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This study uses a concurrent embedded mixed-methods design to assess the impact of additional funding on student outcomes in a large, urban school district in the Southeastern United States. The district implemented student-based budgeting (SBB), which allocates dollars to schools based on student characteristics using a weighted student funding (WSF) formula and provides flexibility to principals to allocate those dollars under site-based budgeting. Using simulated instruments in a difference-in-differences framework, we estimate the impact of additional funding on student outcomes provided by WSF. Student test scores in math and ELA increased by 0.14 and 0.12 standard deviations, respectively. Our qualitative analysis suggests that the flexibility given to principals was a key mechanism that improved student outcomes.

Students with disabilities represent 15% of U.S. public school students. Individualized Education Programs (IEPs) inform how students with disabilities experience education. Very little is known about the aspects of IEPs as they are historically paper-based forms. In this study, we develop a coding taxonomy to categorize IEP goals into 10 subjects and 40 skills. We apply the taxonomy to digital IEP records for an entire state to understand the variety of IEP goal subjects and skills prescribed to students with different disabilities. This study highlights the utility of studying digital IEP records for informing practice and policy.

Galaxy mergers are a key driver of galaxy formation and evolution, including the triggering of AGN and star formation to a still unknown degree. We thus investigate the impact of galaxy mergers on star formation and AGN activity using a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields (CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, and MACS-0416), collectively covering an unmasked area of 189 arcmin$^2$. We focuses on star formation rate (SFR) enhancement, AGN fraction, and AGN excess in major merger ($\mu > 1/4$) close-pair samples, defined by $\Delta z < 0.3$ and projected separations $r_p < 100$ kpc, compared to non-merger samples. We find that SFR enhancement occurs only at $r_p < 20$ kpc, with values of $0.25 \pm 0.10$ dex and $0.26 \pm 0.11$ dex above the non-merger medians for $z = [4.5, 6.5]$ and $z = [6.5, 8.5]$, respectively. No other statistically significant enhancements in galaxy sSFR or stellar mass are observed at any projected separation or redshift bin. We also compare our observational results with predictions from the SC-SAM simulation and find no evidence of star formation enhancement in the simulations at any separation range. Finally, we examine the AGN fraction and AGN excess, finding that the fraction of AGNs in AGN-galaxy pairs, relative to the total AGN population, is $3.25^{+1.50}_{-1.06}$ times greater than the fraction of galaxy pairs relative to the overall galaxy population at the same redshift. We find that nearly all AGNs have a companion within 100 kpc and observe an excess AGN fraction in close-pair samples compared to non-merger samples. This excess is found to be $1.26 \pm 0.06$ and $1.34 \pm 0.06$ for AGNs identified via the inferred BPT diagram and photometric SED selection, respectively., Comment: 17 Pages, 7 Figures, Submitted to MNRAS

Understanding planetary habitability is one of the major challenges of the current scientific era, particularly given the discovery of a large and diverse terrestrial exoplanet population. Discerning the primary factors that contribute to planetary habitability may be extracted through a detailed examination of the terrestrial planets within the Solar System, most particularly Venus, Earth, and Mars, and the evolution of their interiors and atmospheres through time. Here, we provide a detailed description of the fundamental properties of these three planets, the effects of solar evolution, and the potential contributions of these various aspects toward driving their evolutionary pathways. We argue that evolution of Venus, Earth, and Mars provide essential templates from which a more comprehensive approach toward the study of planetary habitability may be derived., Comment: 45 pages, 3 figures, accepted for publication in "The Archean Earth" (ed.: M. Homan, P. Mason, R. Ernst, T. Lyons, C. Heubeck, D. Papineau, E. Stueeken, R. Mazumder, A. Webb, W. Altermann), Elsevier

