, 4 figures

In 21-cm experimental cosmology, accurate characterization of a radio telescope's antenna beam response is essential to measure the 21-cm signal. Computational electromagnetic (CEM) simulations estimate the antenna beam pattern and frequency response by subjecting the EM model to different dependencies, or beam hyper-parameters, such as soil dielectric constant or orientation with the environment. However, it is computationally expensive to search all possible parameter spaces to optimize the antenna design or accurately represent the beam to the level required for use as a systematic model in 21-cm cosmology. We therefore present $\texttt{MEDEA}$, an emulator which rapidly and accurately generates farfield radiation patterns over a large hyper-parameter space. $\texttt{MEDEA}$ takes a subset of beams simulated by CEM software, spatially decomposes them into coefficients in a complete, linear basis, and then interpolates them to form new beams at arbitrary hyper-parameters. We test $\texttt{MEDEA}$ on an analytical dipole and two numerical beams motivated by upcoming lunar lander missions, and then employ $\texttt{MEDEA}$ as a model to fit mock radio spectrometer data to extract covariances on the input beam hyper-parameters. We find that the interpolated beams have RMS relative errors of at most $10^{-2}$ using 20 input beams or less, and that fits to mock data are able to recover the input beam hyper-parameters when the model and mock derive from the same set of beams. When a systematic bias is introduced into the mock data, extracted beam hyper-parameters exhibit bias, as expected. We propose several future extensions to $\texttt{MEDEA}$ to potentially account for such bias., Comment: 23 pages, 12 figures, accepted for publication in ApJ

Quadrotor-based multimodal hopping and flying locomotion significantly improves efficiency and operation time as compared to purely flying systems. However, effective control necessitates continuous estimation of the vertical states. A single hopping state estimator has been shown (Kang 2024), in which two vertical states (position, acceleration) are measured and only velocity is estimated using a moving horizon estimation and visual inertial odometry at 200 Hz. This technique requires complex sensors (IMU, lidar, depth camera, contact force sensor), and computationally intensive calculations (12-core, 5 GHz processor), for a maximum hop height of $\sim$0.6 m at 3.65 kg. Here we show a trained Kalman filter based hopping vertical state estimator (HVSE), requiring only vertical acceleration measurements. Our results show the HVSE can estimate more states (position, velocity) with a mean-absolute-error in the hop apex ratio (height error/ground truth) of 12.5\%, running $\sim$4.2x faster (840 Hz) on a substantially less powerful processor (dual-core 240 MHz) with over $\sim$6.7x the hopping height (4.02 m) at 20\% of the mass (672 g). The presented general HVSE, and training procedure are broadly applicable to jumping, hopping, and legged robots across a wide range of sizes and hopping heights., Comment: 14 pages, 6 figures, 5 tables

In a recent class of phase field crystal (PFC) models, the density order parameter is coupled to powers of its mean field. This effectively introduces a phenomenology of higher-order direct correlation functions acting on long wavelengths, which is required for modelling solid-liquid-vapor systems. The present work generalizes these models by incorporating, into a single-field theory, higher-order direct correlations, systematically constructed in reciprocal space to operate across long {\it and} short wavelengths. The correlation kernels introduced are also readily adaptable to describe distinct crystal structures. We examine the three-phase equilibrium properties and phase diagrams of the proposed model, and reproduce parts of the aluminum phase diagram as an example of its versatile parametrization. We assess the dynamics of the model, showing that it allows robust control of the interface energy between the vapor and condensed phases (liquid and solid). We also examine the dynamics of solid-vapor interfaces over a wide range of parameters and find that dynamical artifacts reported in previous PFC models do not occur in the present formalism. Additionally, we demonstrate the capacity of the proposed formalism for computing complex microstructures and defects such as dislocations, grain boundaries, and voids in solid-liquid-vapor systems, all of which are expected to be crucial for investigating rapid solidification processes.

Strong optomechanical coupling -- a regime where mechanical motion is damped by environmental radiation -- has traditionally required demanding experimental ingredients such as superconducting resonators, high-quality optical cavities, or large magnetic fields. Here we demonstrate a room temperature, cavity-free, all-electric device reaching this regime at radio frequencies, enabled by a mechanically compliant parallel-plate capacitor with a nanoscale plate separation and an aspect ratio exceeding 1,000. The device has four orders of magnitude lower insertion loss than a comparable commercial quartz crystal, and achieves a position imprecision rivaling an optical interferometer. With the help of a back-action isolation scheme, we observe radiative cooling of mechanical motion by a remote cryogenic load. This work provides a technologically accessible route to high-precision sensing, transduction, and signal processing., Comment: 19+13 pages, 5+2 figures

