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The National Assessment of Educational Progress (NAEP) is an integral measure of academic progress over time. It is the largest nationally representative and continuing assessment of what our nation's students know and can do in various subjects such as civics, mathematics, reading, science, technology and engineering literacy, U.S. history, and writing. The program also provides valuable insights into students' educational experiences and opportunities to learn in and outside of the classroom. Elected officials, policymakers, and educators all use NAEP results to develop ways to improve education. The 2024 program will include: (1) assessments in mathematics and reading at grades 4, 8, and 12 and science at grade 8; (2) pilot testing in mathematics and reading at grades 4 and 8, to help improve future NAEP assessments and ensure that they continue to be a reliable measure of student achievement; (3) survey questionnaires for participating students, teachers, and principals, to provide a better understanding of factors that may be related to students' learning. This brief document highlights the NAEP 2024 assessment, teachers as essential partners in NAEP, and recent NAEP results. [For "NAEP 2023 Facts for Teachers. Grades 4, 8, and 12 Field Test," see ED627646.]

High-quality (Q)-factor optical resonators with extreme temporal coherence are of both technological and fundamental importance in optical metrology, continuous-wave lasing, and semiconductor quantum optics. Despite extensive efforts in designing high-Q resonators across different spectral regimes, the experimental realization of very large Q-factors at visible wavelengths remains challenging due to the small feature size that is sensitive to fabrication imperfections, and thus is typically implemented in integrated photonics. In the pursuit of free-space optics with the benefits of large space-bandwidth product and massive parallel operations, here we design and fabricate a visible-wavelength etch-free metasurface with minimized fabrication defects and experimentally demonstrate a million-scale ultrahigh-Q resonance. A new laser-scanning momentum-space-resolved spectroscopy technique with extremely high spectral and angular resolution is developed to characterize the record-high Q-factor as well as the dispersion of the million-Q resonance in free space. By integrating monolayer WSe2 into our ultrahigh-Q meta-resonator, we further demonstrate laser-like highly unidirectional and narrow-linewidth exciton emission, albeit without any operating power density threshold. Under continuous-wave laser pumping, we observe pump-power-dependent linewidth narrowing at room temperature, indicating the potential of our meta-optics platform in controlling coherent quantum light-sources. Our result also holds great promise for applications like optical sensing, spectral filtering, and few-photon nonlinear optics.

Social bots-automated accounts that generate and spread content on social media-are exploiting vulnerabilities in these platforms to manipulate public perception and disseminate disinformation. This has prompted the development of public bot detection services; however, most of these services focus primarily on Twitter, leaving niche platforms vulnerable. Fringe social media platforms such as Parler, Gab, and Gettr often have minimal moderation, which facilitates the spread of hate speech and misinformation. To address this gap, we introduce Entendre, an open-access, scalable, and platform-agnostic bot detection framework. Entendre can process a labeled dataset from any social platform to produce a tailored bot detection model using a random forest classification approach, ensuring robust social bot detection. We exploit the idea that most social platforms share a generic template, where users can post content, approve content, and provide a bio (common data features). By emphasizing general data features over platform-specific ones, Entendre offers rapid extensibility at the expense of some accuracy. To demonstrate Entendre's effectiveness, we used it to explore the presence of bots among accounts posting racist content on the now-defunct right-wing platform Parler. We examined 233,000 posts from 38,379 unique users and found that 1,916 unique users (4.99%) exhibited bot-like behavior. Visualization techniques further revealed that these bots significantly impacted the network, amplifying influential rhetoric and hashtags (e.g., #qanon, #trump, #antilgbt). These preliminary findings underscore the need for tools like Entendre to monitor and assess bot activity across diverse platforms., Comment: 6 pages

Beam stoppers are installed in the transfer lines of the CERN accelerator complex; these components are used as part of the access safety system, which guarantees the safety of workers in the accelerators. They are designed to stop one or at most a few pulses of the beam, where "stop" means the partial or complete absorption of the primary beam in such a way that the remaining unabsorbed primary or secondary beam remains below a specified threshold, as defined by the needs of radiation protection. Prior to Long Shutdown 2 (LS2; 2018--2021), beam stoppers in the injector complex were dimensioned for beam-pulse energies between 9.0 and 30~kJ. The upgrade of the accelerator complex in the framework of the LHC Injectors Upgrade (LIU) project involves beam-pulse energies of up to 92.5~kJ, meaning that these beam stoppers are not able to fulfill the new functional specifications. To cope with the LIU beam parameters and fulfil requirements for safety, maintainability, efficiency, and reliability, a new generation of 28 beam stoppers has been designed, built, and installed. The aim of this paper is to demonstrate the requirements-driven design of these new beam stoppers, outlining the main requirements along with a description of the design and structural assessments. This document presents the implementation and integration of a standardized but adaptable design using a unique 564-mm-long stopper core with a CuCr1Zr absorber and an Inconel~718 diluter, taking into account radiological and infrastructure challenges. The installation process is also described, and the first operational feedback received since LS2 is presented., Comment: 10 pages, 25 figures

