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This brief explores the characteristics of the high schools that newcomer students choose to attend in the Houston Independent School District (HISD). Using data from the 2007-2008 through 2018-2019 school years, the study compares the high school enrollment patterns between newcomer students who attended Las Americas, a school dedicated to serving newcomer middle school students, and newcomer students from other HISD middle schools. Specifically, this brief examined school characteristics related to potential peer networks, English learner (EL) support and resources, disciplinary climate, and academic context. The analyses show that compared to non-Las Americas students, Las Americas students tended to enroll in high schools that provided more access to potential social networks with similar peers with respect to racial/ethnic diversity as well as foreign-born status. While Las Americas students were more likely to enroll in high schools where they might have greater access to EL support and resources, there were no differences in the disciplinary climate of the high schools attended between former Las Americas and non-Las Americas enrollees. Finally, Las Americas students were more likely than non-Las Americas students to choose high schools with a context potentially more conducive to academic achievement, as measured by end-of-course exam scores.

This brief evaluates the causal effect of attending Las Americas Middle School on newcomer students' early high school outcomes. Using administrative data from the Houston Independent School District (HISD) spanning the 2007-2008 through 2018-2019 school years, the study examined the academic performance, course-taking patterns, and school engagement of newcomer students who did and did not attend Las Americas. Attending Las Americas increased newcomer students' English end-of-course (EOC) exam scores and decreased students' likelihood of receiving disciplinary actions. Newcomer students who attended Las Americas fared similarly to their newcomer peers at other middle schools on all other outcomes. The brief concludes with a discussion of the limitations of the analysis, as well as potential implications for policy and practice.

This brief examines the outcomes of high school-aged newcomer students--recently-arrived immigrant English learners--in the Houston Independent School District (HISD). Data from the 2007-2008 through 2018-2019 school years show that newcomer students who attended Liberty High School upon arrival in the U.S. had similar levels of academic performance outcomes to their newcomer peers who attended other high schools in HISD. However, other outcomes differed: Liberty students were less likely to take advanced and career and technical education courses, had lower attendance rates, and had lower graduation rates than newcomer students at other high schools. Notably, many of the differences between Liberty and non-Liberty students shrank in more recent years after Liberty established a day program in addition to its night school program.

Newcomer programs support recent immigrant, English learner students beyond what is typically offered in the traditional English learner classroom by providing students with innovative teaching methods and nonacademic support. In Houston ISD, Liberty High School is a separate-site program that serves high school-aged and older newcomer and immigrant students. To accommodate older students' job schedules, Liberty offers both day and night programs in which students can earn their high school diploma. Given the growing number of adolescent newcomer students in Houston ISD, it is important to understand how recent immigrant, English learner students choose newcomer programs and schools. Using 12 years of administrative data, this research brief describes which student characteristics predict enrollment at Liberty High School. Results show that economically disadvantaged students, students from Central America and Mexico, students who lived closer to the school, and students who lived in neighborhoods with higher concentrations of immigrants were more likely to enroll.

Newcomer programs aim to serve newly arrived immigrant students by providing specialized instruction and nonacademic support beyond what is offered in traditional English learner classrooms. In Houston ISD, Las Americas is a standalone program that serves newcomer students in grades 4-8. Given the growth of newly arrived immigrant students in the district, it is important to understand what characteristics predict whether students and families choose to enroll in a newcomer program. An analysis of 12 years of administrative data showed that economically disadvantaged students, students from sub-Saharan Africa and Central America, students with lower levels of oral English proficiency, and students who lived close to the school were most likely to enroll at Las Americas. Additionally, results suggest that the school's outreach and recruitment strategies were associated with enrollment. Finally, the implementation of the school's grade level expansion and school busing policy both had positive impacts on enrollment.

