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Paraeducators are among the largest categories of public education employees and are increasingly seen as a pool of potential teachers. However, little is known about paraeducator-to-teacher transitions. Using statewide administrative data, we show that while paraeducators may be more racially/ethnically diverse than the teacher workforce, Black and Hispanic paraeducators are less likely than White paraeducators to transition into teaching. We additionally show that teachers with paraeducator experience are similarly effective to teachers without paraeducator experience. Lastly, we use simulations to show that the potential for the paraeducator-to-teacher pipeline to diversify the teaching profession may be limited unless they are highly targeted. Our results have policy design implications for efforts to expand the paraeducator-to-teacher pipeline or to diversify the teacher workforce.

Attracting and retaining high-quality teachers is a pressing policy concern. Increasing teacher salaries and creating more attractive compensation packages are often proposed as a potential solution. Signed into law in March 2023, the LEARNS Act increased Arkansas's minimum teacher salary from $36,000 to $50,000, guaranteed all teachers a minimum raise of $2,000, and added flexibility allowing school districts to deviate from seniority-based traditional salary schedules. To study school districts' adjustments to the new legislation, we collected information about districts' teacher compensation policies one year before and the first year of implementation. We also integrated this data with teachers' administrative records to study patterns of teacher retention and mobility. Our results reveal a more equitable distribution of starting teacher salaries across districts, with minimal variation. The LEARNS Act notably increased funding for rural and high-poverty districts, mitigating the negative association between starting salaries and district poverty rates. However, the initial effects on teacher retention and mobility were modest. While some positive trends emerged, such as reduced probabilities of teachers transitioning to non-instructional roles and increased new teacher placement in geographic areas of shortage, broader impacts on retention and mobility were limited in the first year of implementation.

Many believe that use of generative AI as a private tutor has the potential to shrink access and achievement gaps between students and schools with abundant resources versus those with fewer resources. Shrinking the gap is possible only if paid and free versions of the platforms perform with the same accuracy. In this experiment, we investigate the performance of GPT versions 3.5, 4.0, and 4o-mini on the same 16-question statistics exam given to a class of first-year graduate students. While we do not advocate using any generative AI platform to complete an exam, the use of exam questions allows us to explore aspects of ChatGPT's responses to typical questions that students might encounter in a statistics course. Results on accuracy indicate that GPT 3.5 would fail the exam, GPT4 would perform well, and GPT4o-mini would perform somewhere in between. While we acknowledge the existence of other Generative AI/LLMs, our discussion concerns only ChatGPT because it is the most widely used platform on college campuses at this time. We further investigate differences among the AI platforms in the answers for each problem using methods developed for text analytics, such as reading level evaluation and topic modeling. Results indicate that GPT3.5 and 4o-mini have characteristics that are more similar than either of them have with GPT4., Comment: 24 pages, 2 figures, 3 tables. Submitted for publication August, 2024; revision submitted January 2025

The productive career of a professional athlete is limited compared to the normal human lifespan. Most professional athletes have retired by age 40. The early retirement age is due to a combination of age-related performance and life considerations. While younger players typically are stronger and faster than their older teammates, older teammates add value to a team due to their experience and perspective. Indeed, the highest--paid major league baseball players are those over the age of 35. These players contribute intangibly to a team through mentorship of younger players; however, their peak athletic performance has likely passed. Given this, it is of interest to learn how more mature players contribute to a team in measurable ways. We examine the distribution of plate appearance outcomes from three different age groups as compositional data, using Layered Dirichlet Modeling (LDM). We develop a hypothesis testing framework to compare the average proportions of outcomes for each component among 3 of more groups. LDM can not only determine evidence for differences among populations, but also pinpoint within which component the largest changes are likely to occur. This framework can determine where players can be of most use as they age., Comment: 21 pages, 4 tables, 8 figures, submitted for publication September 2024

A college education historically has been seen as method of moving upward with regards to income brackets and social status. Indeed, many colleges recognize this connection and seek to enroll talented low income students. While these students might have their education, books, room, and board paid; there are other items that they might be expected to use that are not part of most college scholarship packages. One of those items that has recently surfaced is access to generative AI platforms. The most popular of these platforms is ChatGPT, and it has a paid version (ChatGPT4) and a free version (ChatGPT3.5). We seek to explore differences in the free and paid versions in the context of homework questions and data analyses as might be seen in a typical introductory statistics course. We determine the extent to which students who cannot afford newer and faster versions of generative AI programs would be disadvantaged in terms of writing such projects and learning these methods., Comment: Originally submitted for review in May of 2024 but rejected 6 months later