We report the discovery and spectroscopic confirmation of an ultra-faint Milky Way (MW) satellite in the constellation of Leo. This system was discovered as a spatial overdensity of resolved stars observed with Dark Energy Camera (DECam) data from an early version of the third data release of the DECam Local Volume Exploration survey (DELVE EDR3). The low luminosity ($M_V = -3.56_{-0.37}^{+0.47}$; $L_V = 2300_{-800}^{+1000} L_\odot$), large size ($r_{1/2} = 90_{-30}^{+30}$ pc), and large heliocentric distance ($D = 111_{-4}^{+7}$ kpc) are all consistent with the population of ultra-faint dwarf galaxies (UFDs). Using Keck/DEIMOS observations of the system, we were able to spectroscopically confirm 11 member stars, while measuring a mass to light ratio of $1000_{-700}^{+1900} M_\odot/L_\odot$ and a non-zero metallicity dispersion of $\sigma_{[\rm Fe/H]}=0.33_{-0.14}^{+0.19}$, further confirming Leo VI's identity as an UFD. While the system has an highly elliptical shape, $\epsilon = 0.54_{-0.29}^{+0.19}$, we do not find any evidence that it is tidally disrupting. Moreover, despite its apparent on-sky proximity to members of the proposed Crater-Leo infall group, its relatively lower heliocentric distance and inconsistent position in energy-angular momentum space with the other group members make it unlikely for it to be part of the proposed infall group., Comment: 21 pages, 11 figures, 2 tables; to be submitted to AAS Journals

The $^{244}$Pu($^{50}$Ti,$xn$)$^{294-x}$Lv reaction was investigated at Lawrence Berkeley National Laboratory's 88-Inch Cyclotron facility. The experiment was aimed at the production of a superheavy element with $Z\ge 114$ by irradiating an actinide target with a beam heavier than $^{48}$Ca. Produced Lv ions were separated from the unwanted beam and nuclear reaction products using the Berkeley Gas-filled Separator and implanted into a newly commissioned focal plane detector system. Two decay chains were observed and assigned to the decay of $^{290}$Lv. The production cross section was measured to be $\sigma_{\rm prod}=0.44(^{+58}_{-28})$~pb at a center-of-target center-of-mass energy of 220(3)~MeV. This represents the first published measurement of the production of a superheavy element near the `Island-of-Stability', with a beam of $^{50}$Ti and is an essential precursor in the pursuit of searching for new elements beyond $Z=118$., Comment: Submitted to Physical Review Letters

InAs semiconductor quantum dots (QDs) emitting in the near infrared are promising platforms for on-demand single-photon sources and spin-photon interfaces. However, the realization of quantum-photonic nanodevices emitting in the second and third telecom windows with similar performance remains an open challenge. Here, we report an optimized heterostructure design for QDs emitting in the O- and C-bands grown by means of molecular beam epitaxy. The InAs QDs are grown on compositionally graded InGaAs buffers, which act as virtual substrates, and are embedded in mostly relaxed active regions. Reciprocal space maps of the indium profiles and optical absorption spectra are used to optimize In0.22Ga0.78As and In0.30Ga0.70As active regions, accounting for the chosen indium grading profile. This approach results in a tunable QD photoluminescence (PL) emission from 1200 up to 1600 nm. Power and polarization dependent micro-PL measurements performed at 4 K reveal exciton-biexciton complexes from quantum dots emitting in the telecom O- and C-bands. The presented study establishes a flexible platform that can be an essential component for advanced photonic devices based on InAs/GaAs that serve as building blocks for future quantum networks.