Multimodal foundation models (MFMs) such as OFASys show the potential to unlock analysis of complex data such as images, videos, and audio data via text prompts alone. However, their performance may suffer in the face of text input that differs even slightly from their training distribution, which is surprising considering the use of modality-specific data to "ground" the text input. This study demonstrates that prompt instability is a major concern for MFMs, leading to a consistent drop in performance across all modalities, but that instability can be mitigated with additional training with augmented data. We evaluate several methods for grounded prompt perturbation, where we generate perturbations and filter based on similarity to text and/or modality data. After re-training the models on the augmented data, we find improved accuracy and more stable performance on the perturbed test data regardless of perturbation condition, suggesting that the data augmentation strategy helps the models handle domain shifts more effectively. In error analysis, we find consistent patterns of performance improvement across domains, suggesting that retraining on prompt perturbations tends to help general reasoning capabilities in MFMs., Comment: in submission

Gene therapy is poised to transition from niche to mainstream medicine, with recombinant adeno-associated virus (rAAV) as the vector of choice. However, this requires robust, scalable, industrialized production to meet demand and provide affordable patient access, which has thus far failed to materialize. Closing the chasm between demand and supply requires innovation in biomanufacturing to achieve the essential step change in rAAV product yield and quality. Omics provides a rich source of mechanistic knowledge that can be applied to HEK293, the prevailing cell line for rAAV production. In this review, the findings from a growing number of disparate studies that apply genomics, epigenomics, transcriptomics, proteomics, and metabolomics to HEK293 bioproduction are explored. Learnings from CHO-Omics, application of omics approaches to improve CHO bioproduction, provide context for the potential of "HEK-Omics" as a multiomics-informed approach providing actionable mechanistic insights for improved transient and stable production of rAAV and other recombinant products in HEK293., Comment: 41 pages, 1 figure, 1 table

In recent years, there has been considerable progress in research on human activity recognition using data from wearable sensors. This technology also has potential in the context of animal welfare in livestock science. In this paper, we report on research on animal activity recognition in support of welfare monitoring. The data comes from collar-mounted accelerometer sensors worn by Holstein and Jersey calves, the objective being to detect changes in behaviour indicating sickness or stress. A key requirement in detecting changes in behaviour is to be able to classify activities into classes, such as drinking, running or walking. In Machine Learning terms, this is a time-series classification task, and in recent years, the Rocket family of methods have emerged as the state-of-the-art in this area. We have over 27 hours of labelled time-series data from 30 calves for our analysis. Using this data as a baseline, we present Rocket's performance on a 6-class classification task. Then, we compare this against the performance of 11 Deep Learning (DL) methods that have been proposed as promising methods for time-series classification. Given the success of DL in related areas, it is reasonable to expect that these methods will perform well here as well. Surprisingly, despite taking care to ensure that the DL methods are configured correctly, none of them match Rocket's performance. A possible explanation for the impressive success of Rocket is that it has the data encoding benefits of DL models in a much simpler classification framework.

Strong $E\times B$ plasma flow shear is beneficial for reducing turbulent transport. However, traditional methods of driving flow shear do not scale well to large devices such as future fusion power plants. In this paper, we use a large number of nonlinear gyrokinetic simulations to study a novel approach to increase flow shear: decreasing the momentum diffusivity to make the plasma ``easier to push''. We first use an idealized circular geometry and find that one can obtain low momentum diffusivity at tight aspect ratio, low safety factor, high magnetic shear and low temperature gradient. This is the so-called Low Momentum Diffusivity (LMD) regime. To drive intrinsic momentum flux, we then tilt the flux surface, making it up-down asymmetric. In the LMD regime, this intrinsic momentum flux drives strong flow shear that can significantly reduce the heat flux and increase the critical temperature gradient. We also consider the actual experimental geometry of the MAST tokamak to illustrate that this strategy can be practical and create experimentally significant flow shear. Lastly, a preliminary prediction for the SMART tokamak is made., Comment: 43 pages, 23 figures

