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We examine the efficiency of traditional school districts versus charter schools in providing students with teachers who meet their demographic and education needs. Using panel data from the state of Michigan, we estimate the relationship between enrollment of Black, Hispanic, special education, and English learner students and the presence of Black, Hispanic, Special Education, and ESL teachers, and test whether this relationship differs at charter and traditional district-run schools. Because charter schools typically have less market power in hiring than large districts, we compare charter school employment practices to traditional public schools in districts of comparable size. Our results suggest that charter schools are more likely to employ same race teachers for Black students but not Hispanic students, and districts schools are slightly better at providing ESL and SPED teachers. We conclude that charter autonomy does not necessary generate better student-teacher matches, but Michigan charters may occupy a market niche by serving Black students and staffing Black teachers.

In the context of an ongoing national conversation about teacher shortages, we build on prior literature on the efficacy of teacher certification pathways by comparing entry and exit patterns based on age at the time of certification. All trainees who complete a state certification process have invested substantial time and resources into entering teaching. Competing employment opportunities and expectations might vary with age. We use both linear regression and discrete-time hazard models to examine employment and subsequent exit of newly certified teacher trainees in Michigan from 2011 to 2023. We find that while mid-career entrants in their 30s and 40s compose a small share of new certificates, they are more likely to enter a teaching position and no more likely to subsequently exit than counterparts who were certified in their early 20s. Mid-career pathways also contribute to teacher diversity by attracting more Black and male teachers who enter and persist.

Despite increasing recognition of the importance of high-quality school leadership, we know remarkably little about principal skill development. Using administrative data from Tennessee, Oregon, and New York City, we estimate the returns to principal experience as measured by student outcomes, teacher hiring and retention patterns, and teacher and supervisor ratings of principals. The typical principal leads a school for only 3-5 years and leaves the principalship after 6-7 years. We find little evidence that school performance improves as principals gain experience, despite substantial improvement in supervisor ratings. Our results suggest that strategies intended to increase principal retention are unlikely to improve school outcomes absent more comprehensive efforts to strengthen the link between principal skill development and student and school outcomes.

Sub-Neptunes - volatile-rich exoplanets smaller than Neptune - are intrinsically the most common type of planet known. However, the formation and nature of these objects, as well as the distinctions between sub-classes (if any), remain unclear. Two powerful tools to tease out the secrets of these worlds are measurements of (i) atmospheric composition and structure revealed by transit and/or eclipse spectroscopy, and (ii) mass, radius, and density revealed by transit photometry and Doppler spectroscopy. Here we present OrCAS, a survey to better elucidate the origins, compositions, and atmospheres of sub-Neptunes. This radial velocity survey uses a repeatable, quantifiable metric to select targets suitable for subsequent transmission spectroscopy and address key science themes about the atmospheric & internal compositions and architectures of these systems. Our survey targets 26 systems with transiting sub-Neptune planet candidates, with the overarching goal of increasing the sample of such planets suitable for subsequent atmospheric characterization. This paper lays out our survey's science goals, defines our target prioritization metric, and performs light-curve fits and statistical validation using existing TESS photometry and ground-based follow-up observations. Our survey serves to continue expanding the sample of small exoplanets with well-measured properties orbiting nearby bright stars, ensuring fruitful studies of these systems for many years to come., Comment: 20 pages, 4 figures, 4 tables, 26 sub-Neptunes, 31 TOIs. Accepted to AJ

Infrared (IR) imaging offers advantages in several fields due to its unique ability of capturing content in extreme light conditions. However, the demanding hardware requirements of high-resolution IR sensors limit its widespread application. As an alternative, visible light can be used to synthesize IR images but this causes a loss of fidelity in image details and introduces inconsistencies due to lack of contextual awareness of the scene. This stems from a combination of using visible light with a standard dynamic range, especially under extreme lighting, and a lack of contextual awareness can result in pseudo-thermal-crossover artifacts. This occurs when multiple objects with similar temperatures appear indistinguishable in the training data, further exacerbating the loss of fidelity. To solve this challenge, this paper proposes CapHDR2IR, a novel framework incorporating vision-language models using high dynamic range (HDR) images as inputs to generate IR images. HDR images capture a wider range of luminance variations, ensuring reliable IR image generation in different light conditions. Additionally, a dense caption branch integrates semantic understanding, resulting in more meaningful and discernible IR outputs. Extensive experiments on the HDRT dataset show that the proposed CapHDR2IR achieves state-of-the-art performance compared with existing general domain transfer methods and those tailored for visible-to-infrared image translation.