Particle-based representations of radiance fields such as 3D Gaussian Splatting have found great success for reconstructing and re-rendering of complex scenes. Most existing methods render particles via rasterization, projecting them to screen space tiles for processing in a sorted order. This work instead considers ray tracing the particles, building a bounding volume hierarchy and casting a ray for each pixel using high-performance GPU ray tracing hardware. To efficiently handle large numbers of semi-transparent particles, we describe a specialized rendering algorithm which encapsulates particles with bounding meshes to leverage fast ray-triangle intersections, and shades batches of intersections in depth-order. The benefits of ray tracing are well-known in computer graphics: processing incoherent rays for secondary lighting effects such as shadows and reflections, rendering from highly-distorted cameras common in robotics, stochastically sampling rays, and more. With our renderer, this flexibility comes at little cost compared to rasterization. Experiments demonstrate the speed and accuracy of our approach, as well as several applications in computer graphics and vision. We further propose related improvements to the basic Gaussian representation, including a simple use of generalized kernel functions which significantly reduces particle hit counts., Comment: Project page: https://gaussiantracer.github.io/

Ethnography has become one of the established methods for empirical research on software engineering. Although there is a wide variety of introductory books available, there has been no material targeting software engineering students particularly, until now. In this chapter we provide an introduction to teaching and learning ethnography for faculty teaching ethnography to software engineering graduate students and for the students themselves of such courses. The contents of the chapter focuses on what we think is the core basic knowledge for newbies to ethnography as a research method. We complement the text with proposals for exercises, tips for teaching, and pitfalls that we and our students have experienced. The chapter is designed to support part of a course on empirical software engineering and provides pointers and literature for further reading., Comment: 38 pages, to be published in: Daniel Mendez, Paris Avgeriou, Marcos Kalinowski, and Nauman bin Ali (eds.) Teaching Empirical Research Methods in Software Engineering, Springer

The spin-exotic hybrid meson $\pi_{1}(1600)$ is predicted to have a large decay rate to the $\omega\pi\pi$ final state. Using 76.6~pb$^{-1}$ of data collected with the GlueX detector, we measure the cross sections for the reactions $\gamma p \to \omega \pi^+ \pi^- p$, $\gamma p \to \omega \pi^0 \pi^0 p$, and $\gamma p\to\omega\pi^-\pi^0\Delta^{++}$ in the range $E_\gamma =$ 8-10 GeV. Using isospin conservation, we set the first upper limits on the photoproduction cross sections of the $\pi^{0}_{1}(1600)$ and $\pi^{-}_{1}(1600)$. We combine these limits with lattice calculations of decay widths and find that photoproduction of $\eta'\pi$ is the most sensitive two-body system to search for the $\pi_1(1600)$., Comment: 6 pages, 3 figures plus supplemental materials

In this work, we present a new approximate method for obtaining simple wave functions and effective operators for the ground state of exotic nuclei with a neutron halo. We treat the core and halo as inert objects in a two-cluster model. The relative wave function is a combination of simple harmonic oscillator states with a size that is determined from a relation between the nuclear radius and separation energy. Since these wave functions lack the expected exponential decay, we apply a multiplicative renormalization to the radial part of the operators, determined from the nuclear root-mean-square radius. This combination of oscillator wavefunctions and renormalized operators is then applied to the calculation of dipole strength distributions $dB(E1,\varepsilon)/d\varepsilon$ for several $1n$- and $2n$-halo nuclei. By fitting a single free parameter, the results show excellent agreement with the available experimental data., Comment: 8 pages, 3 figures

Confusion and forgetting of object classes have been challenges of prime interest in Few-Shot Object Detection (FSOD). To overcome these pitfalls in metric learning based FSOD techniques, we introduce a novel Submodular Mutual Information Learning (SMILe) framework which adopts combinatorial mutual information functions to enforce the creation of tighter and discriminative feature clusters in FSOD. Our proposed approach generalizes to several existing approaches in FSOD, agnostic of the backbone architecture demonstrating elevated performance gains. A paradigm shift from instance based objective functions to combinatorial objectives in SMILe naturally preserves the diversity within an object class resulting in reduced forgetting when subjected to few training examples. Furthermore, the application of mutual information between the already learnt (base) and newly added (novel) objects ensures sufficient separation between base and novel classes, minimizing the effect of class confusion. Experiments on popular FSOD benchmarks, PASCAL-VOC and MS-COCO show that our approach generalizes to State-of-the-Art (SoTA) approaches improving their novel class performance by up to 5.7% (3.3 mAP points) and 5.4% (2.6 mAP points) on the 10-shot setting of VOC (split 3) and 30-shot setting of COCO datasets respectively. Our experiments also demonstrate better retention of base class performance and up to 2x faster convergence over existing approaches agnostic of the underlying architecture., Comment: Accepted to ECCV 2024, 16 pages, 5 figures, 7 tables