Relying on human experts to evaluate CEFR speaking assessments in an e-learning environment creates scalability challenges, as it limits how quickly and widely assessments can be conducted. We aim to automate the evaluation of CEFR B2 English speaking assessments in e-learning environments from conversation transcripts. First, we evaluate the capability of leading open source and commercial Large Language Models (LLMs) to score a candidate's performance across various criteria in the CEFR B2 speaking exam in both global and India-specific contexts. Next, we create a new expert-validated, CEFR-aligned synthetic conversational dataset with transcripts that are rated at different assessment scores. In addition, new instruction-tuned datasets are developed from the English Vocabulary Profile (up to CEFR B2 level) and the CEFR-SP WikiAuto datasets. Finally, using these new datasets, we perform parameter efficient instruction tuning of Mistral Instruct 7B v0.2 to develop a family of models called EvalYaks. Four models in this family are for assessing the four sections of the CEFR B2 speaking exam, one for identifying the CEFR level of vocabulary and generating level-specific vocabulary, and another for detecting the CEFR level of text and generating level-specific text. EvalYaks achieved an average acceptable accuracy of 96%, a degree of variation of 0.35 levels, and performed 3 times better than the next best model. This demonstrates that a 7B parameter LLM instruction tuned with high-quality CEFR-aligned assessment data can effectively evaluate and score CEFR B2 English speaking assessments, offering a promising solution for scalable, automated language proficiency evaluation.

Getting new insights on pre-weaned calf behavioral adaptation to routine challenges (transport, group relocation, etc.) and diseases (respiratory diseases, diarrhea, etc.) is a promising way to improve calf welfare in dairy farms. A classic approach to automatically monitoring behavior is to equip animals with accelerometers attached to neck collars and to develop machine learning models from accelerometer time-series. However, to be used for model development, data must be equipped with labels. Obtaining these labels requires annotating behaviors from direct observation or videos, a time-consuming and labor-intensive process. To address this challenge, we propose the ActBeCalf (Accelerometer Time-Series for Calf Behaviour classification) dataset: 30 pre-weaned dairy calves (Holstein Friesian and Jersey) were equipped with a 3D-accelerometer sensor attached to a neck-collar from one week of birth for 13 weeks. The calves were simultaneously filmed with a camera in each pen. At the end of the trial, behaviors were manually annotated from the videos using the Behavioral Observation Research Interactive Software (BORIS) by 3 observers using an ethogram with 23 behaviors. ActBeCalf contains 27.4 hours of accelerometer data aligned adequately with calf behaviors. The dataset includes the main behaviors, like lying, standing, walking, and running, and less prominent behaviors, such as sniffing, social interaction, and grooming. Finally, ActBeCalf was used for behavior classification with machine learning models: (i)two classes of behaviors, [active and inactive; model 1] and (ii)four classes of behaviors [running, lying, drinking milk, and 'other' class; model 2] to demonstrate its reliability. We got a balanced accuracy of 92% [model1] and 84% [model2]. ActBeCalf is a comprehensive and ready-to-use dataset for classifying pre-weaned calf behaviour from the acceleration time series., Comment: 20 pages, 15 figures

Wolf-Rayet stars (WRs) are evolved massive stars in the brief stage before they undergo core collapse. Not only are they rare, but they also can be particularly difficult to find due to the high extinction in the Galactic plane. This paper discusses the discovery of three new Galactic WRs previously classified as H$\alpha$ emission stars, but thanks to Gaia spectra, we were able to identify the broad, strong emission lines that characterize WRs. Using the Lowell Discovery Telescope and the DeVeny spectrograph, we obtained spectra for each star. Two are WC9s, and the third is a WN6 + O6.5 V binary. The latter is a known eclipsing system with a 4.4 day period from ASAS-SN data. We calculate absolute visual magnitudes for all three stars to be between -7 and -6, which is consistent with our expectations of these subtypes. These discoveries highlight the incompleteness of the WR census in our local volume of the Milky Way and suggest the potential for future Galactic WR discoveries from Gaia low-dispersion spectra. Furthermore, radial velocity studies of the newly found binary will provide direct mass estimates and orbital parameters, adding to our knowledge of the role that binarity plays in massive star evolution., Comment: Accepted for publication in the Astronomical Journal