Models that rely on subword tokenization have significant drawbacks, such as sensitivity to character-level noise like spelling errors and inconsistent compression rates across different languages and scripts. While character- or byte-level models like ByT5 attempt to address these concerns, they have not gained widespread adoption -- processing raw byte streams without tokenization results in significantly longer sequence lengths, making training and inference inefficient. This work introduces MrT5 (MergeT5), a more efficient variant of ByT5 that integrates a token deletion mechanism in its encoder to dynamically shorten the input sequence length. After processing through a fixed number of encoder layers, a learnt delete gate determines which tokens are to be removed and which are to be retained for subsequent layers. MrT5 effectively ``merges'' critical information from deleted tokens into a more compact sequence, leveraging contextual information from the remaining tokens. In continued pre-training experiments, we find that MrT5 can achieve significant gains in inference runtime with minimal effect on performance. When trained on English text, MrT5 demonstrates the capability to transfer its deletion feature zero-shot across several languages, with significant additional improvements following multilingual training. Furthermore, MrT5 shows comparable accuracy to ByT5 on downstream evaluations such as XNLI and character-level tasks while reducing sequence lengths by up to 80%. Our approach presents a solution to the practical limitations of existing byte-level models.

Design skills are increasingly recognized as a core competency for software professionals. Unfortunately, these skills are difficult to teach because design requires freedom and open-ended thinking, but new designers require a structured process to keep them from being overwhelmed by possibilities. We scaffolded this by creating worksheets for every Design Thinking step, and embedding them in a PowerPoint deck on which students can collaborate. We present our experience teaching a team design project course to 200 first-year-university students, taking them from user interviews to functional prototypes. To challenge and support every student in a class where high school programming experience ranged from zero hours to three computer science courses, we gave teams the option of developing single-user or multi-user (distributed) web applications, using two Event-Driven Programming frameworks. We identified common failure modes from previous years, and developed the scaffolded approach and problem definition to avoid them. The techniques developed include using a "game matrix" for structured brainstorming and developing projects that require students to empathize with users very different from themselves. We present quantitative and qualitative evidence from surveys and focus groups that show how these strategies impacted learning, and the extent to which students' awareness of the strategies led to the development of metacognitive abilities.

How can instructors facilitate spreading out the work in a software engineering or computer science capstone course across time and among team members? Currently teams often compromise the quality of their learning experience by frantically working before each deliverable. Some team members further compromise their own learning, and that of their colleagues, by not contributing their fair share to the team effort. To mitigate these problems, we propose using a GitHub template that contains all the initial infrastructure a team needs, including the folder structure, text-based template documents and template issues. In addition, we propose each team begins the year by identifying specific quantifiable individual productivity metrics for monitoring, such as the count of meetings attended, issues closed and number of commits. Initial data suggests that these steps may have an impact. In 2022/23 we observed 24% of commits happening on the due dates. After partially introducing the above ideas in 2023/24, this number improved to 18%. To measure the fairness we introduce a fairness measure based on the disparity between number of commits between all pairs of teammates. Going forward we propose an experiment where commit data and interview data is compared between teams that use the proposed interventions and those that do not.

Vector Quantized Variational AutoEncoders (VQ-VAEs) are designed to compress a continuous input to a discrete latent space and reconstruct it with minimal distortion. They operate by maintaining a set of vectors -- often referred to as the codebook -- and quantizing each encoder output to the nearest vector in the codebook. However, as vector quantization is non-differentiable, the gradient to the encoder flows around the vector quantization layer rather than through it in a straight-through approximation. This approximation may be undesirable as all information from the vector quantization operation is lost. In this work, we propose a way to propagate gradients through the vector quantization layer of VQ-VAEs. We smoothly transform each encoder output into its corresponding codebook vector via a rotation and rescaling linear transformation that is treated as a constant during backpropagation. As a result, the relative magnitude and angle between encoder output and codebook vector becomes encoded into the gradient as it propagates through the vector quantization layer and back to the encoder. Across 11 different VQ-VAE training paradigms, we find this restructuring improves reconstruction metrics, codebook utilization, and quantization error. Our code is available at https://github.com/cfifty/rotation_trick.