Exposure correction methods aim to adjust the luminance while maintaining other luminance-unrelated information. However, current exposure correction methods have difficulty in fully separating luminance-related and luminance-unrelated components, leading to distortions in color, loss of detail, and requiring extra restoration procedures. Inspired by principal component analysis (PCA), this paper proposes an exposure correction method called luminance component analysis (LCA). LCA applies the orthogonal constraint to a U-Net structure to decouple luminance-related and luminance-unrelated features. With decoupled luminance-related features, LCA adjusts only the luminance-related components while keeping the luminance-unrelated components unchanged. To optimize the orthogonal constraint problem, LCA employs a geometric optimization algorithm, which converts the constrained problem in Euclidean space to an unconstrained problem in orthogonal Stiefel manifolds. Extensive experiments show that LCA can decouple the luminance feature from the RGB color space. Moreover, LCA achieves the best PSNR (21.33) and SSIM (0.88) in the exposure correction dataset with 28.72 FPS.

This paper proposes a novel analytical framework: Transition Network Analysis (TNA), an approach that integrates Stochastic Process Mining and probabilistic graph representation to model, visualize, and identify transition patterns in the learning process data. Combining the relational and temporal aspects into a single lens offers capabilities beyond either framework, including centralities to capture important learning events, community finding to identify patterns of behavior, and clustering to reveal temporal patterns. This paper introduces the theoretical and mathematical foundations of TNA. To demonstrate the functionalities of TNA, we present a case study with students (n=191) engaged in small-group collaboration to map patterns of group dynamics using the theories of co-regulation and socially-shared regulated learning. The analysis revealed that TNA could reveal the regulatory processes and identify important events, temporal patterns and clusters. Bootstrap validation established the significant transitions and eliminated spurious transitions. In doing so, we showcase TNA's utility to capture learning dynamics and provide a robust framework for investigating the temporal evolution of learning processes. Future directions include advancing estimation methods, expanding reliability assessment, exploring longitudinal TNA, and comparing TNA networks using permutation tests., Comment: Accepted at Learning Analytics & Knowledge (LAK '25)

Student dropout is a significant concern for educational institutions due to its social and economic impact, driving the need for risk prediction systems to identify at-risk students before enrollment. We explore the accuracy of such systems in the context of higher education by predicting degree completion before admission, with potential applications for prioritizing admissions decisions. Using a large-scale dataset from Danish higher education admissions, we demonstrate that advanced sequential AI models offer more precise and fair predictions compared to current practices that rely on either high school grade point averages or human judgment. These models not only improve accuracy but also outperform simpler models, even when the simpler models use protected sociodemographic attributes. Importantly, our predictions reveal how certain student profiles are better matched with specific programs and fields, suggesting potential efficiency and welfare gains in public policy. We estimate that even the use of simple AI models to guide admissions decisions, particularly in response to a newly implemented nationwide policy reducing admissions by 10 percent, could yield significant economic benefits. However, this improvement would come at the cost of reduced human oversight and lower transparency. Our findings underscore both the potential and challenges of incorporating advanced AI into educational policymaking.

Social influence plays an important role in shaping individual opinions and actions, particularly in our digitally connected world. AI-generated, personalized content has led to serious and well-founded concerns, including United States Supreme Court Cases regarding the potential for the radicalization of individuals based on social influence. Motivated by these developments, we present a case study investigating the effects of small but intentional perturbations on the integrity of a simple social network. We employ Taylor's classic model of social influence and use tools from robust control theory (most notably the Dynamic Structure Function (DSF)), to identify precisely the perturbations that are sufficient to qualitatively alter the system's equilibrium and also minimal in norm. In particular, we examine two scenarios: perturbations to an existing link and perturbations taking the form of the addition of a new link to the network. In each case, we identify destabilizing perturbations and simulate their effects. Remarkably, we find that even small alterations to network structure may cause sentiments to grow in magnitude without bound, indicating the potential for large-scale shifts in collective behavior to be triggered by minor adjustments in social influence. Our findings emphasize the imperative need for further investigation into vulnerabilities in real-world social networks, where such dynamics may already exist.