In the tensor network approach to statistical physics, properties of the critical point of a 2D lattice model are encoded by a four-legged tensor which is a fixed point of an RG map. The traditional way to find the fixed point tensor consists in iterating the RG map after having tuned the temperature to criticality. Here we develop a different and more direct technique, which solves the fixed point equation via the Newton method. This is challenging due to the existence of marginal deformations -- linear transformations of the coordinate frame, which parametrize a two-dimensional family of fixed points. We address this challenge by including a 90 degree rotation into the RG map. This flips the sign of the problematic marginal eigenvalues, rendering the fixed point isolated and accessible via the Newton method. We demonstrate the power of this technique via explicit computations for the 2D Ising model. Using the Gilt-TNR algorithm at bond dimension $\chi=30$, we find the fixed point tensor with $10^{-9}$ accuracy, much higher than what was previously achieved., Comment: 32+20 pages, 15 figures, 4 tables

We introduce a new framework for verifying systems with a parametric number of concurrently running processes. The systems we consider are well-structured with respect to a specific well-quasi order. This allows us to decide a wide range of verification problems, including control-state reachability, coverability, and target, in a fixed finite abstraction of the infinite state-space, called a 01-counter system. We show that several systems from the parameterized verification literature fall into this class, including reconfigurable broadcast networks (or systems with lossy broadcast), disjunctive systems, synchronizations and systems with a fixed number of shared finite-domain variables. Our framework provides a simple and unified explanation for the properties of these systems, which have so far been investigated separately. Additionally, it extends and improves on a range of the existing results, and gives rise to other systems with similar properties.

UW Coronae Borealis (UW CrB) is a low mass X-ray binary that shows both Type 1 X-ray and optical bursts, which typically last for 20 s. The system has a binary period of close to 2 hours and is thought to have a relatively high inclination due to the presence of an eclipse in the optical light curve. There is also evidence that an asymmetric disc is present in the system, which precesses every 5.5 days based on changes in the depth of the eclipse. In this paper, we present optical photometry and spectroscopy of UW CrB taken over 2 years. We update the orbital ephemeris using observed optical eclipses and refine the orbital period to 110.97680(1) min. A total of 17 new optical bursts are presented, with 10 of these bursts being resolved temporally. The average $e$-folding time of $19\pm3$s for the bursts is consistent with the previously found value. Optical bursts are observed during a previously identified gap in orbital phase centred on $\phi=0.967$, meaning the reprocessing site is not eclipsed as previously thought. Finally, we find that the apparent P-Cygni profiles present in some of the atomic lines in the optical spectra are due to transient absorption., Comment: 14 pages, 17 figures, submitted to OJAp

Photovoltaics (PV) are widely used to harvest solar energy, an important form of renewable energy. Photovoltaic arrays consist of multiple solar panels constructed from solar cells. Solar cells in the field are vulnerable to various defects, and electroluminescence (EL) imaging provides effective and non-destructive diagnostics to detect those defects. We use multiple traditional machine learning and modern deep learning models to classify EL solar cell images into different functional/defective categories. Because of the asymmetry in the number of functional vs. defective cells, an imbalanced label problem arises in the EL image data. The current literature lacks insights on which methods and metrics to use for model training and prediction. In this paper, we comprehensively compare different machine learning and deep learning methods under different performance metrics on the classification of solar cell EL images from monocrystalline and polycrystalline modules. We provide a comprehensive discussion on different metrics. Our results provide insights and guidelines for practitioners in selecting prediction methods and performance metrics., Comment: 30 pages, 14 figures

The field of aging and longevity research is overwhelmed by vast amounts of data, calling for the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the evaluation of geroprotective interventions. Such evaluations should be correct, useful, comprehensive, explainable, and they should consider causality, interdisciplinarity, adherence to standards, longitudinal data and known aging biology. In particular, comprehensive analyses should go beyond comparing data based on canonical biomedical databases, suggesting the use of AI to interpret changes in biomarkers and outcomes. Our requirements motivate the use of LLMs with Knowledge Graphs and dedicated workflows employing, e.g., Retrieval-Augmented Generation. While naive trust in the responses of AI tools can cause harm, adding our requirements to LLM queries can improve response quality, calling for benchmarking efforts and justifying the informed use of LLMs for advice on longevity interventions., Comment: 11 pages, 1 Figure, 1 Table

