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The EdResearch for Action "Overview Series" summarizes the research on key topics to provide K-12 education decision makers and advocates with an evidence base to ground discussions about how to best serve students. This overview brief discusses the central question: What is the evidence on strategies to build a sense of school connectedness and engagement among all students? Key insights are arranged in the following sections: (1) breaking down the issue; (2) evidence-based practices; and (3) practices to avoid. [The BARR Center and Network for College Success are additional collaborators for this report.]

The current study was a retrospective mixed-methods quasi-experimental design (QED) study to determine the effects of Progress Learning on Grades 6-8 mathematics and ELA achievement by comparison growth on the Georgia Milestones Mathematics and ELA assessments of students who received Progress Learning services, in relation to students that did not receive Progress Learning. Supplementary analyses examining the associations between Progress Learning usage metrics and achievement gains are also performed in this study. The results of the main impact analyses showed a positive and statistically significant impact of Progress Learning on student mathematics achievement, with treatment students outgaining comparison students by more than 4 points. The results of the main ELA impact analysis showed a directionally positive, though not statistically significant, impact on ELA achievement, with treatment students outgaining comparison students by more than 3 points. Effect sizes of these analyses ranged between 0.06 to 0.09 SDs, indicating small, though practically meaningful, program impacts of Progress Learning on student achievement, especially in mathematics. Usage analyses showed significant positive associations between student-level Progress Learning usage metrics and achievement gains. Correlations between average Progress Learning activity scores and achievement gains were of particular note, with observed correlations of magnitude above .4 in ELA and above .6 in mathematics. This gives preliminary evidence supporting modest to moderate predictive validity of Progress Learning activity scores in relation to Georgia Milestones scores. These associations remained significant and positive when controlling for prior achievement and demographics, using HLMs similar to those used in the main impact analyses.

Automatic enrollment is a straightforward education reform that facilitates both educational excellence and equity in K-12 schools. By automatically placing highly qualified students in advanced coursework, this low-cost, bi-partisan strategy creates opportunities for high achievement for all students, with low-income, rural and small town, and underrepresented minority students especially appearing to benefit.

Bourgain's symmetrization theorem is a powerful technique reducing boolean analysis on product spaces to the cube. It states that for any product $\Omega_i^{\otimes d}$, function $f: \Omega_i^{\otimes d} \to \mathbb{R}$, and $q > 1$: $$||T_{\frac{1}{2}}f(x)||_q \leq ||\tilde{f}(r,x)||_{q} \leq ||T_{c_q}f(x)||_q$$ where $T_{\rho}f = \sum\limits \rho^Sf^{=S}$ is the noise operator and $\widetilde{f}(r,x) = \sum\limits r_Sf^{=S}(x)$ `symmetrizes' $f$ by convolving its Fourier components $\{f^{=S}\}_{S \subseteq [d]}$ with a random boolean string $r \in \{\pm 1\}^d$. In this work, we extend the symmetrization theorem to high dimensional expanders (HDX). Adapting work of O'Donnell and Zhao (2021), we give as a corollary a proof of optimal global hypercontractivity for partite HDX, resolving one of the main open questions of Gur, Lifshitz, and Liu (STOC 2022). Adapting work of Bourgain (JAMS 1999), we also give the first booster theorem for HDX, resolving a main open questions of Bafna, Hopkins, Kaufman, and Lovett (STOC 2022). Our proof is based on two elementary new ideas in the theory of high dimensional expansion. First we introduce `$q$-norm HDX', generalizing standard spectral notions to higher moments, and observe every spectral HDX is a $q$-norm HDX. Second, we introduce a simple method of coordinate-wise analysis on HDX which breaks high dimensional random walks into coordinate-wise components, and allows each component to be analyzed as a $1$-dimensional operator locally within $X$. This allows for application of standard tricks such as the replacement method, greatly simplifying prior analytic techniques.

Numerous stars exhibit surprisingly large variations in their refractory element abundances, often interpreted as signatures of planetary ingestion events. In this study, we propose that differences in the dust-to-gas ratio near stars during their formation can produce similar observational signals. We investigate this hypothesis using a suite of radiation-dust-magnetohydrodynamic STARFORGE simulations of star formation. Our results show that the distribution of refractory abundance variations ($\rm \Delta [X/H]$) has extended tails, with about 10-30% of all stars displaying variations around $\sim$0.1 dex. These variations are comparable to the accretion of $2-5 \rm M_\oplus$ of planetary material into the convective zones of Sun-like stars. The width of the distributions increases with the incorporation of more detailed dust physics, such as radiation pressure and back-reaction forces, as well as with larger dust grain sizes and finer resolutions. Furthermore, our simulations reveal no correlation between $\rm \Delta [X/H]$ and stellar separations, suggesting that dust-to-gas fluctuations likely occur on scales smaller than those of wide binaries. These findings highlight the importance of considering dust dynamics as a potential source of the observed chemical enrichment in stars., Comment: 10 pages, 3 figures. Submitted to ApJL

We present the first suite of cosmological hydrodynamical zoom-in simulations of isolated dwarf galaxies for a dark sector that consists of Cold Dark Matter and a strongly-dissipative sub-component. The simulations are implemented in GIZMO and include standard baryons following the FIRE-2 galaxy formation physics model. The dissipative dark matter is modeled as Atomic Dark Matter (aDM), which forms a dark hydrogen gas that cools in direct analogy to the Standard Model. Our suite includes seven different simulations of $\sim 10^{10} M_{\odot}$ systems that vary over the aDM microphysics and the dwarf's evolutionary history. We identify a region of aDM parameter space where the cooling rate is aggressive and the resulting halo density profile is universal. In this regime, the aDM gas cools rapidly at high redshifts and only a small fraction survives in the form of a central dark gas disk; the majority collapses centrally into collisionless dark "clumps", which are clusters of sub-resolution dark compact objects. These dark clumps rapidly equilibrate in the inner galaxy, resulting in an approximately isothermal distribution that can be modeled with a simple fitting function. Even when only a small fraction ($\sim 5\%$) of the total dark matter is strongly dissipative, the central densities of classical dwarf galaxies can be enhanced by over an order of magnitude, providing a sharp prediction for observations., Comment: 17 pages, 8 pages of appendices

This study investigates thermal transport across nanocrystalline diamond/AlGaN interfaces, crucial for enhancing thermal management in AlGaN/AlGaN-based devices. Chemical vapor deposition growth of diamond directly on AlGaN resulted in a disordered interface with a high thermal boundary resistance (TBR) of 20.6 m^2-K/GW. We employed sputtered carbide interlayers (e.g., $B_4C$, $SiC$, $B_4C/SiC$) to reduce thermal boundary resistance in diamond/AlGaN interfaces. The carbide interlayers resulted in record-low thermal boundary resistance values of 3.4 and 3.7 m^2-K/GW for Al$_{0.65}$Ga$_{0.35}$N samples with $B_4C$ and $SiC$ interlayers, respectively. STEM imaging of the interface reveals interlayer thicknesses between 1.7-2.5 nm, with an amorphous structure. Additionally, Fast-Fourier Transform (FFT) characterization of sections of the STEM images displayed sharp crystalline fringes in the AlGaN layer, confirming it was properly protected from damage from hydrogen plasma during the diamond growth. In order to accurately measure the thermal boundary resistance we develop a hybrid technique, combining time-domain thermoreflectance and steady-state thermoreflectance fitting, offering superior sensitivity to buried thermal resistances. Our findings underscore the efficacy of interlayer engineering in enhancing thermal transport and demonstrate the importance of innovative measurement techniques in accurately characterizing complex thermal interfaces. This study provides a foundation for future research in improving thermal properties of semiconductor devices through interface engineering and advanced measurement methodologies.