We propose generalized conditional functional principal components analysis (GC-FPCA) for the joint modeling of the fixed and random effects of non-Gaussian functional outcomes. The method scales up to very large functional data sets by estimating the principal components of the covariance matrix on the linear predictor scale conditional on the fixed effects. This is achieved by combining three modeling innovations: (1) fit local generalized linear mixed models (GLMMs) conditional on covariates in windows along the functional domain; (2) conduct a functional principal component analysis (FPCA) on the person-specific functional effects obtained by assembling the estimated random effects from the local GLMMs; and (3) fit a joint functional mixed effects model conditional on covariates and the estimated principal components from the previous step. GC-FPCA was motivated by modeling the minute-level active/inactive profiles over the day ($1{,}440$ 0/1 measurements per person) for $8{,}700$ study participants in the National Health and Nutrition Examination Survey (NHANES) 2011-2014. We show that state-of-the-art approaches cannot handle data of this size and complexity, while GC-FPCA can., Comment: 38 pages with supplementary material, 5 figures for the main article and 4 supplementary figures, 3 tables for the main article and 10 supplementary tables, submitted to Journal of Computational and Graphical Statistics (JCGS)

Self-assembly of nanoscale synthetic subunits is a promising bottom-up strategy for fabrication of functional materials. Here, we introduce a design principle for DNA origami nanoparticles of 50-nm size, exploiting modularity, to make a family of versatile subunits that can target an abundant variety of self-assembled structures. The subunits are based on a core module that remains constant among all the subunits. Variable bond modules and angle modules are added to the exterior of the core to control interaction specificity, strength and structural geometry. A series of subunits with designed bond/angle modules are demonstrated to self-assemble into a rich variety of structures with different Gaussian curvatures, exemplified by sheets, spherical shells, and tubes. The design features flexible joints implemented using single-stranded angle modules between adjacent subunits whose mechanical properties, such as bending elastic moduli, are inferred from cryo-EM. Our findings suggest that incorporating a judicious amount of flexibility in the bond provides error tolerances in design and fabrication while still guaranteeing target fidelity. Lastly, while increasing flexibility could introduce greater variability and potential errors in assembly, these effects can be counterbalanced by increasing the number of distinct bonds, thereby allowing for precise targeting of specific structural binding angles within a broad range of configurations., Comment: 43 pages, 4 main figures, 9 supplementary figures

Background: $^{15}$Be is an unbound nuclide that has been observed to decay by one-neutron emission. Shell model calculations predict two low-lying states in its energy spectrum, however, only a single resonance has been observed from coincident measurements of $^{14}$Be+n. It has been suggested that the yet unobserved state may decay sequentially through the first excited state in $^{14}$Be followed by a two-neutron emission to $^{12}$Be. Purpose: The ground state of $^{15}$Be has yet to be confirmed. A search for this predicted $^{15}$Be state by reconstructing $^{12}$Be$+3$n events allows a possible determination of its ground state properties. Methods: A neutron-pickup reaction was performed with a $^{14}$Be beam on a CD$_2$ target to populate unbound $^{15}$Be states. Decay energies were reconstructed using invariant mass spectroscopy by detecting $^{12}$Be daughter nuclei in coincidence with up to three neutrons. Results: Evidence for at least one resonance in $^{15}$Be is presented based on the reconstruction of $^{12}$Be$+3$n events. Through comparison with simulations, the energy of the strongest resonance in the analyzed reaction and decay channel is determined to be $E_{^{12}Be+3n}=330(20)$ keV. Conclusions: The inclusion of a new $^{15}$Be state among the $^{12}$Be+$3$n events lower in relative decay energy than the previous $^{14}$Be+n observations provides the best fit to the data. Because this suggested new state would be lower in energy than the previously observed state, it is a candidate for the ground state of $^{15}$Be., Comment: Accepted to Physical Review C

Recent advances in synthetic methods enable designing subunits that self-assemble into structures with well-defined sizes and architectures, but yields are frequently suppressed by the formation of off-target metastable structures. Increasing the complexity (number of distinct inter-subunit interaction types) can inhibit off-target structures, but leads to slower kinetics and higher synthesis costs. Here, we use icosahedral shells formed of programmable triangular subunits as a model system, and identify design principles that produce the highest target yield at the lowest complexity. We use a symmetry-based construction to create a range of design complexities, starting from the maximal symmetry Caspar-Klug assembly up to the fully addressable, zero-symmetry assembly. Kinetic Monte Carlo simulations reveal that the most prominent defects leading to off-target assemblies are a class of disclinations. We derive symmetry-based rules for identifying the optimal (lowest-complexity, highest-symmetry) design that inhibits these disclinations, leading to robust, high-fidelity assembly of targets with arbitrarily large sizes. Optimal complexity varies non-monotonically with target size, with `magic' sizes appearing for high-symmetry designs in which symmetry axes do not intersect vertices of the triangular net. The optimal designs at magic sizes require $12$ times fewer inequivalent interaction-types than the (minimal symmetry) fully addressable construction., Comment: 5 pages, 4 figures. SI.pdf with additional information provided