Ram pressure stripped galaxies are rare cases of environmental evolution in action. However, our ability to understand these galaxies is limited by the small number of identified galaxies experiencing ram pressure stripping (RPS). Our aim is to explore the efficacy of citizen science classifications in identifying ram pressure stripped galaxies, and use this to aid in motivating new samples of ram pressure stripped candidates. We compile a sample of over 200 known ram pressure stripped galaxies from existing literature, with morphological classifications obtained from Galaxy Zoo. We compare these galaxies with magnitude and redshift-matched comparison cluster and field galaxies. Additionally, we create a sample of SDSS cluster galaxies, with morphological classifications similar to known ram pressure stripped galaxies, and compare the fraction of potential new RPS candidates against control samples. We find that ram pressure stripped galaxies exhibit a higher proportion of odd and irregular morphological classifications compared to field and cluster comparison samples. This trend is particularly pronounced in galaxies displaying strong optical ram pressure stripping features, but absent from galaxies with only radio tails. We find that SDSS galaxies with Galaxy Zoo classifications consistent with the known RPS galaxies have a higher fraction of visible ram pressure stripping features ($19\%$) compared with other cluster galaxies ($12\%$) when classified by experts. We identify 101 new ram pressure stripping candidate galaxies through these expert classifications. We demonstrate that indirect morphological classifications from citizen science projects can increase the efficiency in which new stripping candidates are found. Projects such as Galaxy Zoo can aid in the identification of ram pressure stripped galaxies that are key to understanding galaxy evolution in clusters., Comment: 20 pages, 11 figures, accepted for publication in Astronomy and Astrophysics

Galaxies within groups exhibit characteristics different from those of galaxies that reside in regions of average density (the field). Galaxy properties also depend on their location within the host structure and orientation with respect to the central galaxy: galaxies in the inner regions that are aligned to the major axis of the central galaxy tend to be more quenched and redder than galaxies in the outskirts and with random orientation. This phenomenon, called anisotropic satellite galaxy quenching (ASGQ), can be explained in two different ways: invoking either external influences (large-scale distribution of matter) or internal factors (black hole activity of the central galaxy). In this work, we study the impact of filaments in shaping the ASGQ in the local Universe, exploiting the magneto-hydrodynamic (MHD) simulation IllustrisTNG. We separated all surviving satellites into young and old populations depending on their infall times. We show that only young satellites contribute to the observed ASGQ. These satellites preferentially infall along the major axis of the central galaxy, which tends to have the same direction of the filament feeding the groups. We demonstrate that old satellites were quenched inside their hosts and do not exhibit signatures of ASGQ. We show that the ASGQ emerges at the time of the infall of the young satellites and is also visible outside $\rm R_{200}$. In contrast, there is no sign of anisotropic distribution in the inner regions ($R<0.5 R_{200}$). We argue that our results support a scenario in which a large-scale structure is imprinted on the ASGQ., Comment: accepted to A&A

Many physical models contain nuisance parameters that quantify unknown and irrelevant properties of an experiment. Typically, these cannot be measured except by fitting the models to the data from the experiment, requiring simultaneous measurement of interesting parameters that are our target of inference and nuisance terms that are not directly of interest. A recent example of this is fitting Effective Field Theory (EFT) models to large-scale structure (LSS) data to make cosmological inferences. These models have a large number of nuisance parameters that are typically correlated with cosmological parameters in the posterior, leading to strong dependence on the nuisance parameter priors. We introduce a reparametrization method that leverages Generalized Additive Models (GAMs) to decorrelate nuisance parameters from the parameters of interest in the likelihood, even in the presence of non-linear relationships. This reparametrization forms a natural basis within which to define priors that are independent between nuisance and target parameters: the separation means that the marginal posterior for cosmological parameters does not depend on simple priors placed on nuisance terms. In application to EFT models using LSS data, we demonstrate that the proposed approach leads to robust cosmological inference., Comment: 24 pages, 9 figures, 5 tables

Modern AI (i.e., Deep Learning and its variants) is here to stay. However, its enigmatic black box nature presents a fundamental challenge to the traditional methods of test and validation (T&E). Or does it? In this paper we introduce a Digital Engineering (DE) approach to T&E (DE-T&E), combined with generative AI, that can achieve requisite mil spec statistical validation as well as uncover potential deleterious Black Swan events that might otherwise not be uncovered until it is too late. An illustration of these concepts is presented for an advanced modern radar example employing deep learning AI.