Reinforcement learning (RL) has recently shown promise in predicting Alzheimer's disease (AD) progression due to its unique ability to model domain knowledge. However, it is not clear which RL algorithms are well-suited for this task. Furthermore, these methods are not inherently explainable, limiting their applicability in real-world clinical scenarios. Our work addresses these two important questions. Using a causal, interpretable model of AD, we first compare the performance of four contemporary RL algorithms in predicting brain cognition over 10 years using only baseline (year 0) data. We then apply SHAP (SHapley Additive exPlanations) to explain the decisions made by each algorithm in the model. Our approach combines interpretability with explainability to provide insights into the key factors influencing AD progression, offering both global and individual, patient-level analysis. Our findings show that only one of the RL methods is able to satisfactorily model disease progression, but the post-hoc explanations indicate that all methods fail to properly capture the importance of amyloid accumulation, one of the pathological hallmarks of Alzheimer's disease. Our work aims to merge predictive accuracy with transparency, assisting clinicians and researchers in enhancing disease progression modeling for informed healthcare decisions. Code is available at https://github.com/rfali/xrlad., Comment: Previous versions accepted to NeurIPS 2023's XAIA and AAAI 2024's XAI4DRL workshops

Mobile app user interfaces (UIs) are rich with action, text, structure, and image content that can be utilized to learn generic UI representations for tasks like automating user commands, summarizing content, and evaluating the accessibility of user interfaces. Prior work has learned strong visual representations with local or global captioning losses, but fails to retain both granularities. To combat this, we propose Textual Foresight, a novel pretraining objective for learning UI screen representations. Textual Foresight generates global text descriptions of future UI states given a current UI and local action taken. Our approach requires joint reasoning over elements and entire screens, resulting in improved UI features: on generation tasks, UI agents trained with Textual Foresight outperform state-of-the-art by 2% with 28x fewer images. We train with our newly constructed mobile app dataset, OpenApp, which results in the first public dataset for app UI representation learning. OpenApp enables new baselines, and we find Textual Foresight improves average task performance over them by 5.7% while having access to 2x less data., Comment: Accepted to ACL 2024 Findings. Data and code to be released at https://github.com/aburns4/textualforesight

Time-domain and multimessenger astronomy (TDAMM) involves the study of transient and time-variable phenomena across various wavelengths and messengers. The Astro2020 Decadal Survey has identified TDAMM as the top priority for NASA in this decade, emphasizing its crucial role in advancing our understanding of the universe and driving new discoveries in astrophysics. The TDAMM community has come together to provide further guidance to funding agencies, aiming to define a clear path toward optimizing scientific returns in this research domain. This encompasses not only astronomy but also fundamental physics, offering insights into gravity properties, the formation of heavy elements, the equation of state of dense matter, and quantum effects associated with extreme magnetic fields. Magnetars, neutron stars with the strongest magnetic fields known in the universe, play a critical role in this context. In this manuscript, we aim to underscore the significance of magnetars in TDAMM, highlighting the necessity of ensuring observational continuity, addressing current limitations, and outlining essential requirements to expand our knowledge in this field., Comment: Accepted by Frontiers

Gamma-ray Bursts (GRBs) are one of the most energetic phenomena in the cosmos, whose study probes physics extremes beyond the reach of laboratories on Earth. Our quest to unravel the origin of these events and understand their underlying physics is far from complete. Central to this pursuit is the rapid classification of GRBs to guide follow-up observations and analysis across the electromagnetic spectrum and beyond. Here, we introduce a compelling approach for a new and robust GRB prompt classification. Leveraging self-supervised deep learning, we pioneer a previously unexplored data product to approach this task: the GRB waterfalls., Comment: Submitted for review to PRL

Cooperative driving, enabled by communication between automated vehicle systems, promises significant benefits to fuel efficiency, road capacity, and safety over single-vehicle driver assistance systems such as adaptive cruise control (ACC). However, the responsible development and implementation of these algorithms poses substantial challenges due to the need for extensive real-world testing. We address this issue and introduce OpenConvoy, an open and extensible framework designed for the implementation and assessment of cooperative driving policies on physical connected and autonomous vehicles (CAVs). We demonstrate the capabilities of OpenConvoy through a series of experiments on a convoy of multi-scale vehicles controlled by Platooning to show the stability of our system across vehicle configurations and its ability to effectively measure convoy cohesion across driving scenarios including varying degrees of communication loss., Comment: 7 pages, 8 figures

We investigate the running sums of some well-known automatic sequences to determine whether they are synchronised., Comment: 21 pages, 7 figures

Understanding the nature of the luminous 1991T-like supernovae is of great importance to supernova cosmology as they are likely to have been more common in the early universe. In this paper we explore the observational properties of 1991T-like supernovae to study their relationship to other luminous, slow-declining Type~Ia supernovae (SNe Ia). From the spectroscopic and photometric criteria defined in Phillips et al. (1992), we identify 17 1991T-like supernovae from the literature. Combining these objects with ten 1991T-like supernovae from the Carnegie Supernova Project-II, the spectra, light curves, and colors of these events, along with their host galaxy properties, are examined in detail. We conclude that 1991T-like supernovae are closely related in essentially all of their UV, optical, and near-infrared properties -- as well as their host galaxy parameters -- to the slow-declining subset of Branch core-normal supernovae and to the intermediate 1999aa-like events, forming a continuum of luminous SNe Ia. The overriding difference between these three subgroups appears to be the extent to which $^{56}$Ni mixes into the ejecta, producing the pre-maximum spectra dominated by Fe III absorption, the broader UV light curves, and the higher luminosities that characterize the 1991T-like events. Nevertheless, the association of 1991T-like SNe with the rare Type Ia CSM supernovae would seem to run counter to this hypothesis, in which case 1991T-like events may form a separate subclass of SNe Ia, possibly arising from single-degenerate progenitor systems., Comment: Accepted for publication in ApJS