We present the serendipitous discovery of a new radio-continuum ring-like object nicknamed Kyklos (J1802-3353), with MeerKAT UHF and L-band observations. The radio ring, which resembles the recently discovered odd radio circles (ORCs), has a diameter of 80 arcsec and is located just 6 deg from the Galactic plane. However, Kyklos exhibits an atypical thermal radio-continuum spectrum ({\alpha} = -0.1 +/- 0.3), which led us to explore different possible formation scenarios. We concluded that a circumstellar shell around an evolved massive star, possibly a Wolf-Rayet, is the most convincing explanation with the present data., Comment: 7 pages, 5 figures, accepted in A&A

We study the radio continuum emission of four galaxies experiencing ram-pressure stripping in four clusters of the Shapley supercluster at redshift z~0.05. Multi-band (235-1367 MHz) radio data, complemented by integral-field spectroscopy, allow us to detect and analyse in detail the non-thermal component both in the galaxy discs and the radio continuum tails. Three galaxies present radio continuum tails which are tens of kiloparsecs long. By deriving the radio spectral index in the inner and outer tails and comparing our findings with the distribution of the extraplanar ionised gas and the results of N-body/hydrodynamical simulations, we demonstrate that these tails are caused by the ram pressure which, together with the ionised gas, sweeps the magnetic field from the galaxy discs. We suggest that the radio continuum emission in these tails can be differently powered by (i) in situ star formation; (ii) relativistic electrons stripped from the disc; (iii) shock excitation or a combination of them. All the ram-pressure stripped galaxies are found in environments where cluster-cluster interactions occurred and/or are ongoing thus strongly supporting the thesis that cluster and group collisions and mergers may locally increase the ram pressure and trigger hydrodynamical interactions between the intracluster medium and the interstellar medium of galaxies., Comment: 18 pages, 14 figures, accepted for publication in MNRAS

Cosmic rays (CRs) are the primary driver of ionization in star forming molecular clouds (MCs). Despite their potential impacts on gas dynamics and chemistry, no simulations of star cluster formation following the creation of individual stars have included explicit cosmic ray transport (CRT) to date. We conduct the first numerical simulations following the collapse of a $2000 M_{\odot}$ MC and the subsequent star formation including CRT using the STARFORGE framework implemented in the GIZMO code. We show that when CR-transport is streaming-dominated, the CR energy in the cloud is strongly attenuated due to energy losses from the streaming instability. Consequently, in a Milky Way like environment the median CR ionization rate (CRIR) in the cloud is low ($ \zeta \lesssim 2 \times 10^{-19} \rm s^{-1}$) during the main star forming epoch of the calculation and the impact of CRs on the star formation in the cloud is limited. However, in high-CR environments, the CR distribution in the cloud is elevated ($\zeta \lesssim 6 \times 10^{-18}$), and the relatively higher CR pressure outside the cloud causes slightly earlier cloud collapse and increases the star formation efficiency (SFE) by $50 \%$ to $\sim 13 \%$. The initial mass function (IMF) is similar in all cases except with possible variations in a high-CR environment. Further studies are needed to explain the range of ionization rates observed in MCs and explore star formation in extreme CR environments., Comment: Accepted to The Astrophysical Journal

Planet formation is expected to be severely limited in disks of low metallicity, owing to both the small solid mass reservoir and the low opacity accelerating the disk gas dissipation. While previous studies have found a weak correlation between the occurrence rates of small planets ($\leq$4R$_\oplus$) and stellar metallicity, so far no studies have probed below the metallicity limit beyond which planet formation is predicted to be suppressed. Here, we constructed a large catalog of ~110,000 metal-poor stars observed by the TESS mission with spectroscopically-derived metallicities, and systematically probed planet formation within the metal-poor regime ([Fe/H] $\leq$ -0.5) for the first time. Extrapolating known higher-metallicity trends for small, short-period planets predicts the discovery of ~68 superEarths around these stars (~85,000 stars) after accounting for survey completeness; however, we detect none. As a result, we have placed the most stringent upper limit on super-Earth occurrence rates around metal-poor stars (-0.75 < [Fe/H] $\leq$ -0.5) to date, $\leq$ 1.67%, a statistically significant (p-value=0.000685) deviation from the prediction of metallicity trends derived with Kepler and K2. We find a clear host star metallicity cliff for super-Earths that could indicate the threshold below which planets are unable to grow beyond an Earth-mass at short orbital periods. This finding provides a crucial input to planet formation theories, and has implications for the small planet inventory of the Galaxy and the galactic epoch at which the formation of small planets started., Comment: Accepted to AJ

The dust grain size distribution (GSD) likely varies significantly across different star-forming environments in the Universe, but the overall impact of this variation on star formation remains unclear. This ambiguity arises because the GSD interacts non-linearly with processes like heating/cooling, radiation, and chemistry, which have competing effects and different environmental dependencies. In this study, we investigate the effects of GSD variation on the thermochemistry and evolution of giant molecular clouds (GMCs). To achieve this, we conducted radiation-dust-magnetohydrodynamic simulations spanning a range of cloud masses and grain sizes, which explicitly incorporate the dynamics of dust grains within the full-physics framework of the STARFORGE project. We find that differences in grain size significantly alter the thermochemistry of GMCs. Specifically, we show that the leading-order effect is that larger grains, under fixed dust mass and dust-to-gas ratio conditions, result in lower dust opacities. This reduced opacity permits ISRF photons to penetrate more deeply and allows internal radiation field photons to permeate more extensively into the cloud, resulting in rapid gas heating and the inhibition of star formation. We find that star formation efficiency is highly sensitive to grain size, with an order of magnitude reduction in efficiency when grain size increases from 0.1 $\rm\mu m$ to 10 $\rm\mu m$. Additionally, we note that warmer gas suppresses the formation of low-mass stars. Moreover, as a consequence of the decreased opacities, we observe a greater proportion of gas residing in diffuse ionized structures., Comment: 13 pages, 5 figures, submitted to ApJ

A poset is called upper homogeneous, or "upho," if every principal order filter of the poset is isomorphic to the whole poset. We study (finite type $\mathbb{N}$-graded) upho lattices, with an eye towards their classification. Any upho lattice has associated to it a finite graded lattice called its core, which determines its rank generating function. We investigate which finite graded lattices arise as cores of upho lattices, providing both positive and negative results. On the one hand, we show that many well-studied finite lattices do arise as cores, and we present combinatorial and algebraic constructions of the upho lattices into which they embed. On the other hand, we show there are obstructions which prevent many finite lattices from being cores., Comment: 39 pages, 11 figures