Coral reefs are increasingly subjected to major disturbances threatening the health of marine ecosystems. Substantial research underway to develop intervention strategies that assist reefs in recovery from, and resistance to, inevitable future climate and weather extremes. To assess potential benefits of interventions, mechanistic understanding of coral reef recovery and resistance patterns is essential. Recent evidence suggests that more than half of the reefs surveyed across the Great Barrier Reef (GBR) exhibit deviations from standard recovery modelling assumptions when the initial coral cover is low ($\leq 10$\%). New modelling is necessary to account for these observed patterns to better inform management strategies. We consider a new model for reef recovery at the coral cover scale that accounts for biphasic recovery patterns. The model is based on a multispecies Richards' growth model that includes a change point in the recovery patterns. Bayesian inference is applied for uncertainty quantification of key parameters for assessing reef health and recovery patterns. This analysis is applied to benthic survey data from the Australian Institute of Marine Sciences (AIMS). We demonstrate agreement between model predictions and data across every recorded recovery trajectory with at least two years of observations following disturbance events occurring between 1992--2020. This new approach will enable new insights into the biological, ecological and environmental factors that contribute to the duration and severity of biphasic coral recovery patterns across the GBR. These new insights will help to inform managements and monitoring practice to mitigate the impacts of climate change on coral reefs.

The microfabrication of cesium vapor cells for optically pumped magnetometry relies on optimization of buffer gas pressure in order to maximize atomic coherence time and sensitivity to external magnetic signals. We demonstrate post-bond nitrogen buffer gas pressure tuning through localized heating of an integrated micro-pill dispenser. We characterize the variation in the intrinsic longitudinal relaxation rate, $\gamma_{10}$, and magnetic sensitivity, as a function of the resulting nitrogen buffer gas pressure. Measurements are conducted through employing an optically pumped magnetometer operating in a free-induction-decay configuration. $\gamma_{10}$ is extracted across a range of nitrogen pressures between $\sim$~60~-~700~Torr, measuring a minimum of 140~Hz at 115~Torr. Additionally, we achieve sensitivities as low as 130 ~fT/$\sqrt{\text{Hz}}$ at a bias field amplitude of $\sim 50~\mu$T. With the optimal nitrogen buffer gas pressure now quantified and achievable post-fabrication, these mass-producible cells can be tailored to suit a variety of sensing applications, ensuring peak magnetometer performance., Comment: 7 pages (inc references), 3 figures

We measure the spin-density matrix elements (SDMEs) of the $\Delta^{++}(1232)$ in the photoproduction reaction $\gamma p \to \pi^-\Delta^{++}(1232)$ with the GlueX experiment in Hall D at Jefferson Lab. The measurement uses a linearly--polarized photon beam with energies from $8.2$ to $8.8$~GeV and the statistical precision of the SDMEs exceeds the previous measurement by three orders of magnitude for the momentum transfer squared region below $1.4$ GeV$^2$. The data are sensitive to the previously undetermined relative sign between couplings in existing Regge-exchange models. Linear combinations of the extracted SDMEs allow for a decomposition into natural and unnatural--exchange amplitudes. We find that the unnatural exchange plays an important role in the low momentum transfer region.

The proliferation of 3D representations, from explicit meshes to implicit neural fields and more, motivates the need for simulators agnostic to representation. We present a data-, mesh-, and grid-free solution for elastic simulation for any object in any geometric representation undergoing large, nonlinear deformations. We note that every standard geometric representation can be reduced to an occupancy function queried at any point in space, and we define a simulator atop this common interface. For each object, we fit a small implicit neural network encoding spatially varying weights that act as a reduced deformation basis. These weights are trained to learn physically significant motions in the object via random perturbations. Our loss ensures we find a weight-space basis that best minimizes deformation energy by stochastically evaluating elastic energies through Monte Carlo sampling of the deformation volume. At runtime, we simulate in the reduced basis and sample the deformations back to the original domain. Our experiments demonstrate the versatility, accuracy, and speed of this approach on data including signed distance functions, point clouds, neural primitives, tomography scans, radiance fields, Gaussian splats, surface meshes, and volume meshes, as well as showing a variety of material energies, contact models, and time integration schemes.