In this Technical Design Report (TDR) we describe the SABRE South detector to be built at the Stawell Underground Physics Laboratory (SUPL). The SABRE South detector is designed to test the long-standing DAMA/LIBRA signal of an annually modulating rate consistent with dark matter by using the same target material. SABRE South uses seven ultra-high purity NaI(Tl) crystals (with a total target mass of either 35 kg or 50 kg), hermetically sealed in copper enclosures that are suspended within a liquid scintillator active veto. High quantum efficiency and low background Hamamatsu R11065 photomultiplier tubes are directly coupled to both ends of the crystal, and enclosed with the crystal in an oxygen free high thermal conductivity copper enclosure. The active veto system consists of 11.6 kL of linear alkylbenzene (LAB) doped with a mixture of fluorophores and contained in a steel vessel, which is instrumented with at least 18 Hamamatsu R5912 photomultipliers. The active veto tags key radiogenic backgrounds intrinsic to the crystals, such as ${^{40}}$K, and is expected to suppress the total background by 27% in the 1-6 keV region of interest. In addition to the liquid scintillator veto, a muon veto is positioned above the detector shielding. This muon veto consists of eight EJ-200 scintillator modules, with Hamamatsu R13089 photomultipliers coupled to both ends. With an expected total background of 0.72 cpd/kg/keV, SABRE South can test the DAMA/LIBRA signal with 5$\sigma$ discovery or 3$\sigma$ exclusion after two years of data taking.

CHANCES, the CHileAN Cluster galaxy Evolution Survey, will study the evolution of galaxies in and around ${\sim}$150 massive galaxy clusters, from the local universe out to z=0.45. CHANCES will use the new 4MOST Spectroscopic Survey Facility on the VISTA 4m telescope to obtain spectra for ${\sim}$500,000 galaxies with magnitudes $r_\mathrm{AB} < 20.5$, providing comprehensive spectroscopic coverage of each cluster out to $5r_{200}$. Its wide and deep scope will trace massive and dwarf galaxies from the surrounding filaments and groups to the cores of galaxy clusters, enabling the study of galaxy pre-processing and the role of the evolving environment on galaxy evolution. In this paper we present and characterize the sample of clusters and superclusters to be targeted by CHANCES. We used literature catalogues based on X-ray emission and Sunyaev-Zel'dovich effect to define the cluster sample in a homogeneous way, with attention to cluster mass and redshift, as well as the availability of ancillary data. We calibrated literature mass estimates from various surveys against each other and provide an initial mass estimate for each cluster, which we used to define the radial extent of the 4MOST coverage. We also present an initial assessment of the structure surrounding these clusters based on the redMaPPer red-sequence algorithm as a preview of some of the science CHANCES will enable., Comment: 11 pages, 9 figures, plus references and appendix containing catalog tables, submitted to A&A

We investigate the unusual H$\alpha$ features found towards the Scutum Supershell via recent arc-minute and arc-second resolution imaging. These multi-degree features resemble a long central spine ending in a bow-shock morphology. We performed a multi-wavelength study in [SII] optical, radio continuum, infrared continuum, HI, CO, X-ray and gamma-ray emissions. Interestingly, we found the Galactic worm GW16.9$-$3.8 HI feature appears within the Scutum Supershell, and likely influences the spine morphology. Furthermore, the rightmost edge of the bow-shock H$\alpha$ emission overlaps with [S II] line emission, 4.85 GHz radio, and both 60$\mu$m and 100$\mu$m infrared continuum emissions, suggesting some potential for excitation by shock heating. We estimated the photo-ionisation from O-type and B-type stars in the region (including those from the OB associations Ser OB1B, Ser OB2 and Sct OB3) and found that this mechanism could supply the excitation to account for the observed H$\alpha$ luminosity of the spine and bow-shock of $\sim$1e36 - 2e36 erg/s (d/2.5 kpc)$^2$. Recent MHD simulations by Drozdov et al. (2022) demonstrate the potential for supernova events to drive outflow and bow-shock types of features of the same energetic nature and physical scale as the H$\alpha$ emission we observe here. While this clearly requires many supernova events over time, we speculate that one contributing event could have come from the presumably energetic supernova (hypernova) birth of the magnetar tentatively identified in the X-ray binary LS 5039., Comment: Accepted to be published by Astronomical Society of Australia (PASA)