Dense linear layers are the dominant computational bottleneck in large neural networks, presenting a critical need for more efficient alternatives. Previous efforts focused on a small number of hand-crafted structured matrices and neglected to investigate whether these structures can surpass dense layers in terms of compute-optimal scaling laws when both the model size and training examples are optimally allocated. In this work, we present a unifying framework that enables searching among all linear operators expressible via an Einstein summation. This framework encompasses many previously proposed structures, such as low-rank, Kronecker, Tensor-Train, Block Tensor-Train (BTT), and Monarch, along with many novel structures. To analyze the framework, we develop a taxonomy of all such operators based on their computational and algebraic properties and show that differences in the compute-optimal scaling laws are mostly governed by a small number of variables that we introduce. Namely, a small $\omega$ (which measures parameter sharing) and large $\psi$ (which measures the rank) reliably led to better scaling laws. Guided by the insight that full-rank structures that maximize parameters per unit of compute perform the best, we propose BTT-MoE, a novel Mixture-of-Experts (MoE) architecture obtained by sparsifying computation in the BTT structure. In contrast to the standard sparse MoE for each entire feed-forward network, BTT-MoE learns an MoE in every single linear layer of the model, including the projection matrices in the attention blocks. We find BTT-MoE provides a substantial compute-efficiency gain over dense layers and standard MoE., Comment: NeurIPS 2024. Code available at https://github.com/AndPotap/einsum-search

Offline black-box optimization aims to maximize a black-box function using an offline dataset of designs and their measured properties. Two main approaches have emerged: the forward approach, which learns a mapping from input to its value, thereby acting as a proxy to guide optimization, and the inverse approach, which learns a mapping from value to input for conditional generation. (a) Although proxy-free~(classifier-free) diffusion shows promise in robustly modeling the inverse mapping, it lacks explicit guidance from proxies, essential for generating high-performance samples beyond the training distribution. Therefore, we propose \textit{proxy-enhanced sampling} which utilizes the explicit guidance from a trained proxy to bolster proxy-free diffusion with enhanced sampling control. (b) Yet, the trained proxy is susceptible to out-of-distribution issues. To address this, we devise the module \textit{diffusion-based proxy refinement}, which seamlessly integrates insights from proxy-free diffusion back into the proxy for refinement. To sum up, we propose \textit{\textbf{R}obust \textbf{G}uided \textbf{D}iffusion for Offline Black-box Optimization}~(\textbf{RGD}), combining the advantages of proxy~(explicit guidance) and proxy-free diffusion~(robustness) for effective conditional generation. RGD achieves state-of-the-art results on various design-bench tasks, underscoring its efficacy. Our code is at https://anonymous.4open.science/r/RGD-27A5/README.md., Comment: 21 pages

Where and under what conditions the transfer of energy between electromagnetic fields and particles takes place in the solar wind remains an open question. We investigate the conditions that promote the growth of kinetic instabilities predicted by linear theory, to infer how turbulence and temperature-anisotropy-driven instabilities are interrelated. Using a large dataset from Solar Orbiter, we introduce the radial rate of strain, a novel measure computed from single-spacecraft data, that we interpret as a proxy for the double-adiabatic strain rate. The solar wind exhibits high absolute values of the radial rate of strain at locations with large temperature anisotropy. We measure the kurtosis and skewness of the radial rate of strain from the statistical moments to show that it is non-Gaussian for unstable intervals and increasingly intermittent at smaller scales with a power-law scaling. We conclude that the velocity field fluctuations in the solar wind contribute to the presence of temperature anisotropy sufficient to create potentially unstable conditions., Comment: Accepted for publication in the Journal of Plasma Physics