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk postoperative site. Target volume annotations adhere to established radiotherapy planning protocols, ensuring consistency across cases and institutions. For preoperative meningiomas, the target volume encompasses the entire GTV and associated nodular dural tail, while for postoperative cases, it includes at-risk resection cavity margins as determined by the treating institution. Case annotations were reviewed and approved by expert neuroradiologists and radiation oncologists. Participating teams will develop, containerize, and evaluate automated segmentation models using this comprehensive dataset. Model performance will be assessed using an adapted lesion-wise Dice Similarity Coefficient and the 95% Hausdorff distance. The top-performing teams will be recognized at the Medical Image Computing and Computer Assisted Intervention Conference in October 2024. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes., Comment: 14 pages, 9 figures, 1 table

Reverberation mapping is the leading technique used to measure direct black hole masses outside of the local Universe. Additionally, reverberation measurements calibrate secondary mass-scaling relations used to estimate single-epoch virial black hole masses. The Australian Dark Energy Survey (OzDES) conducted one of the first multi-object reverberation mapping surveys, monitoring 735 AGN up to $z\sim4$, over 6 years. The limited temporal coverage of the OzDES data has hindered recovery of individual measurements for some classes of sources, particularly those with shorter reverberation lags or lags that fall within campaign season gaps. To alleviate this limitation, we perform a stacking analysis of the cross-correlation functions of sources with similar intrinsic properties to recover average composite reverberation lags. This analysis leads to the recovery of average lags in each redshift-luminosity bin across our sample. We present the average lags recovered for the H$\beta$, Mg II and C IV samples, as well as multi-line measurements for redshift bins where two lines are accessible. The stacking analysis is consistent with the Radius-Luminosity relations for each line. Our results for the H$\beta$ sample demonstrate that stacking has the potential to improve upon constraints on the $R-L$ relation, which have been derived only from individual source measurements until now., Comment: 20 pages, 15 figures. Accepted by MNRAS

The Giant Magellan Telescope will use laser tomography adaptive optics to correct for atmospheric turbulence using artificial guide stars created in the sodium layer of the atmosphere (altitude ~95km). The sodium layer has appreciable thickness (~11km) and this results in the laser guide star being an elongated cylinder shape. Wavefront sensing with a Shack-Hartmann is challenging, as subapertures located further away from the laser launch position image an increasingly elongated perspective of the laser guide star. Large detectors can be used to adequately pack and sample the images on the detector, however, this increases readout noise and limits the design space available for the wavefront sensor. To tackle this challenge, we propose an original solution based on nano-engineered meta-optics tailored to produce a spatially varying anamorphic image scale compression. We present meta-lenslet array designs that can deliver ~100% of the full anamorphic image size reduction required for focal lengths down to 8mm, and greater than 50% image size reduction for focal lengths down to 2mm. This will allow greatly improved sampling of the available information across the whole wavefront sensor, while still being a viable design within the limits of current-generation fabrication facilities.

In 1968, Dashen and Sharp obtained a certain singular Lie algebra of local densities and currents from canonical commutation relations in nonrelativistic quantum field theory. The corresponding Lie group is infinite dimensional: the natural semidirect product of an additive group of scalar functions with a group of diffeomorphisms. Unitary representations of this group describe a wide variety of quantum systems, and have predicted previously unsuspected possibilities; notably, anyons and nonabelian anyons in two space dimensions. We present here foundational reasons why this semidirect product group serves as a universal kinematical group for quantum mechanics. We obtain thus a unified account of all possible quantum kinematics for systems with mass in an arbitrary physical space, and clarify the role played by topology in quantum mechanics. Our development does not require quantization of classical phase space; rather, the classical limit follows from the quantum mechanics. We also consider the relationship of our development to Heisenberg quantization., Comment: This report is based on the presentation by G. A. Goldin at the International Conference on Mathematical Methods in Physics 2023, in Marrakesh, Morocco