Planets form from the same cloud of molecular gas and dust as their host stars. Confirming if planetary bodies acquire the same refractory element composition as their natal disc during formation, and how efficiently volatile elements are incorporated into growing planets, is key to linking the poorly constrained interior composition of rocky exoplanets to the observationally-constrained composition of their host star. Such comparisons also afford insight into the planet formation process. This work compares planetary composition with host-star composition using observations of a white dwarf that has accreted planetary material and its F-type star wide binary companion as a reference for the composition of the natal molecular gas and dust. Spectroscopic analysis reveals abundances of Fe, Mg, Si, Ca, and Ti in both stars. We use the white dwarf measurements to estimate the composition of the exoplanetary material and the F-type companion to constrain the composition of the material the planet formed from. Comparing planetary material to the composition of its natal cloud, our results reveal that the planetary material is depleted in moderate refractories (Mg, Si, Fe) relative to the refractory material (Ca, Ti). Grouping elements based on their condensation temperatures is key to linking stellar and planetary compositions. Fractionation during formation or subsequent planetary evolution leads to the depletion of moderate refractories from the planetary material accreted by the white dwarf. This signature, as seen for bulk Earth, will likely be present in the composition of many exoplanets relative to their host-stars., Comment: 14 pages (without including appendix). 12 Figures. Accepted for publication in A&A

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Electrochemical biosensors ("E-AB" or "E-DNA" type sensors) that utilize square-wave voltammetry originated in academic labs with a few standard experimental configurations for the electrochemical cell and data analysis. We report here on adaptations of these approaches that are friendly to novice scientists such as those in undergraduate laboratories. These approaches utilize commercially available components, low volumes, work over extended periods and enable facile analysis using a custom excel sheet.

Topological Insulators (TIs) present an interesting materials platform for nanoscale, high frequency devices because they support high mobility, low scattering electronic transport within confined surface states. However, a robust methodology to control the properties of surface plasmons in TIs has yet to be developed. We propose that charge transfer between Bi$_2$Se$_3$ and crystalline C$_{60}$ films may provide tunable control of the two-dimensional plasmons in Bi$_2$Se$_3$. We have grown heterostructures of Bi$_2$Se$_3$/C$_{60}$ with exceptional crystallinity. Electron energy loss spectroscopy (EELS) reveals significant hybridisation of $\pi$ states at the interface, despite the expectation for only weak van der Waals interactions, including quenching of 2D plasmons. Momentum-resolved EELS measurements are used to probe the plasmon dispersion, with Density Functional Theory predictions providing an interpretation of results based on interfacial charge dipoles. Our measurements suggest a robust methodology for tuneable TI interfaces that can be engineered for plasmonic applications in computing, communications and sensing.

As a new generation of large-sky spectroscopic surveys comes online, the enormous data volume poses unprecedented challenges in classifying spectra. Modern unsupervised techniques have the power to group spectra based on their dominant features, circumventing the complete reliance on training data suffered by supervised methods. We outline the use of dimensionality reduction to generate a 2D map of the structure of an intermediate-resolution spectroscopic dataset. This technique efficiently separates white dwarfs of different spectral classes in the Dark Energy Spectroscopic Instrument's Early Data Release (DESI EDR), identifying spectral features that had been missed even by visual classification. By focusing the method on particular spectral regions, we identify white dwarfs with helium features at 90 per cent recall, and cataclysmic variables at 100 per cent recall, illustrating rapid selection of low-contamination samples from spectroscopic surveys. We also demonstrate the use of dimensionality reduction in a supervised manner, outlining a procedure to classify any white dwarf spectrum in comparison with those in the DESI EDR. With upcoming surveys promising tens of millions of spectra, our work highlights the potential for semi-supervised techniques as an efficient means of classification and dataset visualisation., Comment: 15 pages, 8 figures. Accepted for publication in MNRAS