In Canada's northern regions, linear disturbances such as roads, seismic exploration lines, and pipelines pose a significant threat to the boreal woodland caribou population (Rangifer tarandus). To address the critical need for management of these disturbances, there is a strong emphasis on developing mapping approaches that accurately identify forest habitat fragmentation. The traditional approach is manually generating maps, which is time-consuming and lacks the capability for frequent updates. Instead, applying deep learning methods to multispectral satellite imagery offers a cost-effective solution for automated and regularly updated map production. Deep learning models have shown promise in extracting paved roads in urban environments when paired with high-resolution (<0.5m) imagery, but their effectiveness for general linear feature extraction in forested areas from lower resolution imagery remains underexplored. This research employs a deep convolutional neural network model based on the VGGNet16 architecture for semantic segmentation of lower resolution (10m) Sentinel-2 satellite imagery, creating precise multi-class linear disturbance maps. The model is trained using ground-truth label maps sourced from the freely available Alberta Institute of Biodiversity Monitoring Human Footprint dataset, specifically targeting the Boreal and Taiga Plains ecozones in Alberta, Canada. Despite challenges in segmenting lower resolution imagery, particularly for thin linear disturbances like seismic exploration lines that can exhibit a width of 1-3 pixels in Sentinel-2 imagery, our results demonstrate the effectiveness of the VGGNet model for accurate linear disturbance retrieval. By leveraging the freely available Sentinel-2 imagery, this work advances cost-effective automated mapping techniques for identifying and monitoring linear disturbance fragmentation.

The understanding of the formation and evolution of the solar system still has many unanswered questions. Formation of solids in the solar system, mineral and organic mixing, and planetary body creation are all topics of interest to the community. Studying these phenomena is often performed through observations, remote sensing, and in-situ analysis, but there are limitations to the methods. Limitations such as IR diffraction limits, spatial resolution issues, and spectral resolution issues can prevent detection of organics, detection and identification of cellular structures, and the disentangling of granular mixtures. Optical-PhotoThermal InfraRed (O-PTIR) spectroscopy is a relatively new method of spectroscopy currently used in fields other than planetary sciences. O-PTIR is a non-destructive, highly repeatable, and fast form of measurement capable of reducing these limitations. Using a dual laser system with an IR source tuned to the mid-IR wavelength we performed laboratory O-PTIR measurements to compare O-PTIR data to existing IR absorption data and laboratory FTIR measurements for planetary materials. We do this for the purpose of introducing O-PTIR to the planetary science community. The technique featured here would serve to better measurements of planetary bodies during in-situ analysis. We find that, unlike other fields where O-PTIR produces almost one-to-one measurements with IR absorption measurements of the same material, granular materials relevant to planetary science do not. However, we do find that the materials compared were significantly close and O-PTIR was still capable of identifying materials relevant to planetary science., Comment: This paper is an introductory of O-PTIR spectroscopy into planetary science. It is also going to be part 1 of a series. This is a pre-print

Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy electron diffraction (RHEED) data with machine learning to establish quantitatively predictive relationships in small sets (\~10) of expert-labeled data, saving significant time on subsequently grown samples. These predictive relationships are evaluated in a representative material system (\ce{W_{1-x}V_xSe2} on c-plane sapphire (0001)) with two aims: 1) predicting grain alignment of the deposited film using pre-growth substrate data, and 2) estimating vanadium dopant concentration using in-situ RHEED as a proxy for ex-situ methods (e.g. x-ray photoelectron spectroscopy). Both tasks are accomplished using the same materials-agnostic features, avoiding specific system retraining and leading to a potential 80\% time saving over a 100-sample synthesis campaign. These predictions provide guidance to avoid doomed trials, reduce follow-on characterization, and improve control resolution for materials synthesis., Comment: 15 pages, 3 figures