We present an efficient graphical approach to construct projectors for the tensor reduction of multi-loop Feynman integrals with both Lorentz and spinor indices in $D$ dimensions. An ansatz for the projectors is constructed making use of its symmetry properties via an orbit partition formula. The graphical approach allows to identify and enumerate the orbits in each case. For the case without spinor indices we find a 1 to 1 correspondence between orbits and integer partitions describing the cycle structure of certain bi-chord graphs. This leads to compact combinatorial formulae for the projector ansatz. With spinor indices the graph-structure becomes more involved, but the method is equally applicable. Our spinor reduction formulae are based on the antisymmetric basis of $\gamma$ matrices, and make use of their orthogonality property. We also provide a new compact formula to pass into the antisymmetric basis. We compute projectors for vacuum tensor Feynman integrals with up to 32 Lorentz indices and up to 4 spinor indices. We discuss how to employ the projectors in problems with external momenta., Comment: 58 pages, 32 tables, 3 figures

Water ice plays a crucial role throughout the different stages of planetary evolution and is abundant in the Universe. However, its presence and nature in debris discs of exoplanetary systems are not yet strongly established observationally. In this study, we quantify and discuss the impact of ice parameters such as volume fraction ${\mathcal{F}}_{\rm ice}$, blow-out grain size, size distribution, and its phase on the observational appearance of debris discs, considering the diverse nature of these systems around stellar spectral types ranging from A to M. Our findings reveal that the prominent ice features at approximately 2.7 and 3.3\,$\mu$m depend on both the water ice fraction ${\mathcal{F}}_{\rm ice}$ and the scattering angle, with backscattering geometries yielding the most prominent signatures. When the phase function is considered and data are not background limited, strong forward and backward scattering (near edge-on discs) are expected to yield the strongest detections in images/spectra for A or F-type stars, while scattering angle matters less for later type stars. The Fresnel peak at 3.1\,$\mu$m serves as a viable discriminant for the transitional phase (crystalline/amorphous), while simultaneously constraining the water ice temperature. For JWST imaging, we find that the F356W and F444W filter combination is most effective for constraining the grain size distribution, while the F356W and F277W filter combination provides better constraints on the ice fraction ${\mathcal{F}}_{\rm ice}$ in debris discs. However, degeneracy between the grain size distribution and ice fraction when using photometric flux ratios means that obtaining robust constraints will likely require more than two filters, or spectroscopic data., Comment: 22 pages, 20 figures

We consider a density-stratified fluid composed of two immiscible layers separated by a sharp interface. We study the regime of long internal waves interacting with modulated surface wave packets and describe their resonant interaction by a system of equations where the internal wave solves a high-order Benjamin-Ono (BO) equation coupled to a linear Schr\"{o}dinger equation for the envelope of the free surface. The perturbation methods are based on the Hamiltonian formulation for the original system of irrotational Euler's equations as described in Benjamin-Bridges [J. Fluid Mech. 333, 1997] and Craig-Guyenne-Kalisch [Comm. Pure Appl. Math. 58, 2005]. We also establish a local wellposedness result for a reduced BO-Schr\"{o}dinger system using an approach developed by Linares-Ponce-Pilod [J. Diff. Eqs. 250, 2011]., Comment: 31 pages

The integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) and Internet of Things (IoT) technology in smart farms is pivotal for efficient resource management and enhanced agricultural productivity sustainably. This paper addresses the critical need for optimizing task offloading in secure UAV-assisted smart farm networks, aiming to reduce total delay and energy consumption while maintaining robust security in data communications. We propose a multi-agent deep reinforcement learning (DRL)-based approach using a deep double Q-network (DDQN) with an action mask (AM), designed to manage task offloading dynamically and efficiently. The simulation results demonstrate the superior performance of our method in managing task offloading, highlighting significant improvements in operational efficiency by reducing delay and energy consumption. This aligns with the goal of developing sustainable and energy-efficient solutions for next-generation network infrastructures, making our approach an advanced solution for achieving both performance and sustainability in smart farming applications.