With the advent of the next generation of astrophysics experiments, the volume of data available to researchers will be greater than ever. As these projects will significantly drive down statistical uncertainties in measurements, it is crucial to develop novel tools to assess the ability of our models to fit these data within the specified errors. We introduce to astronomy the Leave One Out-Probability Integral Transform (LOO-PIT) technique. This first estimates the LOO posterior predictive distributions based on the model and likelihood distribution specified, then evaluates the quality of the match between the model and data by applying the PIT to each estimated distribution and data point, outputting a LOO-PIT distribution. Deviations between this output distribution and that expected can be characterised visually and with a standard Kolmogorov--Smirnov distribution test. We compare LOO-PIT and the more common $\chi^2$ test using both a simplified model and a more realistic astrophysics problem, where we consider fitting Baryon Acoustic Oscillations in galaxy survey data with contamination from emission line interlopers. LOO-PIT and $\chi^2$ tend to find different signals from the contaminants, and using these tests in conjunction increases the statistical power compared to using either test alone. We also show that LOO-PIT outperforms $\chi^2$ in certain realistic test cases., Comment: 28 pages, 13 figures. Prepared for submission to JCAP. Comments welcomed

An important goal of environmental epidemiology is to quantify the complex health risks posed by a wide array of environmental exposures. In analyses focusing on a smaller number of exposures within a mixture, flexible models like Bayesian kernel machine regression (BKMR) are appealing because they allow for non-linear and non-additive associations among mixture components. However, this flexibility comes at the cost of low power and difficult interpretation, particularly in exposomic analyses when the number of exposures is large. We propose a flexible framework that allows for separate selection of additive and non-additive effects, unifying additive models and kernel machine regression. The proposed approach yields increased power and simpler interpretation when there is little evidence of interaction. Further, it allows users to specify separate priors for additive and non-additive effects, and allows for tests of non-additive interaction. We extend the approach to the class of multiple index models, in which the special case of kernel machine-distributed lag models are nested. We apply the method to motivating data from a subcohort of the Human Early Life Exposome (HELIX) study containing 65 mixture components grouped into 13 distinct exposure classes.

Quasi-periodic Eruptions (QPEs) are luminous bursts of soft X-rays from the nuclei of galaxies, repeating on timescales of hours to weeks. The mechanism behind these rare systems is uncertain, but most theories involve accretion disks around supermassive black holes (SMBHs), undergoing instabilities or interacting with a stellar object in a close orbit. It has been suggested that this disk could be created when the SMBH disrupts a passing star, implying that many QPEs should be preceded by observable tidal disruption events (TDEs). Two known QPE sources show long-term decays in quiescent luminosity consistent with TDEs, and two observed TDEs have exhibited X-ray flares consistent with individual eruptions. TDEs and QPEs also occur preferentially in similar galaxies. However, no confirmed repeating QPEs have been associated with a spectroscopically confirmed TDE or an optical TDE observed at peak brightness. Here we report the detection of nine X-ray QPEs with a mean recurrence time of approximately 48 hours from AT2019qiz, a nearby and extensively studied optically-selected TDE. We detect and model the X-ray, ultraviolet and optical emission from the accretion disk, and show that an orbiting body colliding with this disk provides a plausible explanation for the QPEs.

Gamma-ray bursts (GRBs) are intense, short-lived bursts of gamma-ray radiation observed up to a high redshift ($z \sim 10$) due to their luminosities. Thus, they can serve as cosmological tools to probe the early Universe. However, we need a large sample of high$-z$ GRBs, currently limited due to the difficulty in securing time at the large aperture Telescopes. Thus, it is painstaking to determine quickly whether a GRB is high$z$ or low$-z$, which hampers the possibility of performing rapid follow-up observations. Previous efforts to distinguish between high$-$ and low$-z$ GRBs using GRB properties and machine learning (ML) have resulted in limited sensitivity. In this study, we aim to improve this classification by employing an ensemble ML method on 251 GRBs with measured redshifts and plateaus observed by the Neil Gehrels Swift Observatory. Incorporating the plateau phase with the prompt emission, we have employed an ensemble of classification methods to enhance the sensitivity unprecedentedly. Additionally, we investigate the effectiveness of various classification methods using different redshift thresholds, $z_{threshold}$=$z_t$ at $z_{t}=$ 2.0, 2.5, 3.0, and 3.5. We achieve a sensitivity of 87\% and 89\% with a balanced sampling for both $z_{t}=3.0$ and $z_{t}=3.5$, respectively, representing a 9\% and 11\% increase in the sensitivity over Random Forest used alone. Overall, the best results are at $z_{t} = 3.5$, where the difference between the sensitivity of the training set and the test set is the smallest. This enhancement of the proposed method paves the way for new and intriguing follow-up observations of high$-z$ GRBs., Comment: 45 pages, 16 Figures (6 Figures with single panel, 4 Figures with 8 panels, 2 Figures with 4 panels, 1 Figure with 12 panels, 1 figure with 6 panels, 2 Figure with 2 panels), 6 Tables, accepted for publication in ApJS