We present the results of the Large Binocular Telescope Satellites Of Nearby Galaxies Survey (LBT-SONG) ``Far Sample,'' including survey completeness estimates. We find 10 satellite candidates in the inner virial regions of 13 star-forming galaxies outside the Local Group. The hosts are at distances between $\sim 5-11$ Mpc and have stellar masses in the little explored range of $\sim 5 \times 10^8 - 5\times 10^{10}~\text{M}_{\odot}$. Among the 10 satellite candidates, 3 are new discoveries in this survey. In this paper, we characterize the properties of 8 low-mass satellite candidates, including the 3 new discoveries but excluding 2 well-studied massive satellites. Of the 8 low-mass dwarfs, optical colors from the LBT imaging and measurements in the ultraviolet with GALEX suggest that 2 show signs of active star formation, and 6 are likely quenched (although some may still have H\textsc{i} gas reservoirs). Notably, we report the discovery of an ultrafaint dwarf candidate, NGC 672 dwD, with $\text{M}_{\text{V}} = -6.6$ and an estimated stellar mass of $5.6 \times 10^4 ~\text{M}_{\odot}$ if its association with the host is confirmed. It is spatially coincident with a weak detection of H\textsc{i}, with $\text{M}_{\text{HI}}/\text{M}_{\text{*}} \sim 1$. If confirmed, it would be the least luminous known ultrafaint satellite to be so gas-rich. The prevalence of quenched satellites in our sample suggests there are environmental effects at work in lower mass hosts that are similar to those at play in Milky Way-size hosts, although the preponderance of H\textsc{i} detections is at odds with the paucity of H\textsc{i} detections in Milky Way satellites. By robustly measuring our survey completeness function, we are able to compare our observational results to predictions from theory, finding good agreement with the Cold Dark Matter galaxy evolution paradigm., Comment: 18 pages, 9 figures

The ability to think analytically is vital in today's world. In an era defined by rapid technological advancements, economic globalization and societal complexities, analytical thinking skills are paramount to career success. Analytical thinking has been conceptualized and defined in a variety of ways, reflecting its multifaceted nature and the diverse contexts in which it operates. At its core, analytical thinking represents the ability to break down problems, systems or ideas into component parts, identify patterns or relationships among data, draw conclusions, and articulate how the parts relate to the whole. This paper examines the contradictory definitions of analytical thinking and attempts to reconcile them. This literature review (a) provides a working definition of analytical thinking, (b) describes how analytical thinking develops for K-12 students, (c) examines different conceptions of how analytical thinking is taught, (d) discusses specific instructional practices that support the development of analytical thinking strategies, and (e) analyzes how analytical thinking has been assessed. The review concludes with implications for the design and use of assessments of analytical thinking in K-12 schools.

Recent years have produced a surge in interest in improving state assessment programs. Many states are designing new assessments. Much of this innovation is aimed at addressing longstanding areas of unhappiness with typical domain-sampled, end-of-year state assessments: States want to streamline assessment activities, enhance the instructional utility of assessment results, and improve assessment experiences for students. Designing and redesigning state assessment to meet these goals often involves working quickly to meet legislated timelines or legislative turnover. And working quickly often means that good practices--particularly those related to iterative design and program evaluation--can be overlooked. Without a systematic and robust process of designing and evaluating these programs, educators and policymakers risk losing critical insights into what works well, what's going wrong, and why. This paper presents a framework that state education leaders can use to design and evaluate an innovative assessment program.

The diversity of California's teaching force continues to lag behind its student population. While students of Color make up 78% of the state's K-12 population, Teachers of Color and Indigenous Teachers (TOCIT) comprise just 34% of the teaching workforce (California Department of Education, 2018), a statistic that has dominated the teacher shortage narrative. Although there is promise in the fact that teacher education program (TEP) enrollment is more diverse than the state's current educator workforce, it's still 27% less diverse than the state's K-12 students. As demand for greater representation increases, so have the initiatives to recruit and retain more racially diverse teachers. In the past few years, California has spent billions in an effort to diversify its teacher workforce, including Assembly Bill 520, which allocated $15 million to be distributed to school districts to develop and implement programs that diversify teaching staffs, and Assembly Bill 130, which appropriated $350 million over the next five years to create or expand Teacher Residency Programs, a pathway that has been shown to recruit and retain higher numbers of TOCIT (California Legislative Information, 2023). This study sets out to (1) explore how current policies, structures, practices, attitudes, and ideologies across the pipeline and profession impede the recruitment and retention of TOCIT, and (2) better understand the racialized experiences and perspectives of pre-service and in-service Teachers of Color and Indigenous Teachers. The following paper is based on a mix of qualitative and quantitative data collected from system leaders in teacher preparation, pre-service teachers, in-service teachers, and former teachers over the course of approximately one year: spring 2021 to summer 2022.