Recent advances in active materials and fabrication techniques have enabled the production of cyclically self-deployable metamaterials with an expanded functionality space. However, designing metamaterials that possess continuously tunable mechanical properties after self-deployment remains a challenge, notwithstanding its importance. Inspired by push puppets, we introduce an efficient design strategy to create reversibly self-deployable metamaterials with continuously tunable post-deployment stiffness and damping. Our metamaterial comprises contracting actuators threaded through beads with matching conical concavo-convex interfaces in networked chains. The slack network conforms to arbitrary shapes, but when actuated, it self-assembles into a preprogrammed configuration with beads gathered together. Further contraction of the actuators can dynamically tune the assembly's mechanical properties through the beads' particle jamming, while maintaining the overall structure with minimal change. We show that, after deployment, such metamaterials exhibit pronounced tunability in bending-dominated configurations: they can become more than 35 times stiffer and change their damping capability by over 50%. Through systematic analysis, we find that the beads'conical angle can introduce geometric nonlinearity, which has a major effect on the self-deployability and tunability of the metamaterial. Our work provides routes towards reversibly self-deployable, lightweight, and tunable metamaterials, with potential applications in soft robotics, reconfigurable architectures, and space engineering., Comment: 6 figures

We investigate the universality of entanglement kinetics in polymer melts. We compare predictions of a recently developed constitutive equation for disentanglement to molecular dynamics simulations of both united-atom polyethylene and Kremer-Grest models for polymers in shear and extensional flow. We confirm that entanglements recover on the retraction timescale, rather than the reptation timescale. We find that the convective constraint release parameter $\beta$ is independent of molecular weight, but that it increases with the ratio of Kuhn length $b_K$ to packing length $p$ as $\beta\sim (b_K/p)^\alpha$, with an exponent $\alpha=1.9$, which may suggest that disentanglement rate correlates with an increase in the tube diameter. These results may help shed light on which polymers are more likely to undergo shear banding., Comment: 18 pages + 5 figures main text + 11 pages + 2 figures SI (in preprint form)

Recent studies have found evidence for parity violation in the BOSS spectroscopic galaxy survey, with statistical significance as high as $7\sigma$. These analyses assess the significance of the parity-odd four-point correlation function (4PCF) with a statistic called $\chi^2$. This statistic is biased if the parity-even eight-point correlation function (8PCF) of the data differs from the mock catalogs. We construct new statistics $\chi^2_\times$, $\chi^2_{\mathrm{null}}$ that separate the parity violation signal from the 8PCF bias term, allowing them to be jointly constrained. Applying these statistics to BOSS, we find that the parity violation signal ranges from $0$ to $2.5\sigma$ depending on analysis choices, whereas the 8PCF bias term is $\sim 6\sigma$. We conclude that there is no compelling evidence for parity violation in BOSS. Our new statistics can be used to search for parity violation in future surveys, such as DESI, without 8PCF biases., Comment: 46 pages, 11 figures; comments welcome!

The purpose of this study was to examine the efficacy of IXL Math in an elementary school context, using a randomized-control trial (RCT). This study examined mathematics achievement trajectories of 545 Grades 3-5 students across four elementary schools in a small Michigan school district during the 2022-23 school year. Teachers were randomly selected to use IXL Math (treatment condition) or business-as-usual instruction (control condition) for the winter and spring of the 2022-23 school year. Outcome variables included spring 2023 Renaissance Star mathematics scores (progress monitoring) and spring 2023 M-STEP mathematics scores (state test scores). Prior achievement variables included winter 2023 Renaissance Star mathematics scores and spring 2022 M-STEP mathematics scores. Treatment teachers were also administered a survey focusing on teacher experiences with and reactions to the IXL Math program. District-wide, nine of 11 treatment teachers volunteered to complete the survey. Hierarchical linear modeling (HLM) with students nested within teachers was used to complete the main impact analyses, while quantitative survey data were analyzed descriptively, and open-ended items were analyzed qualitatively. The results of the main impact analyses showed a significant positive impact of IXL Math on student mathematics achievement, with treatment students gaining approximately 10-points more on the Renaissance Star mathematics assessment than did control students. The effect size of this impact was 0.13 SDs, indicating a small-to-moderate practical impact of IXL Math on student mathematics achievement. Subgroup analyses showed that IXL Math had additional significant positive impacts on Hispanic, special education, ELL, and free and reduced meal (FARMS) students, with the magnitudes of these impacts ranging from 13-17 points. In addition, teacher perceptions of IXL Math were generally very positive, especially in relation to professional development and overall program perceptions. In all, the results of this study provide evidence supporting efficacy of IXL Math in improving student mathematics achievement, especially for student subgroups that historically had started at lower performance levels.

The purpose of this study was to examine the efficacy of Imagine Learning's MyPath program on student mathematics and reading achievement growth. This study examined achievement growth trajectories of kindergarten students in one suburban Midwestern district in the 2022-23 school year. NWEA MAP mathematics and reading scores served as the main outcome variables in quantitative impact analyses. As Imagine MyPath was used by all district kindergarten students, MAP mathematics and reading score growth of district kindergarten students was compared to that of matched comparison students obtained from a Similar Schools Report (SSR) provided to the district by NWEA. Additionally, a questionnaire was administered to district teachers that used Imagine MyPath to examine teacher perceptions of the program. A statistically significant positive impact of Imagine MyPath on mathematics achievement was observed, with treatment students outgaining matched comparison students by more than 2 points. Treatment students also outgained matched comparison students in reading achievement, but this difference was minimal and not statistically significant. Teacher perceptions of Imagine MyPath were largely positive, especially regarding perceived program impacts on student learning. The results of this study provide preliminary evidence regarding the efficacy of Imagine MyPath in relation to mathematics achievement. It is important to note that results from this study are derived from data in one unique district in which all students used the program. Future research may focus on contexts where some students are using Imagine MyPath while other students are participating in business-as-usual instruction.

Educators and policymakers across the country have been raising alarms about a growing teacher shortage. In Michigan, teacher shortages were so severe in the 2018-19 school year that 2,500 classrooms were staffed by long-term substitutes--nearly ten times the number placed in classrooms five years prior. The COVID-19 pandemic has raised even greater concerns about the supply of teachers, with several surveys of Michigan educators administered early in the COVID-19 pandemic indicating that a substantial proportion of full-time and substitute teachers are considering leaving the teaching profession due to pandemic-related concerns. In this study, we use administrative data from Michigan to understand how teachers' propensities to leave the public school system, switch districts, or switch schools shifted after the onset of the COVID-19 pandemic.