The companion to PSR J1622-0315, one of the most compact known redback millisecond pulsars, shows extremely low irradiation despite its short orbital period. We model this system to determine the binary parameters, combining optical observations from NTT in 2017 and NOT in 2022 with the binary modeling code ICARUS. We find a best-fit neutron star mass of $2.3 \pm 0.4\,\text{M}_\odot $, and a companion mass of $0.15 \pm 0.02\,\text{M}_\odot$. We detect for the first time low-level irradiation from asymmetry in the minima as well as a change in the asymmetry of the maxima of its light curves over five years. Using star spot models, we find better fits than those from symmetric direct heating models, with consistent orbital parameters. We discuss an alternative scenario where the changing asymmetry is produced by a variable intrabinary shock. In summary, we find that PSR J1622-0315 combines low irradiation with variable light curve asymmetry, and a relatively high neutron star mass., Comment: 18 pages, 11 figures

Upper-limb amputees face tremendous difficulty in operating dexterous powered prostheses. Previous work has shown that aspects of prosthetic hand, wrist, or elbow control can be improved through "intelligent" control, by combining movement-based or gaze-based intent estimation with low-level robotic autonomy. However, no such solutions exist for whole-arm control. Moreover, hardware platforms for advanced prosthetic control are expensive, and existing simulation platforms are not well-designed for integration with robotics software frameworks. We present the Prosthetic Arm Control Testbed (ProACT), a platform for evaluating intelligent control methods for prosthetic arms in an immersive (Augmented Reality) simulation setting. Using ProACT with non-amputee participants, we compare performance in a Box-and-Blocks Task using a virtual myoelectric prosthetic arm, with and without intent estimation. Our results show that methods using intent estimation improve both user satisfaction and the degree of success in the task. To the best of our knowledge, this constitutes the first study of semi-autonomous control for complex whole-arm prostheses, the first study including sequential task modeling in the context of wearable prosthetic arms, and the first testbed of its kind. Towards the goal of supporting future research in intelligent prosthetics, the system is built upon on existing open-source frameworks for robotics., Comment: 10 pages, 8 figures. Under review. Code and data will be available at https://armlabstanford.github.io/proact after publication

We contribute a general and flexible framework to estimate the size of a closed population in the presence of $K$ capture-recapture lists and heterogeneous capture probabilities. Our novel identifying strategy leverages the fact that it is sufficient for identification that a subset of the $K$ lists are not arbitrarily dependent \textit{within the subset of the population unobserved by the remaining lists}, conditional on covariates. This identification approach is interpretable and actionable, interpolating between the two predominant approaches in the literature as special cases: (conditional) independence across lists and log-linear models with no highest-order interaction. We derive nonparametric doubly-robust estimators for the resulting identification expression that are nearly optimal and approximately normal for any finite sample size, even when the heterogeneous capture probabilities are estimated nonparametrically using machine learning methods. Additionally, we devise a sensitivity analysis to show how deviations from the identification assumptions affect the resulting population size estimates, allowing for the integration of domain-specific knowledge into the identification and estimation processes more transparently. We empirically demonstrate the advantages of our method using both synthetic data and real data from the Peruvian internal armed conflict to estimate the number of casualties. The proposed methodology addresses recent critiques of capture-recapture models by allowing for a weaker and more interpretable identifying assumption and accommodating complex heterogeneous capture probabilities depending on high-dimensional or continuous covariates.

Small Language Models (SLMs) are generally considered to be more compact versions of large language models (LLMs), typically having fewer than 7 billion parameters. This study investigates the ability of small language models to learn, retain, and subsequently eliminate noise that is typically not found on the internet, where most pretraining datasets are sourced. For this, four pre-trained SLMs were utilized: Olmo 1B, Qwen1.5 1.8B, Gemma 2B, and Phi2 2.7B. The models were instruction-tuned without noise and tested for task execution with in-context learning. Afterward, noise patterns were introduced to evaluate the models' learning and unlearning capabilities. We evaluated the models' performance at various training levels. Phi consistently excelled with word-level noise but performed the worst with character-level noise. Despite being the smallest with approximately 1 billion parameters, Olmo performed consistently well on tasks.