Labor organization efforts grew following the pandemic in the United States at tech companies, automakers, and even higher education institutions. This brief examines unionization trends at private colleges and universities from 2007 to 2023, revealing staff as the main force behind unionization attempts, followed by contingent faculty. Major unions like the SEIU and the AFL-CIO play significant roles in representing college and university employees. This study underscores the importance of understanding historic unionization efforts, shedding light on often overlooked staff categories like maintenance and security.

Separating disinformation from fact on the web has long challenged both the search and the reasoning powers of humans. We show that the reasoning power of large language models (LLMs) and the retrieval power of modern search engines can be combined to automate this process and explainably verify claims. We integrate LLMs and search under a multi-hop evidence pursuit strategy. This strategy generates an initial question based on an input claim using a sequence to sequence model, searches and formulates an answer to the question, and iteratively generates follow-up questions to pursue the evidence that is missing using an LLM. We demonstrate our system on the FEVER 2024 (AVeriTeC) shared task. Compared to a strategy of generating all the questions at once, our method obtains .045 higher label accuracy and .155 higher AVeriTeC score (evaluating the adequacy of the evidence). Through ablations, we show the importance of various design choices, such as the question generation method, medium-sized context, reasoning with one document at a time, adding metadata, paraphrasing, reducing the problem to two classes, and reconsidering the final verdict. Our submitted system achieves .510 AVeriTeC score on the dev set and .477 AVeriTeC score on the test set., Comment: To appear in the Seventh FEVER Workshop at EMNLP 2024

Language model performance depends on identifying the optimal mixture of data groups to train on (e.g., law, code, math). Prior work has proposed a diverse set of methods to efficiently learn mixture proportions, ranging from fitting regression models over training runs to dynamically updating proportions throughout training. Surprisingly, we find that no existing method consistently outperforms a simple stratified sampling baseline in terms of average test perplexity per group. In this paper, we study the cause of this inconsistency by unifying existing methods into a standard optimization framework. We show that all methods set proportions to minimize total loss, subject to a method-specific mixing law -- an assumption on how loss is a function of mixture proportions. We find that existing parameterizations of mixing laws can express the true loss-proportion relationship empirically, but the methods themselves often set the mixing law parameters inaccurately, resulting in poor and inconsistent performance. Finally, we leverage the insights from our framework to derive a new online method named Aioli, which directly estimates the mixing law parameters throughout training and uses them to dynamically adjust proportions. Empirically, Aioli outperforms stratified sampling on 6 out of 6 datasets by an average of 0.28 test perplexity points, whereas existing methods fail to consistently beat stratified sampling, doing up to 6.9 points worse. Moreover, in a practical setting where proportions are learned on shorter runs due to computational constraints, Aioli can dynamically adjust these proportions over the full training run, consistently improving performance over existing methods by up to 12.01 test perplexity points.

Dual-comb ranging has emerged as an effective technology for long-distance metrology, providing absolute distance measurements with high speed, precision, and accuracy. Here, we demonstrate a dual-comb ranging method that utilizes a free-space transceiver unit, enabling dead-zone-free measurements and simultaneous ranging with interchanged comb roles to allow for long-distance measurements even when the target is moving. It includes a GPU-accelerated algorithm for real-time signal processing and a free-running single-cavity solid-state dual-comb laser with a carrier wavelength $\lambda_c \approx$ 1055 nm, a pulse repetition rate of 1 GHz and a repetition rate difference of 5.06 kHz. This combination offers a fast update rate and sufficient signal strength to reach a single-shot time-of-flight precision of around 0.1 $\mu$m (i.e. $< \lambda_c/4$) on a cooperative target placed at a distance of more than 40 m. The free-running laser is sufficiently stable to use the phase information for interferometric distance measurements, which improves the single-shot precision to $<$20 nm. To assess the ranging accuracy, we track the motion of the cooperative target when moved over 40 m and compare it to a reference interferometer. The residuals between the two measurements are below 3 $\mu$m. These results highlight the potential of this approach for accurate and dead-zone-free long-distance ranging, supporting real-time tracking with nm-level precision., Comment: Supplementary Information included