Recent simulations have demonstrated the formation of 'flux-frozen' and hyper-magnetized disks, qualitatively distinct from both classical $\alpha$ disks and magnetically-arrested disks, as a natural consequence of fueling gas to supermassive black holes in galactic nuclei. We previously showed that the dynamical structure of said disks can be approximated by simple analytic similarity models. Here we study the thermal properties of these models over a wide range of physical scales and accretion rates. We show there are several characteristic zones: a dusty 'torus'-like region, a multi-phase neutral and then multi-phase ionized, broad line-emitting region interior to the sublimation radius, before finally a transition to a thermal accretion disk with a warm Comptonizing layer. The disks are strongly-flared with large scale heights, and reprocess and/or scatter an order-one fraction of the central disk emission. As a result, this simple accretion disk model predicts phenomena including the existence of a dusty torus and its covering factor, geometry, clumpiness, and dust temperatures; a broad-line-region (BLR) with its characteristic sizes and luminosities and ionization properties; extended scattering/reprocessing surfaces producing cooler disk continuum and apparently large observed disk sizes; and existence of warm Comptonizing layers and hard coronal gas. Remarkably, these properties emerge without our having to introduce new components or parameters: they are all part of the accretion flow if the disks are in the hyper-magnetized limit., Comment: Submitted to the Open Journal of Astrophysics. Comments welcome. 36 pages, 14 figures

Stellar feedback influences the star formation rate (SFR) and the interstellar medium of galaxies in ways that are difficult to quantify numerically, because feedback is an essential ingredient of realistic simulations. To overcome this, we conduct a feedback-halting experiment starting with a Milky Way-mass galaxy in the FIRE-2 simulation framework. Terminating feedback, and comparing to a simulation in which feedback is maintained, we monitor how the runs diverge. We find that without feedback, interstellar turbulent velocities decay. There is a marked increase of dense material, while the SFR increases by over an order of magnitude. Importantly, this SFR boost is a factor of $\sim$15-20 larger than is accounted for by the increased free fall rate caused by higher densities. This implies that feedback moderates the star formation efficiency per free-fall time more directly than simply through the density distribution. To probe changes at the scale of giant molecular clouds (GMCs), we identify GMCs using density and virial parameter thresholds, tracking clouds as the galaxy evolves. Halting feedback stimulates rapid changes, including a proliferation of new bound clouds, a decrease of turbulent support in loosely-bound clouds, an overall increase in cloud densities, and a surge of internal star formation. Computing the cloud-integrated SFR using several theories of turbulence regulation, we show that these theories underpredict the surge in SFR by at least a factor of three. We conclude that galactic star formation is essentially feedback-regulated on scales that include GMCs, and that stellar feedback affects GMCs in multiple ways., Comment: 20 pages, 10 figures, under ongoing review at ApJ

We present radio observations of the galaxy cluster Abell S1136 at 888 MHz, using the Australian Square Kilometre Array Pathfinder radio telescope, as part of the Evolutionary Map of the Universe Early Science program. We compare these findings with data from the Murchison Widefield Array, XMM-Newton, the Wide-field Infrared Survey Explorer, the Digitised Sky Survey, and the Australia Telescope Compact Array. Our analysis shows the X-ray and radio emission in Abell S1136 are closely aligned and centered on the BCG, while the X-ray temperature profile shows a relaxed cluster with no evidence of a cool core. We find that the diffuse radio emission in the centre of the cluster shows more structure than seen in previous low-resolution observations of this source, which appeared formerly as an amorphous radio blob, similar in appearance to a radio halo; our observations show the diffuse emission in the Abell S1136 galaxy cluster contains three narrow filamentary structures visible at 888 MHz, between$\sim 80$ and 140 kpc in length; however the properties of the diffuse emission do not fully match that of a radio (mini-)halo or (fossil) tailed radio source., Comment: To appear in PASA

Stars form within dense cores composed of both gas and dust within molecular clouds. However, despite the crucial role that dust plays in the star formation process, its dynamics is frequently overlooked, with the common assumption being a constant, spatially uniform dust-to-gas ratio and grain size spectrum. In this study, we introduce a set of radiation-dust-magnetohydrodynamic simulations of star forming molecular clouds from the {\small STARFORGE} project. These simulations expand upon the earlier radiation MHD models, which included cooling, individual star formation, and feedback. Notably, they explicitly address the dynamics of dust grains, considering radiation, drag, and Lorentz forces acting on a diverse size spectrum of live dust grains. We find that interactions between radiation and dust significantly influence the properties of gas surrounding and accreting onto massive stars. Specifically, we find that once stars exceed a certain mass threshold ($\sim 2 M_{\odot}$), their emitted radiation can evacuate dust grains from their vicinity, giving rise to a dust-suppressed zone of size $\sim 100$ AU. Commencing during the early accretion stages and preceding the Main-sequence phase, this process results in a mass-dependent depletion in the accreted dust-to-gas (ADG) mass ratio within both the circumstellar disc and the star. We predict massive stars ($\gtrsim 10 M_{\odot}$) would exhibit ADG ratios that are approximately one order of magnitude lower than that of their parent clouds. Consequently, stars, their discs, and circumstellar environments would display notable deviations in the abundances of elements commonly associated with dust grains, such as carbon and oxygen., Comment: 14 pages, 9 figures, submitted to ApJ

We present an investigation of clustered stellar feedback in the form of superbubbles identified within eleven galaxies from the FIRE-2 (Feedback in Realistic Environments) cosmological zoom-in simulation suite, at both cosmic noon (1 < z < 3) and in the local Universe. We study the spatially-resolved multiphase outflows that these supernovae drive, comparing our findings with recent theory and observations. These simulations consist of five LMC-mass galaxies and six Milky Way-mass progenitors (with a minimum baryonic particle mass of $m_{b.min} = 7100 M_{\odot}$), for which we calculate the local mass and energy loading factors on 750~pc scales from the identified outflows. We also characterize the multiphase morphology and properties of the identified superbubbles, including the `shell' of cool ($T<10^5$ K) gas and break out of energetic hot ($T>10^5$ K) gas when the shell bursts. For all galaxies, the outflow mass, momentum, and energy fluxes appear to reach their peak during the identified superbubbles, and we investigate the effects on the interstellar medium (ISM), circumgalactic medium (CGM), and subsequent star formation rates. We find that these simulations, regardless of redshift, have mass-loading factors and momentum fluxes in the cool gas that largely agree with recent observations. Lastly, we also investigate how methodological choices in measuring outflows can affect loading factors for galactic winds.