This paper investigates the impact of weave architectures on the mechanics of crack propagation in fiber-reinforced woven polymer composites under quasi-static loading. Woven composites consist of fabrics containing fibers interwoven at 0 degrees (warp) and 90 degrees (weft) bound by a polymer matrix. The mechanical properties can be tuned by weaving fiber bundles with single or multiple materials in various patterns or architectures. Although the effects of uniform weave architectures, like plain, twill, satin, etc. on in-plane modulus and fracture toughness have been studied, the influence of patterned weaves consisting of a combination of sub-patterns, that is, architected weaves, on these behaviors is not understood. We focus on identifying the mechanisms affecting crack path tortuosity and propagation rate in architected weave composites containing various sub-patterns. Through compact tension tests, we determine how architected weave patterns compared to uniform weaves influence mode-I fracture toughness of woven composites due to interactions of different failure modes. Results show that fracture toughness increases at transition regions between sub-patterns in architected weave composites, with more tortuous crack propagation and higher resistance to crack growth than uniform weave composites. We also introduce three geometrical parameters transition, area, and skewness factors to characterize sub-patterns and their effects on in-plane fracture toughness. This knowledge can be exploited to design and fabricate safer lightweight structures for marine and aerospace sectors with enhanced damage tolerance under extreme loads.

RAND researchers conducted a study that aimed to fill the information gap on how to successfully implement a statewide financial education mandate by studying the implementation of Nevada's 2017 financial literacy education reform. The study focused on the reform enacted by the Nevada Legislature in 2017, Senate Bill 249, which required that financial literacy education be implemented in third through 12th grade starting in the 2017-2018 school year. RAND researchers examined the following research questions to better understand Nevada's financial literacy reform and potential implications for strengthening financial literacy reforms more broadly: (1) After the 2017 Nevada mandate, how was financial literacy education implemented in schools?; (2) What were the main barriers and facilitators to financial literacy implementation across Nevada's schools?; and (3) How do educators, leaders, and policymakers perceive financial literacy education in Nevada?

More than half of U.S. states have enacted policies to expand financial literacy education in schools with kindergarten through grade 12 (K-12) with the goal of improving students' financial literacy skills. Although financial literacy education can be key to building long-term financial capabilities and well-being, more information is needed on how to effectively design state-level financial literacy education requirements. In this report, we examine the implementation of Nevada's financial literacy requirements in grades 3 through 12 using data from a teacher survey, focus groups, interviews, and financial literacy programs. We describe several key takeaways from the implementation process and recommendations for strengthening financial literacy education in K-12 schools. This report should be of interest to state policymakers, education leaders, teachers, and those working to advance youth financial literacy.

Generative AI (GenAI) use in research writing is growing fast. However, it is unclear how peer reviewers recognize or misjudge AI-augmented manuscripts. To investigate the impact of AI-augmented writing on peer reviews, we conducted a snippet-based online survey with 17 peer reviewers from top-tier HCI conferences. Our findings indicate that while AI-augmented writing improves readability, language diversity, and informativeness, it often lacks research details and reflective insights from authors. Reviewers consistently struggled to distinguish between human and AI-augmented writing but their judgements remained consistent. They noted the loss of a "human touch" and subjective expressions in AI-augmented writing. Based on our findings, we advocate for reviewer guidelines that promote impartial evaluations of submissions, regardless of any personal biases towards GenAI. The quality of the research itself should remain a priority in reviews, regardless of any preconceived notions about the tools used to create it. We emphasize that researchers must maintain their authorship and control over the writing process, even when using GenAI's assistance.

Automatic monitoring of calf behaviour is a promising way of assessing animal welfare from their first week on farms. This study aims to (i) develop machine learning models from accelerometer data to classify the main behaviours of pre-weaned calves and (ii) set up a digital tool for monitoring the behaviour of pre-weaned calves from the models' prediction. Thirty pre-weaned calves were equipped with a 3-D accelerometer attached to a neck-collar for two months and filmed simultaneously. The behaviours were annotated, resulting in 27.4 hours of observation aligned with the accelerometer data. The time-series were then split into 3 seconds windows. Two machine learning models were tuned using data from 80% of the calves: (i) a Random Forest model to classify between active and inactive behaviours using a set of 11 hand-craft features [model 1] and (ii) a RidgeClassifierCV model to classify between lying, running, drinking milk and other behaviours using ROCKET features [model 2]. The performance of the models was tested using data from the remaining 20% of the calves. Model 1 achieved a balanced accuracy of 0.92. Model 2 achieved a balanced accuracy of 0.84. Behavioural metrics such as daily activity ratio and episodes of running, lying, drinking milk, and other behaviours expressed over time were deduced from the predictions. All the development was finally embedded into a Python dashboard so that the individual calf metrics could be displayed directly from the raw accelerometer files.