The persistence of the DAMA/LIBRA (DAMA) modulation over the past two decades has been a source of great contention within the dark matter community. The DAMA collaboration reports a persistent, modulating event rate within their setup of NaI(Tl) scintillating crystals at the INFN Laboratori Nazionali del Gran Sasso (LNGS) underground laboratory. A recent work alluded that this signal could have arisen due to an analysis artefact, caused by DAMA not accounting for time variation of decaying background radioisotopes in their analysis procedure. In this work, we examine in detail this 'induced modulation' effect, arguing that a number of aspects of the DAMA signal are incompatible with an induced modulation arising from decays of background isotopes over the lifetime of the experiment. Using a toy model of the DAMA/LIBRA experiment, we explore the induced modulation effect under different variations of the activities of the relevant isotopes - namely, $^3$H and $^{210}$Pb - highlighting the various inconsistencies between the resultant toy datasets and the DAMA signal. We stress the importance of the SABRE experiment, whose goal is to unambiguously test for the presence of such a modulating signal in an experiment using the same target material and comparable levels of background.

Retirement plans can create strong financial incentives that have important labor market implications, and many states have adopted alternative plan designs that significantly change these incentives. The authors use longitudinal data to investigate the impact of Washington State's 1996 introduction of a hybrid retirement plan on late-career attrition. The unique setup of Washington's plans allows them to provide empirical evidence on the influence of financial incentives created by statutory retirement eligibility thresholds. Findings show that despite facing very different financial incentives, teachers enrolled in the hybrid and traditional plans respond similarly to reaching a key retirement eligibility threshold. The authors hypothesize that teachers are anchoring to the eligibility thresholds, muting the influence of the financial incentives. They also provide evidence that, in the presence of bright-line eligibility thresholds that can anchor workers' separation behavior, commonly used structural models may overpredict workers' responsiveness to the financial incentives embedded in retirement plans. [This paper will be published in "ILR Review."]

In early 2020, the global COVID-19 pandemic closed schools for the rest of the 2019-20 school year (fiscal year 2020 or FY20). The United States Congress deployed funds to help K-12 schools adjust and plan for reopening via the Coronavirus Aid, Relief, and Economic Security (CARES) Act, including the initial $13.2 billion installment through the Elementary and Secondary School Emergency Relief Fund (ESSER I) and initial $2.95 billion installment through the Governor's Emergency Education Relief (GEER I) Fund. Private sector and non-profit organizations, including charter schools, were also eligible for loans through the Small Business Administration's Paycheck Protection Program (PPP), most of which were partially or fully forgiven. In this report, we extend our analysis of school funding during FY20 from the 2023 report, "Charter School Funding: Little Progress Toward Equity in the City." In that report, we found that, on average, charter schools receive about 30 percent ($7,147) less funding per pupil compared to traditional public schools (TPS). That analysis excluded COVID relief funding. Here we compare the initial emergency COVID relief funds received by TPS and charter schools in 18 US Cities: Atlanta, Georgia; Boston, Massachusetts; Camden, New Jersey; Chicago, Illinois; Denver, Colorado; Detroit, Michigan; Houston, Texas; Indianapolis, Indiana; Little Rock, Arkansas; Memphis, Tennessee; New Orleans, Louisiana; New York City, New York; Oakland, California; Phoenix, Arizona; San Antonio, Texas; Tulsa, Oklahoma; and Washington, DC. We use data from federal and state sources to address the following research questions: (1) did emergency COVID relief funds allocated to publicly-funded schools in FY20 widen or narrow the preexisting charter school funding gap? and (2) was initial COVID relief funding (allocated for FY20) distributed equitably relative to student poverty?

Charter schools are public schools that operate free from some government regulations in return for a commitment to achieve a set of student outcomes specified in their charter. Nearly 8,000 public charter schools enrolled 3.7 million students in the U.S. in 2020-21. In major cities, charter schools receive less funding per pupil compared to traditional public schools (TPS). Charter schools also use their funding more efficiently, achieving better short- and long-term outcomes per dollar invested, relative to TPS. In this study, the authors reexamine the productivity of publicly funded schools, using funding data from the charter school revenue report "Charter School Funding: Little Progress Towards Equity in the City." The authors also use achievement data from the Center for Research on Educational Outcomes' (CREDO's) city and national studies, the NAEP Data Explorer, and wage data from the Bureau of Labor Statistics. The authors also have access to complete data for nine cities: Camden, New Jersey; Denver, Colorado; Houston, Texas; Indianapolis, Indiana; Memphis, Tennessee; New Orleans, Louisiana; New York City, New York; San Antonio, Texas; and Washington, DC. They found that charter schools demonstrate an approximately 40 percent higher level of cost-effectiveness than TPS on average across nine cities.