The replicability crisis is a major issue across nearly all areas of empirical science, calling for the formal study of replicability in statistics. Motivated in this context, [Impagliazzo, Lei, Pitassi, and Sorrell STOC 2022] introduced the notion of replicable learning algorithms, and gave basic procedures for $1$-dimensional tasks including statistical queries. In this work, we study the computational and statistical cost of replicability for several fundamental high dimensional statistical tasks, including multi-hypothesis testing and mean estimation. Our main contribution establishes a computational and statistical equivalence between optimal replicable algorithms and high dimensional isoperimetric tilings. As a consequence, we obtain matching sample complexity upper and lower bounds for replicable mean estimation of distributions with bounded covariance, resolving an open problem of [Bun, Gaboardi, Hopkins, Impagliazzo, Lei, Pitassi, Sivakumar, and Sorrell, STOC2023] and for the $N$-Coin Problem, resolving a problem of [Karbasi, Velegkas, Yang, and Zhou, NeurIPS2023] up to log factors. While our equivalence is computational, allowing us to shave log factors in sample complexity from the best known efficient algorithms, efficient isoperimetric tilings are not known. To circumvent this, we introduce several relaxed paradigms that do allow for sample and computationally efficient algorithms, including allowing pre-processing, adaptivity, and approximate replicability. In these cases we give efficient algorithms matching or beating the best known sample complexity for mean estimation and the coin problem, including a generic procedure that reduces the standard quadratic overhead of replicability to linear in expectation., Comment: 119 pages

The origins and mergers of supermassive black holes (BHs) remain a mystery. We describe a scenario from a novel multi-physics simulation featuring rapid ($\lesssim 1\,$Myr) hyper-Eddington gas capture by a $\sim 1000\,{\rm M}_{\odot}$ ``seed'' BH up to supermassive ($\gtrsim 10^{6}\,M_{\odot}$) masses, in a massive, dense molecular cloud complex typical of high-redshift starbursts. Due to the high cloud density, stellar feedback is inefficient and most of the gas turns into stars in star clusters which rapidly merge hierarchically, creating deep potential wells. Relatively low-mass BH seeds at random positions can be ``captured'' by merging sub-clusters and migrate to the center in $\sim1$ free-fall time (vastly faster than dynamical friction). This also efficiently produces a paired BH binary with $\sim 0.1$\,pc separation. The centrally-concentrated stellar density profile (akin to a ``proto-bulge'') allows the cluster as a whole to capture and retain gas and build up a large (pc-scale) circum-binary accretion disk with gas coherently funnelled to the central BH (even when the BH radius of influence is small). The disk is ``hyper-magnetized'' and ``flux-frozen'': dominated by a toroidal magnetic field with plasma $\beta \sim 10^{-3}$, with the fields amplified by flux-freezing. This drives hyper-Eddington inflow rates $\gtrsim 1\,\rm M_\odot yr^{-1}$, which also drive the two BHs to nearly-equal masses. The late-stage system appears remarkably similar to recently-observed high-redshift ``little red dots.'' This scenario can provide an explanation for rapid SMBH formation, growth and mergers in high-redshift galaxies., Comment: 9 pages, 5 figures, final version matching the publication

Advances in Large Language Models (LLMs) have led to significant interest in their potential to support human experts across a range of domains, including public health. In this work we present automated evaluations of LLMs for public health tasks involving the classification and extraction of free text. We combine six externally annotated datasets with seven new internally annotated datasets to evaluate LLMs for processing text related to: health burden, epidemiological risk factors, and public health interventions. We initially evaluate five open-weight LLMs (7-70 billion parameters) across all tasks using zero-shot in-context learning. We find that Llama-3-70B-Instruct is the highest performing model, achieving the best results on 15/17 tasks (using micro-F1 scores). We see significant variation across tasks with all open-weight LLMs scoring below 60% micro-F1 on some challenging tasks, such as Contact Classification, while all LLMs achieve greater than 80% micro-F1 on others, such as GI Illness Classification. For a subset of 12 tasks, we also evaluate GPT-4 and find comparable results to Llama-3-70B-Instruct, which scores equally or outperforms GPT-4 on 6 of the 12 tasks. Overall, based on these initial results we find promising signs that LLMs may be useful tools for public health experts to extract information from a wide variety of free text sources, and support public health surveillance, research, and interventions., Comment: 33 pages. Feedback and comments are highly appreciated

The detection of supermassive black holes (SMBHs) in high-redshift luminous quasars may require a phase of rapid accretion, and as a precondition, substantial gas influx toward seed black holes (BHs) from kilo-parsec or parsec scales. Our previous research demonstrated the plausibility of such gas supply for BH seeds within star-forming giant molecular clouds (GMCs) with high surface density ($\sim 10^4\,{\rm {\rm M_\odot}\, pc}^{-2}$), facilitating ``hyper-Eddington'' accretion via efficient feeding by dense clumps which are driven by turbulence and stellar feedback. This article investigates the impacts of feedback from accreting BHs on this process, including radiation, mechanical jets, and highly relativistic cosmic rays. We run a suite of numerical simulations to explore diverse parameter spaces of BH feedback, including the sub-grid accretion model, feedback energy efficiency, mass loading factor, and initial metallicity. Utilizing radiative feedback models inferred from the slim disk, we find that hyper-Eddington accretion is still achievable, yielding BH bolometric luminosities as high as $10^{41}$ -- $10^{44}\,\rm erg/s$, depending on the GMC properties and specific feedback model assumed. We find the maximum possible mass growth of seed BHs ($\Delta M_{\rm BH}^{\rm max}$) is regulated by the momentum deposition rate from BH feedback, $\dot{p}_{\rm feedback}/(\dot{M}_{\rm BH} c)$, which leads to an analytic scaling that agrees well with simulations. This scenario predicts the rapid formation of $\sim 10^4\,\rm M_\odot$ intermediate-massive BHs (IMBHs) from stellar-mass BHs within $\sim \rm Myr$. Furthermore, we examine the impacts of sub-grid accretion models and how BH feedback may influence star formation within these cloud complexes., Comment: Comments welcome

The magnetohydrodynamics (MHD) equations plus 'non-ideal' (Ohmic, Hall, ambipolar) resistivities are widely used to model weakly-ionized astrophysical systems. We show that if gradients in the magnetic field become too steep, the implied charge drift speeds become much faster than microphysical signal speeds, invalidating the assumptions used to derive both the resistivities and MHD equations themselves. Generically this situation will excite microscale instabilities that suppress the drift and current. We show this could be relevant at low ionization fractions especially if Hall terms appear significant, external forces induce supersonic motions, or dust grains become a dominant charge carrier. Considering well-established treatments of super-thermal drifts in laboratory, terrestrial, and Solar plasmas as well as conduction and viscosity models, we generalize a simple prescription to rectify these issues, where the resistivities are multiplied by a correction factor that depends only on already-known macroscopic quantities. This is generalized for multi-species and weakly-ionized systems, and leaves the equations unchanged in the drift limits for which they are derived, but restores physical behavior (driving the system back towards slow drift by diffusing away small-scale gradients in the magnetic field) if the limits are violated. This has important consequences: restoring intuitive behaviors such as the system becoming hydrodynamic in the limit of zero ionization; suppressing magnetic structure on scales below a critical length which can comparable to circumstellar disk sizes; limiting the maximum magnetic amplification; and suppressing the effects of the Hall term in particular. This likely implies that the Hall term does not become dynamically important under most conditions of interest in these systems., Comment: 16 pages, 4 figures. Submitted to the Open Journal of Astrophysics. Comments welcome