This second international workshop on explainable AI for the Arts (XAIxArts) brought together a community of researchers in HCI, Interaction Design, AI, explainable AI (XAI), and digital arts to explore the role of XAI for the Arts. Workshop held at the 16th ACM Conference on Creativity and Cognition (C&C 2024), Chicago, USA., Comment: Proceedings of The second international workshop on eXplainable AI for the Arts (XAIxArts)

We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen's spatially varying material properties from noisy measurements of its mechanical response to loading. Conditional score-based diffusion models are generative models that learn to approximate the score function of a conditional distribution using samples from the joint distribution. More specifically, the score functions corresponding to multiple realizations of the measurement are approximated using a single neural network, the so-called score network, which is subsequently used to sample the posterior distribution using an appropriate Markov chain Monte Carlo scheme based on Langevin dynamics. Training the score network only requires simulating the forward model. Hence, the proposed approach can accommodate black-box forward models and complex measurement noise. Moreover, once the score network has been trained, it can be re-used to solve the inverse problem for different realizations of the measurements. We demonstrate the efficacy of the proposed approach on a suite of high-dimensional inverse problems in mechanics that involve inferring heterogeneous material properties from noisy measurements. Some examples we consider involve synthetic data, while others include data collected from actual elastography experiments. Further, our applications demonstrate that the proposed approach can handle different measurement modalities, complex patterns in the inferred quantities, non-Gaussian and non-additive noise models, and nonlinear black-box forward models. The results show that the proposed framework can solve large-scale physics-based inverse problems efficiently.

We demonstrate a sapphire machining process integrated with intermediate-scale quantum processors. The process allows through-substrate electrical connections, necessary for low-frequency mode-mitigation, as well as signal-routing, which are vital as quantum computers scale in qubit number, and thus dimension. High-coherence qubits are required to build fault-tolerant quantum computers and so material choices are an important consideration when developing a qubit technology platform. Sapphire, as a low-loss dielectric substrate, has shown to support high-coherence qubits. In addition, recent advances in material choices such as tantalum and titanium-nitride, both deposited on a sapphire substrate, have demonstrated qubit lifetimes exceeding 0.3 ms. However, the lack of any process equivalent of deep-silicon etching to create through-substrate-vias in sapphire, or to inductively shunt large dies, has limited sapphire to small-scale processors, or necessitates the use of chiplet architecture. Here, we present a sapphire machining process that is compatible with high-coherence qubits. This technique immediately provides a means to scale QPUs with integrated mode-mitigation, and provides a route toward the development of through-sapphire-vias, both of which allow the advantages of sapphire to be leveraged as well as facilitating the use of sapphire-compatible materials for large-scale QPUs., Comment: 9 pages, 6 figures

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

Time-domain astrophysics continues to grow rapidly, with the inception of new surveys drastically increasing data volumes. Democratised, distributed approaches to training sets for machine learning classifiers are crucial to make the most of this torrent of discovery -- with citizen science approaches proving effective at meeting these requirements. In this paper, we describe the creation of and the initial results from the $\textit{Kilonova Seekers}$ citizen science project, built to find transient phenomena from the GOTO telescopes in near real-time. $\textit{Kilonova Seekers}$ launched in July 2023 and received over 600,000 classifications from approximately 2,000 volunteers over the course of the LIGO-Virgo-KAGRA O4a observing run. During this time, the project has yielded 20 discoveries, generated a `gold-standard' training set of 17,682 detections for augmenting deep-learned classifiers, and measured the performance and biases of Zooniverse volunteers on real-bogus classification. This project will continue throughout the lifetime of GOTO, pushing candidates at ever-greater cadence, and directly facilitate the next-generation classification algorithms currently in development., Comment: 20 pages, 15 figures. Accepted in MNRAS