We present an analysis of 126 new radial velocity measurements from the MAROON-X spectrograph to investigate the TOI-1266 system, which hosts two transiting sub-Neptunes at 10.8 and 18.8 days. We measure masses of $M_{b}=4.01~\pm~0.55~M_{\oplus}$ for TOI-1266 b and $M_{c}=2.00~\pm~0.72~M_{\oplus}$ for TOI-1266 c. Our mass measurements agree with existing HARPS-N observations which we combined using a weighted average yielding masses for TOI-1266 b, and c of $M_{b}=4.10~\pm~0.43~M_{\oplus}$, $M_{c}=2.4~\pm~0.54~M_{\oplus}$ respectively. The combined dataset enabled a $\approx30\%$ improvement in mass precision. With bulk densities of $\rho_{b}$ = 1.25 $\pm$ 0.36 g cm$^{-3}$ and $\rho_{c}$ = 1.36 $\pm$ 0.31 g cm$^{-3}$, the planets are among the lowest density sub-Neptunes orbiting an M dwarf. They are both consistent with rocky cores surrounded by hydrogen helium envelopes. TOI-1266 c may also be consistent with a water-rich composition, but we disfavor that interpretation from an Occam's razor perspective., Comment: 8 pages, 6 figures

The physics of recombination lines (RLs) in the HeI singlet system is expected to be relatively simple, supported by accurate atomic models. We examine the intensities of HeI singlets $\lambda \lambda$3614, 3965, 5016, 6678, 7281 and the triplet HeI $\lambda$5876 in various types of ionized nebulae and compare them with theoretical predictions to test the validity of the ``Case B'' recombination scenario and the assumption of thermal homogeneity. Our analysis includes 85 spectra from Galactic and extragalactic HII regions, 90 from star-forming galaxies, and 218 planetary nebulae, all compiled by the DEep Spectra of Ionized REgions Database Extended (DESIRED-E) project. By evaluating the ratios HeI $\lambda$7281/$\lambda$6678 and HeI $\lambda$7281/$\lambda$5876, we determine $T_e$(HeI) and compare it with direct measurements of $T_e$([OIII] $\lambda$4363/$\lambda$5007). We find that $T_e$(HeI) is systematically lower than $T_e$([OIII]) across most objects and nebula types. Additionally, we identify a correlation between the abundance discrepancy factor (ADF(O$^{2+}$)) and the difference $T_e$([OIII]) - $T_e$(HeI) for planetary nebulae. We explore two potential explanations: photon loss from $n^1P \rightarrow 1^1S$ transitions and temperature inhomogeneities. Deviations from ``Case B'' may indicate photon absorption by HI rather than HeI and/or generalized ionizing photon escape, highlighting the need for detailed consideration of radiative transfer effects. If temperature inhomogeneities are widespread, identifying a common physical phenomenon affecting all ionized nebulae is crucial. Our results suggest that both scenarios can contribute to the observed discrepancies., Comment: Submitted to the AAS, comments and feedback from the community are welcome

Different brain imaging modalities offer unique insights into brain function and structure. Combining them enhances our understanding of neural mechanisms. Prior multimodal studies fusing functional MRI (fMRI) and structural MRI (sMRI) have shown the benefits of this approach. Since sMRI lacks temporal data, existing fusion methods often compress fMRI temporal information into summary measures, sacrificing rich temporal dynamics. Motivated by the observation that covarying networks are identified in both sMRI and resting-state fMRI, we developed a novel fusion method, by combining deep learning frameworks, copulas and independent component analysis (ICA), named copula linked parallel ICA (CLiP-ICA). This method estimates independent sources for each modality and links the spatial sources of fMRI and sMRI using a copula-based model for more flexible integration of temporal and spatial data. We tested CLiP-ICA using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results showed that CLiP-ICA effectively captures both strongly and weakly linked sMRI and fMRI networks, including the cerebellum, sensorimotor, visual, cognitive control, and default mode networks. It revealed more meaningful components and fewer artifacts, addressing the long-standing issue of optimal model order in ICA. CLiP-ICA also detected complex functional connectivity patterns across stages of cognitive decline, with cognitively normal subjects generally showing higher connectivity in sensorimotor and visual networks compared to patients with Alzheimer, along with patterns suggesting potential compensatory mechanisms., Comment: 25 pages, 10 figures, journal article