Among the bent tail radio galaxies common in galaxy clusters are some with long, collimated tails (so-called head-tail galaxies) shaped by their interactions with the intracluster medium (ICM). Here we report the discovery of intricate filamentary structure in and beyond the ~28' (570 kpc) long, helical radio tail of the Corkscrew Galaxy (1610-60.5, ESO137-G007), which resides in the X-ray bright cluster Abell 3627 (D = 70 Mpc). Deep radio continuum data were obtained with wide-field Phased Array Feeds on the Australian Square Kilometer Array Pathfinder (ASKAP) at 944 MHz and 1.4 GHz. The Corkscrew Galaxy is located 15' north of the prominent wide-angle tail (WAT) radio galaxy 1610-60.8 (ESO137-G006) near the cluster centre. While the bright (young) part of its radio tail is highly collimated, the faint (old) part shows increasing oscillation amplitudes, break-ups, and filaments. We find a stunning set of arc-shaped radio filaments beyond and mostly orthogonal to the collimated Corkscrew tail end, forming a partial bubble. This may be the first detection of a "proto-lobe" seen in 3D MHD simulations by Nolting et al. (2019), formed by the face-on impact of the Corkscrew Galaxy with a shock front in the cluster outskirts. Interactions of the radio galaxy tail with the ICM are likely responsible for the tail collimation and shear forces within the ICM for its increasingly filamentary structure. We also report the discovery of small (~20-30 kpc) ram-pressure stripped radio tails in four Abell 3627 cluster galaxies., Comment: 11 pages, 7 figures, MNRAS, submitted

We report the ASKAP discovery of a new Galactic supernova remnant (SNR) candidate G308.73+1.38, which we name Raspberry. This new SNR candidate has an angular size of 20.7 arcmin $\times$ 16.7 arcmin, and we measure a total integrated flux of 407$\pm$50 mJy. We estimate Raspberry's most likely diameter of 10$-$30 pc which would place it at a distance of 3$-$5 kpc, on the near side of the Milky Way's Scutum$-$Centaurus Arm. We also find a Stokes$-$V point source close to the centre of Raspberry with a $\sim$5$\sigma$ significance. This point source may be the remaining compact source, a neutron star, or possibly a pulsar, formed during the initial supernova event., Comment: 3 pages, 1 figure

Fueling star formation in large, discy galaxies requires a continuous supply of gas accreting into star-forming regions. Previously, we characterized this accretion in 4 Milky Way mass galaxies ($M_{\rm halo}\sim10^{12}M_{\odot}$) in the FIRE-2 cosmological zoom-in simulations. At $z\sim0$, we found that gas within the inner circumgalactic medium (iCGM) approaches the disc with comparable angular momentum (AM) to the disc edge, joining in the outer half of the gaseous disc. Within the disc, gas moves inward at velocities of $\sim$1-5~km~s$^{-1}$ while fully rotationally supported. In this study, we analyze the torques that drive these flows. In all cases studied, we find that the torques in discs enable gas accreted near the disc edge to transport inwards and fuel star formation in the central few kpc. The primary sources of torque come from gravity, hydrodynamical forces, and the sub-grid $P dV$ work done by supernova (SNe) remnants interacting with gas on $\lesssim$10 pc scales. These SNe remnant interactions induce negative torques within the inner disc and positive torques in the outer disc. The gas-gas gravitational, hydro, and "feedback" torques transfer AM outward to where accreting gas joins the disc, playing an important role in driving inflows and regulating disc structure. Gravitational torques from stars and dark matter provide an AM sink within the innermost regions of the disc and iCGM, respectively. Feedback torques are dominant within the disc, while gravitational and hydrodynamical torques have similar significance depending on the system/region. Torques from viscous shearing, magnetic forces, stellar winds, and radiative transfer are less significant., Comment: 20 pages, 18 figures. Accepted for publication in MNRAS. Added 1 figure to Appendix B, modified text

New advancements in radio data post-processing are underway within the SKA precursor community, aiming to facilitate the extraction of scientific results from survey images through a semi-automated approach. Several of these developments leverage deep learning (DL) methodologies for diverse tasks, including source detection, object or morphology classification, and anomaly detection. Despite substantial progress, the full potential of these methods often remains untapped due to challenges associated with training large supervised models, particularly in the presence of small and class-unbalanced labelled datasets. Self-supervised learning has recently established itself as a powerful methodology to deal with some of the aforementioned challenges, by directly learning a lower-dimensional representation from large samples of unlabelled data. The resulting model and data representation can then be used for data inspection and various downstream tasks if a small subset of labelled data is available. In this work, we explored contrastive learning methods to learn suitable radio data representation from unlabelled images taken from the ASKAP EMU and SARAO MeerKAT GPS surveys. We evaluated trained models and the obtained data representation over smaller labelled datasets, also taken from different radio surveys, in selected analysis tasks: source detection and classification, and search for objects with peculiar morphology. For all explored downstream tasks, we reported and discussed the benefits brought by self-supervised foundational models built on radio data., Comment: 21 pages, 16 figures

In galaxy formation simulations, it is increasingly common to represent supernovae (SNe) at finite resolution (when the Sedov-Taylor phase is unresolved) via hybrid energy-momentum coupling with some 'terminal momentum' $p_{\rm term}$ (depending weakly on ambient density and metallicity) that represents unresolved work from an energy-conserving phase. Numerical implementations can ensure momentum and energy conservation of such methods, but these require that couplings depend on the surrounding gas velocity field (radial velocity $\langle v_{r} \rangle$). This raises the question of whether $p_{\rm term}$ should also be velocity-dependent, which we explore analytically and in simulations. We show that for simple spherical models, the dependence of $p_{\rm term}$ on $\langle v_{r} \rangle$ introduces negligible corrections beyond those already imposed by energy conservation if $\langle v_{r} \rangle \ge 0$. However, for SNe in some net converging flow ($\langle v_{r} \rangle<0$), naively coupling the total momentum when a blastwave reaches the standard cooling/snowplow phase (or some effective cooling time/velocity/temperature criterion) leads to enormous $p_{\rm term}$ and potentially pathological behaviors. We propose an alternative $\Delta$-Momentum formulation which represents the differential SNe effect and show this leads to opposite behavior of $p_{\rm term}$ in this limit. We also consider a more conservative velocity-independent formulation. Testing in numerical simulations, these directly translate to large effects on predicted star formation histories and stellar masses of massive galaxies, explaining differences between some models and motivating further study in idealized simulations., Comment: 9 pages, 3 figures. Submitted to the Open Journal of Astrophysics. Comments welcome