Reinforcement learning (RL) is an effective method of finding reasoning pathways in incomplete knowledge graphs (KGs). To overcome the challenges of a large action space, a self-supervised pre-training method is proposed to warm up the policy network before the RL training stage. To alleviate the distributional mismatch issue in general self-supervised RL (SSRL), in our supervised learning (SL) stage, the agent selects actions based on the policy network and learns from generated labels; this self-generation of labels is the intuition behind the name self-supervised. With this training framework, the information density of our SL objective is increased and the agent is prevented from getting stuck with the early rewarded paths. Our self-supervised RL (SSRL) method improves the performance of RL by pairing it with the wide coverage achieved by SL during pretraining, since the breadth of the SL objective makes it infeasible to train an agent with that alone. We show that our SSRL model meets or exceeds current state-of-the-art results on all Hits@k and mean reciprocal rank (MRR) metrics on four large benchmark KG datasets. This SSRL method can be used as a plug-in for any RL architecture for a KGR task. We adopt two RL architectures, i.e., MINERVA and MultiHopKG as our baseline RL models and experimentally show that our SSRL model consistently outperforms both baselines on all of these four KG reasoning tasks. Full code for the paper available at https://github.com/owenonline/Knowledge-Graph-Reasoning-with-Self-supervised-Reinforcement-Learning., Comment: 17 pages, 11 figures

GW230529 is the first compact binary coalescence detected by the LIGO-Virgo-KAGRA collaboration with at least one component mass confidently in the lower mass-gap, corresponding to the range 3-5$M_{\odot}$. If interpreted as a neutron star-black hole merger, this event has the most symmetric mass ratio detected so far and therefore has a relatively high probability of producing electromagnetic (EM) emission. However, no EM counterpart has been reported. At the merger time $t_0$, Swift-BAT and Fermi-GBM together covered 100$\%$ of the sky. Performing a targeted search in a time window $[t_0-20 \text{s},t_0+20 \text{s}]$, we report no detection by the Swift-BAT and the Fermi-GBM instruments. Combining the position-dependent $\gamma-$ray flux upper limits and the gravitational-wave posterior distribution of luminosity distance, sky localization and inclination angle of the binary, we derive constraints on the characteristic luminosity and structure of the jet possibly launched during the merger. Assuming a top-hat jet structure, we exclude at 90$\%$ credibility the presence of a jet which has at the same time an on-axis isotropic luminosity $\gtrsim 10^{48}$ erg s$^{-1}$, in the bolometric band 1 keV-10 MeV, and a jet opening angle $\gtrsim 15$ deg. Similar constraints are derived testing other assumptions about the jet structure profile. Excluding GRB 170817A, the luminosity upper limits derived here are below the luminosity of any GRB observed so far., Comment: 18 pages, 1 table, 11 figures

Forte is an open-source library specialized in multireference electronic structure theories for molecular systems and the rapid prototyping of new methods. This paper gives an overview of the capabilities of Forte, its software architecture, and examples of applications enabled by the methods it implements.

Despite the longstanding adage "an image is worth a thousand words," creating accurate and hyper-detailed image descriptions for training Vision-Language models remains challenging. Current datasets typically have web-scraped descriptions that are short, low-granularity, and often contain details unrelated to the visual content. As a result, models trained on such data generate descriptions replete with missing information, visual inconsistencies, and hallucinations. To address these issues, we introduce ImageInWords (IIW), a carefully designed human-in-the-loop annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process. We validate the framework through evaluations focused on the quality of the dataset and its utility for fine-tuning with considerations for readability, comprehensiveness, specificity, hallucinations, and human-likeness. Our dataset significantly improves across these dimensions compared to recently released datasets (+66%) and GPT-4V outputs (+48%). Furthermore, models fine-tuned with IIW data excel by +31% against prior work along the same human evaluation dimensions. Given our fine-tuned models, we also evaluate text-to-image generation and vision-language reasoning. Our model's descriptions can generate images closest to the original, as judged by both automated and human metrics. We also find our model produces more compositionally rich descriptions, outperforming the best baseline by up to 6% on ARO, SVO-Probes, and Winoground datasets., Comment: Webpage (https://google.github.io/imageinwords), GitHub (https://github.com/google/imageinwords), HuggingFace (https://huggingface.co/datasets/google/imageinwords)