The propagation of wave fields and their interactions with matter are important for established and emerging fields in optical sciences. Efficient methods for predicting such behaviour have been employed routinely for coherent sources. However, most real world optical systems exhibit partial coherence, for which the present mathematical description involves high dimensional complex functions and hence poses challenges for numerical implementations. This demands significant computational resources to determine the properties of partially coherent wavefields. Here, we describe the novel Phase-Space (PS) propagator, an efficient and self-consistent technique for free space propagation of wave fields which are partially coherent in the spatial domain. The PS propagator makes use of the fact that the propagation of a wave field in free space is equivalent to a shearing of the corresponding PSD function. Computationally, this approach is simpler and the need for using different propagation methods for near and far-field regions is removed., Comment: 17 pages, 4 figures, to be submitted to Optics Communications For the associated Python package, see https://github.com/jakerogers-1/Phase-Space-Propagator

Accurate forest canopy height estimation is essential for evaluating aboveground biomass and carbon stock dynamics, supporting ecosystem monitoring services like timber provisioning, climate change mitigation, and biodiversity conservation. However, despite advancements in spaceborne LiDAR technology, data for northern high latitudes remain limited due to orbital and sampling constraints. This study introduces a methodology for generating spatially continuous, high-resolution canopy height and uncertainty estimates using Deep Learning Regression models. We integrate multi-source, multi-seasonal satellite data from Sentinel-1, Landsat, and ALOS-PALSAR-2, with spaceborne GEDI LiDAR as reference data. Our approach was tested in Ontario, Canada, and validated with airborne LiDAR, demonstrating strong performance. The best results were achieved by incorporating seasonal Sentinel-1 and Landsat features alongside PALSAR data, yielding an R-square of 0.72, RMSE of 3.43 m, and bias of 2.44 m. Using seasonal data instead of summer-only data improved variability by 10%, reduced error by 0.45 m, and decreased bias by 1 m. The deep learning model's weighting strategy notably reduced errors in tall canopy height estimates compared to a recent global model, though it overestimated lower canopy heights. Uncertainty maps highlighted greater uncertainty near forest edges, where GEDI measurements are prone to errors and SAR data may encounter backscatter issues like foreshortening, layover, and shadow. This study enhances canopy height estimation techniques in areas lacking spaceborne LiDAR coverage, providing essential tools for forestry, environmental monitoring, and carbon stock estimation.

This paper presents a divergence cleaning formulation for the velocity in the weakly compressible smoothed particle hydrodynamics (SPH) scheme. The proposed hyperbolic/parabolic divergence cleaning, ensures that the velocity divergence, $div(\mathbf{u})$, is minimised throughout the simulation. The divergence equation is coupled with the momentum conservation equation through a scalar field $\psi$. A parabolic term is added to the time-evolving divergence equation, resulting in a hyperbolic/parabolic form, dissipating acoustic waves with a speed of sound proportional to the local Mach number in order to maximise dissipation of the velocity divergence, preventing unwanted diffusion of the pressure field. The $div(\mathbf{u})$-SPH algorithm is implemented in the open-source weakly compressible SPH solver DualSPHysics. The new formulation is validated against a range of challenging 2-D test cases including the Taylor-Green vortices, patch impact test, jet impinging on a surface, and wave impact in a sloshing tank. The results show that the new formulation reduces the divergence in the velocity field by at least one order of magnitude which prevents spurious numerical noise and the formation of unphysical voids. The temporal evolution of the impact pressures shows that the $div(\mathbf{u})$-SPH formulation virtually eliminates unwanted acoustic pressure oscillations. Investigation of particle resolution confirms that the new $div(\mathbf{u})$-SPH formulation does not reduce the spatial convergence rate.

HIP 41378 f is a sub-Neptune exoplanet with an anomalously low density. Its long orbital period and deep transit make it an ideal candidate for detecting oblateness photometrically. We present a new cross-platform, GPU-enabled code greenlantern, suitable for computing transit light curves of oblate planets at arbitrary orientations. We then use Markov Chain Monte Carlo to fit K2 data of HIP 41378 b, d, and f, specifically examining HIP 41378 f for possible oblateness and obliquity. We find that the flattening of HIP 41378 f is $f \leq 0.18$ at the 95% confidence level, consistent with a rotation period of $P_\text{rot} \geq 22.2$ hr. In the future, high-precision data from JWST has the potential to tighten such a constraint and can differentiate between spherical and flattened planets., Comment: 14 pages, 10 figures, 2 tables. Submitted to ApJL. Code available at https://github.com/emprice/greenlantern