While galaxy rotation curves provide one of the most powerful methods for measuring dark matter profiles in the inner regions of rotation-supported galaxies, at the dwarf scale there are factors that can complicate this analysis. Given the expectation of a universal profile in dark matter-only simulations, the diversity of observed rotation curves has become an often-discussed issue in Lambda Cold Dark Matter cosmology on galactic scales. We analyze a suite of Feedback in Realistic Environments (FIRE) simulations of $10^{10}-10^{12}$ $M_\odot$ halos with standard cold dark matter, and compare the true circular velocity to rotation curve reconstructions. We find that, for galaxies with well-ordered gaseous disks, the measured rotation curve may deviate from true circular velocity by at most 10% within the radius of the disk. However, non-equilibrium behavior, non-circular motions, and non-thermal and non-kinetic stresses may cause much larger discrepancies of 50% or more. Most rotation curve reconstructions underestimate the true circular velocity, while some reconstructions transiently over-estimate it in the central few kiloparsecs due to dynamical phenomena. We further demonstrate that the features that contribute to these failures are not always visibly obvious in HI observations. If such dwarf galaxies are included in galaxy catalogs, they may give rise to the appearance of "artificial" rotation curve diversity that does not reflect the true variation in underlying dark matter profiles., Comment: 20 pages, 10 figures

We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the condition number of $X^\top X$, where $X$ is the design matrix. All prior private algorithms for this task require either $d^{3/2}$ examples, error growing polynomially with the condition number, or exponential time. Our near-optimal accuracy guarantee holds for any dataset with bounded statistical leverage and bounded residuals. Technically, we build on the approach of Brown et al. (2023) for private mean estimation, adding scaled noise to a carefully designed stable nonprivate estimator of the empirical regression vector., Comment: 42 pages, 3 figures

We show that Claude 3 Opus, a large language model (LLM) released by Anthropic in March 2024, exhibits stronger machine translation competence than other LLMs. Though we find evidence of data contamination with Claude on FLORES-200, we curate new benchmarks that corroborate the effectiveness of Claude for low-resource machine translation into English. We find that Claude has remarkable \textit{resource efficiency} -- the degree to which the quality of the translation model depends on a language pair's resource level. Finally, we show that advancements in LLM translation can be compressed into traditional neural machine translation (NMT) models. Using Claude to generate synthetic data, we demonstrate that knowledge distillation advances the state-of-the-art in Yoruba-English translation, meeting or surpassing strong baselines like NLLB-54B and Google Translate., Comment: 17 pages, 15 figures

The exploration of intelligent machines has recently spurred the development of physical neural networks, a class of intelligent metamaterials capable of learning, whether in silico or in situ, from observed data. In this study, we introduce a back-propagation framework for lattice-based mechanical neural networks (MNNs) to achieve prescribed static and dynamic performance. This approach leverages the steady states of nodes for back-propagation, efficiently updating the learning degrees of freedom without prior knowledge of input loading. One-dimensional MNNs, trained with back-propagation in silico, can exhibit the desired behaviors on demand function as intelligent mechanical machines. The framework is then employed for the precise morphing control of the two-dimensional MNNs subjected to different static loads. Moreover, the intelligent MNNs are trained to execute classical machine learning tasks such as regression to tackle various deformation control tasks. Finally, the disordered MNNs are constructed and trained to demonstrate pre-programmed wave bandgap control ability, illustrating the versatility of the proposed approach as a platform for physical learning. Our approach presents an efficient pathway for the design of intelligent mechanical metamaterials for a wide range of static and dynamic target functionalities, positioning them as powerful engines for physical learning.

We prove optimal concentration of measure for lifted functions on high dimensional expanders (HDX). Let $X$ be a $k$-dimensional HDX. We show for any $i\leq k$ and $f:X(i)\to [0,1]$: \[\Pr_{s\in X(k)}\left[\left|\underset{{t\subseteq s}}{\mathbb{E}}[f(t)]-\mu\right|\geq\varepsilon\right]\leq exp\left(-\varepsilon^2\frac{k}{i}\right).\] Using this fact, we prove that high dimensional expanders are reverse hypercontractive, a powerful functional inequality from discrete analysis implying that for any sets $A,B \subset X(k)$, the probability a $\rho$-correlated pair passes between them is at least \[\Pr_{s,s' \sim T_\rho}[s \in A, s' \in B] \geq \Pr[A]^{O(1)} \Pr[B]^{O(1)}.\] Our results hold under weak spectral assumptions on $X$. Namely we prove exponential concentration of measure for any complex below the `Trickling-Down Threshold' (beyond which concentration may be arbitrarily poor), and optimal concentration for $\sqrt{k}$-skeletons of such complexes. We also show optimal bounds for the top dimension of stronger HDX among other settings. We leverage our inequalities to prove several new agreement testing theorems on high dimensional expanders, including a new 99%-regime test for subsets, and a variant of the `Z-test' achieving inverse exponential soundness under the stronger assumption of $\ell_\infty$-expansion. The latter gives rise to the first optimal testers beyond the complete complex and products, a stepping stone toward the use of HDX in strong soundness PCPs. We also give applications within expansion, analysis, combinatorics, and coding theory, including a proof that two-sided HDX have optimal geometric overlap (giving the first explicit bounded-degree construction), near-optimal double samplers, new super-exponential degree lower bounds for certain HDX, distance-amplified list-decodable and locally testable codes, a Frankl-R\"odl Theorem and more., Comment: A mistake in the proof of Theorem 7.14 was corrected (Theorem 7.15 in previous version). Also some minor changes and typos were fixed

We present the discovery of large radio shells around a massive pair of interacting galaxies and extended diffuse X-ray emission within the shells. The radio data were obtained with the Australian Square Kilometer Array Pathfinder (ASKAP) in two frequency bands centred at 944 MHz and 1.4 GHz, respectively, while the X-ray data are from the XMM-Newton observatory. The host galaxy pair, which consists of the early-type galaxies ESO 184-G042 and LEDA 418116, is part of a loose group at a distance of only 75 Mpc (redshift z = 0.017). The observed outer radio shells (diameter ~ 145 kpc) and ridge-like central emission of the system, ASKAP J1914-5433 (Physalis), are likely associated with merger shocks during the formation of the central galaxy (ESO 184-G042) and resemble the new class of odd radio circles (ORCs). This is supported by the brightest X-ray emission found offset from the centre of the Physalis system, instead centered at the less massive galaxy, LEDA 418116. The host galaxy pair is embedded in an irregular envelope of diffuse light, highlighting on-going interactions. We complement our combined radio and X-ray study with high-resolution simulations of the circumgalactic medium (CGM) around galaxy mergers from the Magneticum project to analyse the evolutionary state of the Physalis system. We argue that ORCs / radio shells could be produced by a combination of energy release from the central AGN and subsequent lightening up in radio emission by merger shocks traveling through the CGM of these systems., Comment: 12 pages, 8 figures, submitted to MNRAS