Health coaching helps patients achieve personalized and lifestyle-related goals, effectively managing chronic conditions and alleviating mental health issues. It is particularly beneficial, however cost-prohibitive, for low-socioeconomic status populations due to its highly personalized and labor-intensive nature. In this paper, we propose a neuro-symbolic goal summarizer to support health coaches in keeping track of the goals and a text-units-text dialogue generation model that converses with patients and helps them create and accomplish specific goals for physical activities. Our models outperform previous state-of-the-art while eliminating the need for predefined schema and corresponding annotation. We also propose a new health coaching dataset extending previous work and a metric to measure the unconventionality of the patient's response based on data difficulty, facilitating potential coach alerts during deployment., Comment: Accepted to the main conference of LREC-COLING 2024

Health coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly personalized and labor-intensive nature. In this paper, we propose to build a dialogue system that converses with the patients, helps them create and accomplish specific goals, and can address their emotions with empathy. However, building such a system is challenging since real-world health coaching datasets are limited and empathy is subtle. Thus, we propose a modularized health coaching dialogue system with simplified NLU and NLG frameworks combined with mechanism-conditioned empathetic response generation. Through automatic and human evaluation, we show that our system generates more empathetic, fluent, and coherent responses and outperforms the state-of-the-art in NLU tasks while requiring less annotation. We view our approach as a key step towards building automated and more accessible health coaching systems., Comment: Accepted to the main conference of COLING 2022

The spin-orbit splitting between neutron 1$p$ orbitals at $^{33}$Si has been deduced using the single-neutron-adding ($d$,$p$) reaction in inverse kinematics with a beam of $^{32}$Si, a long-lived radioisotope. Reaction products were analyzed by the newly implemented SOLARIS spectrometer at the reaccelerated-beam facility at the National Superconducting Cyclotron Laboratory. The measurements show reasonable agreement with shell-model calculations that incorporate modern cross-shell interactions, but they contradict the prediction of proton density depletion based on relativistic mean-field theory. The evolution of the neutron 1$p$-shell orbitals is systematically studied using the present and existing data in the isotonic chains of $N=17$, 19, and 21. In each case, a smooth decrease in the separation of the $1p_{3/2}$-$1p_{1/2}$ orbitals is seen as the respective $p$-orbitals approach zero binding, suggesting that the finite nuclear potential strongly influences the evolution of nuclear structure in this region., Comment: 10 pages, 5 figures

Demonstrations of topological photonics have so far largely been confined to infrared wavelengths where imaging technology and access to low-dimensional quantum materials are both limited. Here, we designed and fabricated silicon nitride ring-resonator arrays to demonstrate photonic topological edge states at ~780 nm. We observed edge states corresponding to the integer quantum Hall Hamiltonian with topological protection against fabrication disorder. This demonstration extends the concept of topological edge states to the near-visible regime and paves the way for nonlinear and non-Hermitian topological photonics with the rich library of near-visible quantum emitters., Comment: 7 pages, 4 figures

The impact of using artificial intelligence (AI) to guide patient care or operational processes is an interplay of the AI model's output, the decision-making protocol based on that output, and the capacity of the stakeholders involved to take the necessary subsequent action. Estimating the effects of this interplay before deployment, and studying it in real time afterwards, are essential to bridge the chasm between AI model development and achievable benefit. To accomplish this, the Data Science team at Stanford Health Care has developed a Testing and Evaluation (T&E) mechanism to identify fair, useful and reliable AI models (FURM) by conducting an ethical review to identify potential value mismatches, simulations to estimate usefulness, financial projections to assess sustainability, as well as analyses to determine IT feasibility, design a deployment strategy, and recommend a prospective monitoring and evaluation plan. We report on FURM assessments done to evaluate six AI guided solutions for potential adoption, spanning clinical and operational settings, each with the potential to impact from several dozen to tens of thousands of patients each year. We describe the assessment process, summarize the six assessments, and share our framework to enable others to conduct similar assessments. Of the six solutions we assessed, two have moved into a planning and implementation phase. Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.

Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability. In this context, an evaluation of the robustness of such biomarkers is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to reproduce (same data, same method) and replicate (different data or method) the models described in Nguyen et al., 2021 to predict individual's PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson's Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in Nguyen et al.,2021 and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. The success of the reproduction was assessed using different criteria. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 > 0), which is consistent with its findings. The challenges encountered while reproducing and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.