As a step towards the ultimate goal of a high-precision mass measurement of doubly-magic $^{100}$Sn, the mass of $^{103}$Sn was measured at the Low Energy Beam and Ion Trap (LEBIT) located at the Facility for Rare Isotope Beams (FRIB). Utilizing the time-of-flight ion cyclotron resonance (ToF-ICR) technique, a mass uncertainty of 3.7~keV was achieved, an improvement by more than an order of magnitude compared to a recent measurement performed in 2023 at the Cooler Storage Ring (CSRe) in Lanzhou. Although the LEBIT and CSRe mass measurements of $^{103}$Sn are in agreement, they diverge from the experimental mass value reported in the 2016 version of the Atomic Mass Evaluation (AME2016), which was derived from the measured $Q_{\beta^+}$ value and the mass of $^{103}$In. In AME2020, this indirectly measured $^{103}$Sn mass was classified as a `seriously irregular mass' and replaced with an extrapolated value, which aligns with the most recent measured values from CSRe and LEBIT. As such, the smoothness of the mass surface is confidently reestablished for $^{103}$Sn. Furthermore, LEBIT's mass measurement of $^{103}$Sn enabled a significant reduction in the mass uncertainties of five parent isotopes which are now dominated by uncertainties in their respective $Q$-values.

This report highlights the findings of the sixteenth survey of states conducted by the National Center on Educational Outcomes (NCEO). It has been administered for more than three decades to collect information from states about the participation and performance of students with disabilities in the assessments that comprise the comprehensive assessment system. Topics addressed included: accessibility and accommodations, alternate assessments based on alternate academic achievement standards (AA-AAAS), interim assessments, accountability, English learners with disabilities, technology, graduation requirements, State Systemic Improvement Plans (SSIPs)/State-identified Measurable Results (SiMRs), family engagement, and technical assistance needs.

This is the third in a series of annual reports about teacher shortages in Michigan that the state legislature requested in December 2020 (2020 PA 316). Although the state data on this topic is limited, these analyses still help to paint a picture of teacher shortages across Michigan, assist policymakers to target policies and programs in ways that can best support the state and local communities in growing their teacher workforces, and highlight ways that new or better data may provide a deeper understanding of local and statewide teacher shortages. In addition to updating the analyses from EPIC's first comprehensive report, we continue to adjust and expand on our past analyses based on the results from prior reports and any additions or improvements to the data available each year.

Super-Earths are highly irradiated, small planets with bulk densities approximately consistent with Earth. We construct combined interior-atmosphere models of super-Earths that trace the partitioning of water throughout a planet, including an iron-rich core, silicate-rich mantle, and steam atmosphere. We compare these models with exoplanet observations to infer a $1\sigma$ upper limit on total water mass fraction of $\lesssim 3\%$ at the population level. We consider end-member scenarios that may change this value, including the efficiency of mantle outgassing and escape of high mean-molecular weight atmospheres. Although our constraints are agnostic as to the origin of water, we show that our upper limits are consistent with its production via chemical reactions of primordial hydrogen-dominated atmospheres with magma oceans. This mechanism has also been hypothesised to explain Earth's water content, possibly pointing to a unified channel for the origins of water on small terrestrial planets., Comment: Submitted to ApJ

Precision spraying evaluation requires automation primarily in post-spraying imagery. In this paper we propose an eXplainable Artificial Intelligence (XAI) computer vision pipeline to evaluate a precision spraying system post-spraying without the need for traditional agricultural methods. The developed system can semantically segment potential targets such as lettuce, chickweed, and meadowgrass and correctly identify if targets have been sprayed. Furthermore, this pipeline evaluates using a domain-specific Weakly Supervised Deposition Estimation task, allowing for class-specific quantification of spray deposit weights in {\mu}L. Estimation of coverage rates of spray deposition in a class-wise manner allows for further understanding of effectiveness of precision spraying systems. Our study evaluates different Class Activation Mapping techniques, namely AblationCAM and ScoreCAM, to determine which is more effective and interpretable for these tasks. In the pipeline, inference-only feature fusion is used to allow for further interpretability and to enable the automation of precision spraying evaluation post-spray. Our findings indicate that a Fully Convolutional Network with an EfficientNet-B0 backbone and inference-only feature fusion achieves an average absolute difference in deposition values of 156.8 {\mu}L across three classes in our test set. The dataset curated in this paper is publicly available at https://github.com/Harry-Rogers/PSIE