The development of low-frequency radio astronomy experiments for detecting 21-cm line emission from hydrogen presents new opportunities for creative solutions to the challenge of characterizing an antenna beam pattern. The Array of Long Baseline Antennas for Taking Radio Observations from the Seventy-ninth parallel (ALBATROS) is a new radio interferometer sited in the Canadian high Arctic that aims to map Galactic foregrounds at frequencies below $\sim$30 MHz. We present PteroSoar, a custom-built hexacopter outfitted with a transmitter, that will be used to characterize the beam patterns of ALBATROS and other experiments. The PteroSoar drone hardware is motivated by the need for user-servicing at remote sites and environmental factors that are unique to the high Arctic. In particular, magnetic heading is unreliable because the magnetic field lines near the north pole are almost vertical. We therefore implement moving baseline real time kinematic (RTK) positioning with two GPS units to obtain heading solutions with $\sim$1$^\circ$ accuracy. We present a preliminary beam map of an ALBATROS antenna, thus demonstrating successful PteroSoar operation in the high Arctic.

The WHT Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph. This new instrument will be exploited to obtain high S/N spectra of $\sim$25000 galaxies at intermediate redshifts for the WEAVE Stellar Population Survey (WEAVE-StePS). We test machine learning methods for retrieving the key physical parameters of galaxies from WEAVE-StePS-like spectra using both photometric and spectroscopic information at various S/Ns and redshifts. We simulated $\sim$105000 galaxy spectra assuming SFH with an exponentially declining star formation rate, covering a wide range of ages, stellar metallicities, sSFRs, and dust extinctions. We then evaluated the ability of the random forest and KNN algorithms to correctly predict such parameters assuming no measurement errors. We checked how much the predictive ability deteriorates for different S/Ns and redshifts, finding that both algorithms still accurately estimate the ages and metallicities with low bias. The dispersion varies from 0.08-0.16 dex for ages and 0.11-0.25 dex for metallicity, depending on the redshift and S/N. For dust attenuation, we find a similarly low bias and dispersion. For the sSFR, we find a very good constraining power for star-forming galaxies, log sSFR$\gtrsim$ -11, where the bias is $\sim$ 0.01 dex and the dispersion is $\sim$ 0.10 dex. For more quiescent galaxies, with log sSFR$\lesssim$ -11, we find a higher bias, 0.61-0.86 dex, and a higher dispersion, $\sim$ 0.4 dex, for different S/Ns and redshifts. Generally, we find that the RF outperforms the KNN. Finally, the retrieved sSFR was used to successfully classify galaxies as part of the blue cloud, green valley, or red sequence. We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies even at relatively low S/N=10 per angstrom spectra with available ancillary photometric information., Comment: 19 pages, 10 + 2 figures, 4 tables, accepted in A&A

As TeV gamma-ray astronomy progresses into the era of the Cherenkov Telescope Array (CTA), instantaneously following up on gamma-ray transients is becoming more important than ever. To this end, a worldwide network of Imaging Atmospheric Cherenkov Telescopes has been proposed. Australia is ideally suited to provide coverage of part of the Southern Hemisphere sky inaccessible to H.E.S.S. in Namibia and the upcoming CTA-South in Chile. This study assesses the sources detectable by a small, transient-focused array in Australia based on CTA telescope designs. The TeV emission of extragalactic sources (including the majority of gamma-ray transients) can suffer significant absorption by the extragalactic background light. As such, we explored the improvements possible by implementing stereoscopic and topological triggers, as well as lowered image cleaning thresholds, to access lower energies. We modelled flaring gamma-ray sources based on past measurements from the satellite-based gamma-ray telescope Fermi-LAT. We estimate that an array of four Medium-Sized Telescopes (MSTs) would detect $\sim$24 active galactic nucleus flares >5$\sigma$ per year, up to a redshift of $z\approx1.5$. Two MSTs achieved $\sim$80-90% of the detections of four MSTs. The modelled Galactic transients were detectable within the observation time of one night, 11 of the 21 modelled gamma-ray bursts were detectable, as were $\sim$10% of unidentified transients. An array of MST-class telescopes would thus be a valuable complementary telescope array for transient TeV gamma-ray astronomy., Comment: 13 pages, 13 figures, 4 tables, accepted for publication in PASA

This study examines charter school funding equity in 18 cities using 2019-20 data, the most recent available. The study uses official school district and state budget documents to capture every dollar flowing to schools, including in-kind services. The study answers the following questions: (1) What is the difference in per-pupil revenue between traditional public schools (TPS) and charter schools in major US cities in the 2019-20 school year? (2) Do differences in student demographics explain any funding differences between TPS and charter schools? (3) Does the relationship between TPS and charter school funding vary across categories of school revenue? and (4) Which cities are driving the results? Findings reveal that: (1) Charter school students across 18 cities receive an average of 30 percent less funding per pupil than students in TPS in 17 of those cities; (2) About 65 percent of the underfunding of public charter schools in the cities can be attributed to lower enrollments of students with disabilities in the charter sector; and (3) Charter schools receive less local funding, state funding, federal funding, and nonpublic funding than TPS.