High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4m Blanco telescope. Since Feb 2019, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day cadences in the $(u)gri(z)$ bands, over a collective sky area of $\sim 38$ deg${\rm ^2}$. The main science goal of HELM is to provide high-quality optical light curves for a large sample of active galactic nuclei (AGNs), and to build decades-long time baselines when combining past and future optical light curves in these legacy fields. These optical images and light curves will facilitate the measurements of AGN reverberation mapping lags, as well as studies of AGN variability and its dependences on accretion properties. In addition, the time-resolved and coadded DECam photometry will enable a broad range of science applications from galaxy evolution to time-domain science. We describe the design and implementation of the program and present the first data release that includes source catalogs and the first $\sim 3.5$ years of light curves during 2019A--2022A., Comment: 11 pages, 8 figures, 4 tables, submitted to ApJS. Median source catalogs and light curves of individual objects are publicly available at https://ariel.astro.illinois.edu/helm/

We classify low-energy $\alpha$-harmonic maps from a closed non-spherical Riemannian surface $\Sigma$ of constant curvature to the round sphere via their bubble scales and centres. In particular we show that as $1<\alpha\downarrow 1$ and assuming $E_\alpha$ is close to $| \Sigma|+4\pi$ then degree-one $\alpha$-harmonic maps blow a bubble based at a critical point $a_c$ of a an explicit function $\mathcal{J}$ and at scale $\sqrt{ |\mathcal{J}(a_c)|^{-1}(\alpha-1)}$. Up to a constant, $\mathcal{J}$ is the sum of the squares of any $L^2$-orthonormal basis of holomorphic one-forms on the domain., Comment: 33 pages, minor typographical corrections

There are two common ways in which developers are incorporating proprietary and domain-specific data when building applications of Large Language Models (LLMs): Retrieval-Augmented Generation (RAG) and Fine-Tuning. RAG augments the prompt with the external data, while fine-Tuning incorporates the additional knowledge into the model itself. However, the pros and cons of both approaches are not well understood. In this paper, we propose a pipeline for fine-tuning and RAG, and present the tradeoffs of both for multiple popular LLMs, including Llama2-13B, GPT-3.5, and GPT-4. Our pipeline consists of multiple stages, including extracting information from PDFs, generating questions and answers, using them for fine-tuning, and leveraging GPT-4 for evaluating the results. We propose metrics to assess the performance of different stages of the RAG and fine-Tuning pipeline. We conduct an in-depth study on an agricultural dataset. Agriculture as an industry has not seen much penetration of AI, and we study a potentially disruptive application - what if we could provide location-specific insights to a farmer? Our results show the effectiveness of our dataset generation pipeline in capturing geographic-specific knowledge, and the quantitative and qualitative benefits of RAG and fine-tuning. We see an accuracy increase of over 6 p.p. when fine-tuning the model and this is cumulative with RAG, which increases accuracy by 5 p.p. further. In one particular experiment, we also demonstrate that the fine-tuned model leverages information from across geographies to answer specific questions, increasing answer similarity from 47% to 72%. Overall, the results point to how systems built using LLMs can be adapted to respond and incorporate knowledge across a dimension that is critical for a specific industry, paving the way for further applications of LLMs in other industrial domains.

There is a large and long-standing gap in education outcomes between pupils from economically disadvantaged backgrounds and their peers in England (EEF, 2018; Hutchinson et al., 2020). Although there is some evidence of improvement since 2011, the gap has widened recently, largely as a result of school closures during the COVID-19 pandemic (Tuckett et al., 2022; Howard et al., 2021; Rose et al., 2021). The Government is committed to improving outcomes for disadvantaged pupils (HM Treasury, 2021). The disadvantaged pupils' attainment gap is a key metric to assess progress in narrowing the gaps between disadvantaged pupils and their peers. To discuss the implications and suggest the best ways forward, the National Foundation for Educational Research (NFER) convened a roundtable in summer 2022 of experts involved in policy and research on economic disadvantage and education. This followed on from prior research by NFER exploring these issues in detail. The roundtable discussion informed the development of three key policy recommendations: (1) The Government should explore the feasibility of establishing a household income-based measure of disadvantage for the future; (2) The Government should explore the feasibility of introducing a 'continuity measure' of disadvantage from 2024 onwards. This would be based on the underlying eligibility criteria for FSM, and remove the effect of the transitional arrangements; and (3) The Government should consider replacing the current rank-based disadvantaged pupils' attainment gap measure with a simpler metric based on average point scores. [For "Investigating the Changing Landscape of Pupil Disadvantage: Exploring the Implications of the Pandemic and the Roll-Out of Universal Credit on Measures of Pupil Disadvantage, the Attainment Gap and School Funding," see ED618981.]