Recently, we demonstrated self-consistent formation of strongly-magnetized quasar accretion disks (QADs) from cosmological radiation-magnetohydrodynamic-thermochemical galaxy-star formation simulations, including the full STARFORGE physics shown previously to produce a reasonable IMF under typical ISM conditions. Here we study star formation and the stellar IMF in QADs, on scales from 100 au to 10 pc from the SMBH. We show it is critical to include physics often previously neglected, including magnetic fields, radiation, and (proto)stellar feedback. Closer to the SMBH, star formation is suppressed, but the (rare) stars that do form exhibit top-heavy IMFs. Stars can form only in special locations (e.g. magnetic field switches) in the outer QAD. Protostars accrete their natal cores rapidly but then dynamically decouple from the gas and wander, ceasing accretion on timescales ~100 yr. Their jets control initial core accretion, but the ejecta are swept up into the larger-scale QAD flow without much dynamical effect. The strong tidal environment strongly suppresses common-core multiplicity. The IMF shape depends sensitively on un-resolved dynamics of protostellar disks (PSDs), as the global dynamical times can become incredibly short ($\ll$ yr) and tidal fields are incredibly strong, so whether PSDs can efficiently transport angular momentum or fragment catastrophically at $\lesssim 10$ au scales requires novel PSD simulations to properly address. Most analytic IMF models and analogies with planet formation in PSDs fail qualitatively to explain the simulation IMFs, though we discuss a couple of viable models., Comment: 39 pages, 23 figures, replaced with version accepted to the Open Journal of Astrophysics. Additional images and movies from the simulations available at http://www.tapir.caltech.edu/~phopkins/Site/animations/Movies_zoom.html

The observationally-inferred size versus stellar-mass relationship (SMR) for low-mass galaxies provides an important test for galaxy formation models. However, the relationship relies on assumptions that relate observed luminosity profiles to underlying stellar mass profiles. Here we use the Feedback in Realistic Environments simulations of low-mass galaxies to explore how the predicted SMR changes depending on whether one uses star-particle counts directly or mock observations. We reproduce the SMR found in The Exploration of Local Volume Satellites survey remarkably well only when we infer stellar masses and sizes using mock observations. However, when we use star particles to directly infer stellar masses and half-mass radii, we find that our galaxies are too large and obey a SMR with too little scatter compared to observations. This discrepancy between the "true" galaxy size and mass and those derived in the mock observation approach is twofold. First, our simulated galaxies have higher and more varied MLRs at a fixed colour than those commonly-adopted, which tends to underestimate their stellar masses compared to their true, simulated values. Second, our galaxies have radially increasing MLR gradients therefore using a single MLR tends to under-predict the mass in the outer regions. Similarly, the true half-mass radius is larger than the half-light radius because the light is more concentrated than the mass. If our simulations are accurate representations of the real universe, then the relationship between galaxy size and stellar mass is even tighter for low-mass galaxies than is commonly inferred from observed relations., Comment: 11 pages, 7 figures, 1 table

The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction on ELA achievement, as measured by SBA scores. We compared "striving learner" students who were assigned to use i-Ready Personalized Instruction (Treatment students) and "striving learner" students assigned to only receive i-Ready Diagnostic assessments (Comparison students). The study sample consisted of 8,722 "striving learner" students in Grades 3-6 from four school districts in California. All four districts used i-Ready Diagnostic assessments, but schools in one district assigned some students to receive the Personalized Instruction product in addition to the Diagnostic product. Striving learner students in this district assigned to both the Personalized Instruction and Diagnostic products were compared to striving learner students in the other districts who were only assigned to the Diagnostic product. Results of this study did not show any statistically significant associations between i-Ready Personalized Instruction usage and SBA ELA achievement. Notably, less than 20% of treatment students met i-Ready's recommended usage guidelines of at least 30 minutes per week of Personalized Instruction use for at least 18 weeks. Students that met usage guidelines directionally outperformed comparison students, although this impact was not significant. Overall, impacts were directionally positive for students who met usage guidelines, but the large percentage of students who did meet recommended guidelines may have impacted overall patterns of results. Taken with the results of similar mathematics achievement analyses, additional implementation focus may be warranted on ensuring that as many students as possible reach Personalized Instruction usage guidelines.

The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction on math achievement, as measured by SBA scores. We compared "striving learner" students who were assigned to use i-Ready Personalized Instruction (Treatment students) and "striving learner" students assigned to only receive i-Ready Diagnostic assessments (Comparison students). The study sample consisted of 11,034 "striving learner" students in Grades 3-6 from four school districts in California. All four districts used i-Ready Diagnostic assessments, but schools in one district assigned some students to receive the Personalized Instruction product in addition to the Diagnostic product. Striving learner students in this district assigned to both the Personalized Instruction and Diagnostic products were compared to striving learner students in the other districts who were only assigned to the Diagnostic product. Overall, this analysis presents promising evidence of i-Ready Personalized Instruction on student math achievement. This relationship between i-Ready Personalized Instruction and math achievement was strongest for students in Grades 4 and 5 and students identified as Black. Usage was also related to achievement and future studies should seek to further examine the reasons behind this variation in usage.

The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction on math achievement, as measured by SBA scores. We compared students in schools using i-Ready Personalized Instruction school-wide (Treatment students) and students who only received i-Ready Diagnostic assessments and who were in schools only partially using i-Ready Personalized Instruction (Comparison students). The study sample consisted of 5,330 students in 22 schools from Grades 3-6 from one school district in southern California. All schools used i-Ready Diagnostic assessments, but some schools were considered "full instruction" and assigned all students to receive both the Diagnostic and Personalized Instruction products. Students in these schools were compared to "diagnostic-only" students in other "partial instruction" schools where only some students received the Instruction product in addition to the Diagnostic product. (These Diagnostic and Instruction students in partial instruction schools are not included in this study.) Overall, this analysis presents promising evidence of i-Ready Personalized Instruction on student math achievement. This relationship between i-Ready Personalized Instruction and math achievement was strongest for students in Title I eligible schools, Grade 4, and among students who met the recommended usage guidelines. Future studies should seek to further examine the reasons behind this variation in usage.

The purpose of this evaluation was to examine the impact of i-Ready Personalized Instruction on reading achievement, as measured by SBA scores. We compared students in schools using i-Ready Personalized Instruction school-wide (Treatment students) to students who only received i-Ready Diagnostic assessments and who were in schools only partially using i-Ready Personalized Instruction (Comparison students). The study sample consisted of about 5,632 students in 22 schools from Grades 3-6 from one school district in southern California. All schools used i-Ready Diagnostic assessments, but some schools were considered "full instruction" and assigned all students to receive both the Diagnostic and Personalized Instruction products. Students in these schools were compared to "Diagnostic-only" students in other "partial Instruction" schools where only some students received the Personalized Instruction product in addition to the Diagnostic product. (These Diagnostic and Personalized Instruction students in partial instruction schools are not included in this study.) We did not find that treatment students had significantly higher SBA ELA scores, although directionally, treatment students averaged higher SBA ELA scores than did comparison students. Supplementary analysis of students achieving proficiency in reading on their SBA score showed that treatment did have a statistically significant impact on this outcome. Specifically, students in the treatment group had 1.6 times higher odds of scoring proficient (versus not scoring at least proficient) than their peers in the comparison group. No significant patterns were found in the impact of treatment on specific student subgroups. Overall, this analysis presents promising evidence of i-Ready Personalized Instruction on student reading proficiency. This relationship between i-Ready Personalized Instruction and reading achievement was strongest for students with higher usage of the program. Future studies should seek to further examine the reasons behind this variation in usage.