Motivated by its application in the frequency assignment problem for cellular networks, conflict-free coloring was first studied by Even et al. in [Conflict-free colorings of simple geometric regions with applications to frequency assignment in cellular networks, SIAM Journal on Computing, 2004]. A \emph{conflict-free coloring} of a hypergraph $\mathcal{H}$ is an assignment of colors to the vertex set of $\mathcal{H}$ such that every hyperedge in $\mathcal{H}$ has a vertex whose color is distinct from every other vertex in that hyperedge. The minimum number of colors required for such a coloring is known as the \emph{conflict-free chromatic number} of $\mathcal{H}$. Conflict-free coloring has also been studied on open/closed neighborhood hypergraphs of a given graph. In this paper, we study the list variant of conflict-free coloring where, for every vertex $v$, we are given a list of admissible colors $L_v$ such that $v$ is allowed to be colored only from $L_v$. We prove upper bounds for the list conflict-free chromatic number of general hypergraphs and graphs., Comment: 17 pages

We present ConvexECG, an explainable and resource-efficient method for reconstructing six-lead electrocardiograms (ECG) from single-lead data, aimed at advancing personalized and continuous cardiac monitoring. ConvexECG leverages a convex reformulation of a two-layer ReLU neural network, enabling the potential for efficient training and deployment in resource constrained environments, while also having deterministic and explainable behavior. Using data from 25 patients, we demonstrate that ConvexECG achieves accuracy comparable to larger neural networks while significantly reducing computational overhead, highlighting its potential for real-time, low-resource monitoring applications.

Classification models are a key component of structural digital twin technologies used for supporting asset management decision-making. An important consideration when developing classification models is the dimensionality of the input, or feature space, used. If the dimensionality is too high, then the `curse of dimensionality' may rear its ugly head; manifesting as reduced predictive performance. To mitigate such effects, practitioners can employ dimensionality reduction techniques. The current paper formulates a decision-theoretic approach to dimensionality reduction for structural asset management. In this approach, the aim is to keep incurred misclassification costs to a minimum, as the dimensionality is reduced and discriminatory information may be lost. This formulation is constructed as an eigenvalue problem, with separabilities between classes weighted according to the cost of misclassifying them when considered in the context of a decision process. The approach is demonstrated using a synthetic case study., Comment: To appear in the Proceedings of ISMA 2024 (International Conference on Noise and Vibration Engineering) and USD2024 (International Conference on Uncertainty in Structural Dynamics), Leuven, Belgium

Over the Nullarbor Plain in South Australia, the Desert Fireball Network detected a fireball on the night of 1 June 2019 (7:30 pm local time), and six weeks later recovered a single meteorite (42 g) named Arpu Kuilpu. This meteorite was then distributed to a consortium of collaborating institutions to be measured and analyzed by a number of methodologies including: SEM-EDS, EPMA, ICP-MS, gamma-ray spectrometry, ideal gas pycnometry, magnetic susceptibility measurement, {\mu}CT, optical microscopy, and accelerator and noble gas mass spectrometry techniques. These analyses revealed that Arpu Kuilpu is an unbrecciated H5 ordinary chondrite, with minimal weathering (W0-1) and minimal shock (S2). The olivine and pyroxene mineral compositions (in mol%) are Fa: 19.2 +- 0.2, and Fs: 16.8 +- 0.2, further supporting the H5 type and class. The measured oxygen isotopes are also consistent with an H chondrite ({\delta}17O = 2.904 +- 0.177; {\delta}18O = 4.163 +- 0.336; {\Delta}17O = 0.740 +- 0.002). Ideal gas pycnometry measured bulk and grain densities of 3.66 +- 0.02 and 3.77 +- 0.02 g cm-3, respectively, yielding a porosity of 3.0 % +- 0.7. The magnetic susceptibility of this meteorite is log X = 5.16 +- 0.08. The most recent impact-related heating event experienced by Arpu Kuilpu was measured by 40Ar/39Ar chronology to be 4467 +- 16 Ma, while the cosmic ray exposure age is estimated to be between 6-8 Ma. The noble gas isotopes, radionuclides, and fireball observations all indicate that Arpu Kuilpu's meteoroid was quite small (maximum radius of 10 cm, though more likely between 1-5 cm). Although this meteorite is a rather ordinary ordinary chondrite, its prior orbit resembled that of a Jupiter Family Comet (JFC) further lending support to the assertion that many cm- to m-sized objects on JFC orbits are asteroidal rather than cometary in origin.