Recent advancements in thin-film lithium niobate (TFLN) photonics have led to a new generation of high-performance electro-optic devices, including modulators, frequency combs, and microwave-to-optical transducers. However, the broader adoption of TFLN-based devices that rely on all-optical nonlinearities have been limited by the sensitivity of quasi-phase matching (QPM), realized via ferroelectric poling, to fabrication tolerances. Here, we propose a scalable fabrication process aimed at improving the wavelength-accuracy of optical frequency mixers in TFLN. In contrast to the conventional pole-before-etch approach, we first define the waveguide in TFLN and then perform ferroelectric poling. This sequence allows for precise metrology before and after waveguide definition to fully capture the geometry imperfections. Systematic errors can also be calibrated by measuring a subset of devices to fine-tune the QPM design for remaining devices on the wafer. Using this method, we fabricated a large number of second harmonic generation devices aimed at generating 737 nm light, with 73% operating within 5 nm of the target wavelength. Furthermore, we also demonstrate thermo-optic tuning and trimming of the devices via cladding deposition, with the former bringing ~96% of tested devices to the target wavelength. Our technique enables the rapid growth of integrated quantum frequency converters, photon pair sources, and optical parametric amplifiers, thus facilitating the integration of TFLN-based nonlinear frequency mixers into more complex and functional photonic systems.

Globally, artificial intelligence (AI) implementation is growing, holding the capability to fundamentally alter organisational processes and decision making. Simultaneously, this brings a multitude of emergent risks to organisations, exposing vulnerabilities in their extant risk management frameworks. This necessitates a greater understanding of how organisations can position themselves in response. This issue is particularly pertinent within the financial sector with relatively mature AI applications matched with severe societal repercussions of potential risk events. Despite this, academic risk management literature is trailing behind the speed of AI implementation. Adopting a management perspective, this study aims to contribute to the understanding of AI risk management in organisations through an exploratory empirical investigation into these practices. In-depth insights are gained through interviews with nine practitioners from different organisations within the UK financial sector. Through examining areas of organisational convergence and divergence, the findings of this study unearth levels of risk management framework readiness and prevailing approaches to risk management at both a processual and organisational level. Whilst enhancing the developing literature concerning AI risk management within organisations, the study simultaneously offers a practical contribution, providing key areas of guidance for practitioners in the operational development of AI risk management frameworks.

In radiotherapy, 2D orthogonally projected kV images are used for patient alignment when 3D-on-board imaging(OBI) unavailable. But tumor visibility is constrained due to the projection of patient's anatomy onto a 2D plane, potentially leading to substantial setup errors. In treatment room with 3D-OBI such as cone beam CT(CBCT), the field of view(FOV) of CBCT is limited with unnecessarily high imaging dose, thus unfavorable for pediatric patients. A solution to this dilemma is to reconstruct 3D CT from kV images obtained at the treatment position. Here, we propose a dual-models framework built with hierarchical ViT blocks. Unlike a proof-of-concept approach, our framework considers kV images as the solo input and can synthesize accurate, full-size 3D CT in real time(within milliseconds). We demonstrate the feasibility of the proposed approach on 10 patients with head and neck (H&N) cancer using image quality(MAE: <45HU), dosimetrical accuracy(Gamma passing rate (2%/2mm/10%)>97%) and patient position uncertainty(shift error: <0.4mm). The proposed framework can generate accurate 3D CT faithfully mirroring real-time patient position, thus significantly improving patient setup accuracy, keeping imaging dose minimum, and maintaining treatment veracity., Comment: 17 pages, 8 figures and tables

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

Augmented Reality (AR) provides a safe and low-cost option for hazardous safety training that allows for the visualization of aspects that may be invisible, such as radiation. Effectively visually communicating such threats in the environment around the user is not straightforward. This work describes visually encoding radiation using the spatial awareness mesh of an AR Head Mounted Display. We leverage the AR device's GPUs to develop a real time solution that accumulates multiple dynamic sources and uses stencils to prevent an environment being over saturated with a visualization, as well as supporting the encoding of direction explicitly in the visualization. We perform a user study (25 participants) of different visualizations and obtain user feedback. Results show that there are complex interactions and while no visual representation was statistically superior or inferior, user opinions vary widely. We also discuss the evaluation approaches and provide recommendations.