Conrey and Ghosh studied the second moment of the Riemann zeta function, evaluated at its local extrema along the critical line, finding the leading order behaviour to be $\frac{e^2 - 5}{2 \pi} T (\log T)^2$. This problem is closely related to a mixed moment of the Riemann zeta function and its derivative. We present a new approach which will uncover the lower order terms for the second moment as a descending chain of powers of logarithms in the asymptotic expansion., Comment: 22 pages, 9 figures, 2 tables

Tailoring the nanoscale distribution of chemical species at grain boundaries is a powerful method to dramatically influence the properties of polycrystalline materials. However, classical approaches to the problem have tacitly assumed that only competition is possible between solute species. In this paper, we show that solute elements can cooperate in the way they segregate to grain boundaries: in properly targeted alloys, the different chemical species cooperate to each fill complementary grain boundary sites disfavored by the other. By developing a theoretical "spectral" approach to this problem based on quantum-accurate grain boundary site distributions, we show how grain boundaries can be cooperatively alloyed, whether by depletion or enrichment. We provide machine-learned co-segregation information for over 700 ternary aluminum-based alloys, and experimentally validate the concept in one ternary alloy where co-segregation is not expected by prior models, but is expected based on the cooperative model.

This article presents the latest results of our ALMA program to study circumstellar disk characteristics as a function of orbital and stellar properties in a sample of young binary star systems known to host at least one disk. Optical and infrared observations of the eccentric, ~48-year period binary DF Tau indicated the presence of only one disk around the brighter component. However, our 1.3 mm ALMA thermal continuum maps show two nearly-equal brightness components in this system. We present these observations within the context of updated stellar and orbital properties which indicate that the inner disk of the secondary is absent. Because the two stars likely formed together, with the same composition, in the same environment, and at the same time, we expect their disks to be co-eval. However the absence of an inner disk around the secondary suggests uneven dissipation. We consider several processes which have the potential to accelerate inner disk evolution. Rapid inner disk dissipation has important implications for planet formation, particularly in the terrestrial-planet-forming region., Comment: 18 pages, 8 figures, accepted to AJ

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

Background: The circadian rhythm aligns physiology and behaviour with the 24-hour light-dark cycle, and its disruption is linked to neurological disorders such as epilepsy. However, how to best quantify circadian disruption remains unclear, as it can manifest across various properties and timescales. A promising but under-explored approach is to assess the intra-individual variability in circadian rhythms over timescales of weeks to years. This is yet to be studied in epilepsy. Methods: We retrospectively used wearable smartwatch data (Fitbit) from 143 people with epilepsy (PWE) and 31 controls. For each participant, we extracted the circadian oscillation underlying their heart rate time series and analysed the intra-individual variability of three circadian properties: period, acrophase, and amplitude. Findings: We found increased intra-individual variability in period (77 min vs. 62 min, z=3.32, p<0.001) and acrophase (68 min vs. 54 min, z=2.97, p=0.003) for PWE compared to controls, but not in amplitude (1.98 bpm vs. 2.05 bpm, z=-0.66, p=0.51). For PWE, we did not find any correlations between seizure frequency and intra-individual variability in circadian properties, or any difference between weeks with and without seizures. Interpretation: This finding indicates that the circadian rhythm of heart rate is more variable for people with epilepsy and that this can be detected using a wearable device. However, we were unable to find any associations with seizure frequency or occurrence, suggesting intra-individual variability could be another manifestation of epilepsy aetiology. Future work should investigate the combined role of anti-seizure medications, demographics, co-morbidities, and health behaviours in driving the increased intra-individual variability of circadian properties in epilepsy.