We employ Bayesian Model Averaging (BMA) as a powerful statistical framework to address key cosmological questions about the universe's fundamental properties. We explore extensions beyond the standard $\Lambda$CDM model, considering a varying curvature density parameter $\Omega_{\rm k}$, a spectral index $\mathrm{n}_{\rm s}=1$ and a varying $n_{\rm run}$, a constant dark energy equation of state (EOS) $w_0$CDM and a time-dependent one $w_0w_a$CDM. We also test cosmological data against a varying effective number of neutrino species $N_{\rm eff}$. Data from different combinations of cosmic microwave background (CMB) data from the last Planck PR4 analysis, CMB lensing from Planck 2018, baryonic acoustic oscillations (BAO) and the Bicep-KECK 2018 results, are used. We find that the standard $\Lambda$CDM model is favoured when combining CMB data with CMB lensing, BAO and Bicep-KECK 2018 data against $K-\Lambda$CDM model $N_{\rm eff}-\Lambda$CDM with a probability $> 80\%$. When investigating the dark energy EOS, we find that this dataset is not able to express a strong preference between the standard $\Lambda$CDM model and the constant dark energy EOS model $w_0$CDM, with an approximately split model posterior probability of $\approx 60\%:40\%$ in favour of $\Lambda$CDM, whereas the time-varying dark energy EOS model is ruled out. Finally, we find that the CMB data alone show a strong preference for a model that includes the running of the spectral index $n_{\rm run}$, with a probability $\approx 90\%$, when compared to the $n_{\rm s}=1$ model and the standard $\Lambda$CDM. Overall, we find that including the model uncertainty in the considered cases does not significantly impact the Hubble tension.

Approximately 20 percent of the public school students in the City of Los Angeles attend a charter school. Los Angeles is home to the second-largest school district in the nation. The School Choice Demonstration Project research team has documented disparities between traditional public school (TPS) and charter school funding in Los Angeles Unified School District (LAUSD) since the 2002-03 school year. Throughout these reports, TPS in LAUSD have consistently received more funding per pupil than charter schools in LAUSD--anywhere from 22 to 40 percent more. In 2013, the California Legislature changed the way public schools had been funded for the last 40 years by establishing the Local Control Funding Formula (LCFF), a funding mechanism intended to increase funding equity. In this report, the authors investigate the status of the TPS-charter school funding gap a year after the full implementation of the LCFF. The authors use official financial documents from the California Department of Education and LAUSD to account for every dollar TPS and charter schools received in the 2019-20 school year, including in-kind services. [Additional funding for this report was provided by City Fund.]

In a typical Morris water maze experiment, a mouse is placed in a circular water tank and allowed to swim freely until it finds a platform, triggering a route of escape from the tank. For reference purposes, the tank is divided into four quadrants: the target quadrant where the trigger to escape resides, the opposite quadrant to the target, and two adjacent quadrants. Several response variables can be measured: the amount of time that a mouse spends in different quadrants of the water tank, the number of times the mouse crosses from one quadrant to another, or how quickly a mouse triggers an escape from the tank. When considering time within each quadrant, it is hypothesized that normal mice will spend smaller amounts of time in quadrants that do not contain the escape route, while mice with an acquired or induced mental deficiency will spend equal time in all quadrants of the tank. Clearly, proportion of time in the quadrants must sum to one and are therefore statistically dependent; however, most analyses of data from this experiment treat time in quadrants as statistically independent. A recent paper introduced a hypothesis testing method that involves fitting such data to a Dirichlet distribution. While an improvement over studies that ignore the compositional structure of the data, we show that methodology is flawed. We introduce a two-sample test to detect differences in proportion of components for two independent groups where both groups are from either a Dirichlet or nested Dirichlet distribution. This new test is used to reanalyze the data from a previous study and come to a different conclusion., Comment: 30 pages, 7 figures

The US Department of Energys Office of Nuclear Energy is planning for an integrated waste management approach to transport, store, and eventually dispose of spent nuclear fuel and other high-level radioactive waste as part of the Integrated Waste Management program. In support of this effort, the Stakeholder Tool for Assessing Radioactive Transportation is being developed within the IWM program. This is a web-based decision support tool that can be used to analyze geospatial data related to the transportation of SNF and HLW., Comment: Presented at the '2023 ANS Winter Conference and Expo'