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The COVID-19 pandemic widened existing educational inequities for Black and Latino students, students who are English learners, and students with disabilities. Research shows that family engagement has a positive impact on student outcomes; therefore, forging connections between schools and families as enrollment rebounds will be especially crucial to enhancing student learning and well-being in the wake of the pandemic. Given that context, this report examines the perceptions and experiences of first and second grade parents and teachers with their school's engagement initiatives using a mixed-methods analysis of data from two nationally representative surveys, an online discussion board, and interviews with family engagement coordinators. Using existing research on meaningful and effective family engagement as a guide, the authors examined parents' and teachers' perceptions about several facets of effective engagement: communication; shared decision-making; connections, trust, and mutual respect; and cultural inclusiveness. Where applicable, the authors discuss how responses differed by race or ethnicity, household income, multilingual status, and whether the parent has a child who has been identified as having a disability.

Most Americans today need some education beyond high school to secure a well-paying job, and higher education is a key factor in social and economic mobility. But higher education is not accessible to all students. To further equalize opportunity, states must understand options for improving the way they fund higher education. America's public K-12 system has been grappling with similar funding disparities for decades -- challenges that offer important lessons for postsecondary funding. Bellwether's analysis, "Strengthening State Higher Education Funding: Lessons Learned From K-12," identifies insights and lessons from K-12 finance policy that may be instructive as states rework how they fund higher education. This report explores four topics and associated questions that are also relevant across K-12 education: (1) Equity: How can states distribute funding to students and public IHEs with the greatest needs?; (2) Adequacy: How much funding is enough to achieve the desired outcomes?; (3) Cost Sharing: How can local governments and states share the cost of public higher education?; and (4) Stability: How can public IHEs secure consistent and predictable funding?

Recent developments in computer graphics, machine learning, and sensor technologies enable numerous opportunities for extended reality (XR) setups for everyday life, from skills training to entertainment. With large corporations offering consumer-grade head-mounted displays (HMDs) in an affordable way, it is likely that XR will become pervasive, and HMDs will develop as personal devices like smartphones and tablets. However, having intelligent spaces and naturalistic interactions in XR is as important as technological advances so that users grow their engagement in virtual and augmented spaces. To this end, large language model (LLM)--powered non-player characters (NPCs) with speech-to-text (STT) and text-to-speech (TTS) models bring significant advantages over conventional or pre-scripted NPCs for facilitating more natural conversational user interfaces (CUIs) in XR. In this paper, we provide the community with an open-source, customizable, extensible, and privacy-aware Unity package, CUIfy, that facilitates speech-based NPC-user interaction with various LLMs, STT, and TTS models. Our package also supports multiple LLM-powered NPCs per environment and minimizes the latency between different computational models through streaming to achieve usable interactions between users and NPCs. We publish our source code in the following repository: https://gitlab.lrz.de/hctl/cuify, Comment: This work has been submitted to the IEEE for possible publication

A shape optimization program is developed for the ratio of Riesz capacities $\text{Cap}_q(K)/\text{Cap}_p(K)$, where $K$ ranges over compact sets in $\mathbb{R}^n$. In different regions of the $pq$-parameter plane, maximality is conjectured for the ball, the vertices of a regular simplex, or the endpoints of an interval. These cases are separated by a symmetry-breaking transition region where the shape of maximizers remains unclear. On the boundary of $pq$-parameter space one encounters existing theorems and conjectures, including: Watanabe's theorem minimizing Riesz capacity for given volume, the classical isodiametric theorem that maximizes volume for given diameter, Szeg\H{o}'s isodiametric theorem maximizing Newtonian capacity for given diameter, and the still-open isodiametric conjecture for Riesz capacity. The first quadrant of parameter space contains P\'{o}lya and Szeg\H{o}'s conjecture on maximizing Newtonian over logarithmic capacity for planar sets. The maximal shape for each of these scenarios is known or conjectured to be the ball. In the third quadrant, where both $p$ and $q$ are negative, the maximizers are quite different: when one of the parameters is $-\infty$ and the other is suitably negative, maximality is proved for the vertices of a regular simplex or endpoints of an interval. Much more is proved in dimensions $1$ and $2$, where for large regions of the third quadrant, maximizers are shown to consist of the vertices of intervals or equilateral triangles.

We utilize serendipitous observations from the Asteroid Terrestrial-impact Last Alert System (ATLAS) and the Zwicky Transient Facility (ZTF) in addition to targeted follow-up observations from the Las Cumbres Observatory (LCO) and Liverpool Telescope to analyze the first observed instance of cometary activity by the newly-discovered Jupiter-family comet C/2023 RN3 (ATLAS), whose orbital dynamics place it close to residing on a Centaur-like orbit. Across our 7-month baseline, we observe an epoch of cometary activity commencing in August 2023 with an increase in brightness of >5.4 mag. The lightcurve of 2023 RN3 indicates the presence of continuous cometary activity across our observations, suggesting the onset of a new period of sustained activity. We find no evidence of any outbursts on top of the observed brightening, nor do we find any significant color evolution across our observations. 2023 RN3 is visibly extended in LCO and Liverpool Telescope observations, indicating the presence of a spatially-extended coma. Numerical integration of 2023 RN3's orbit reveals the comet to have recently undergone a slight increase in semimajor axis due to a planetary encounter with Jupiter, however whether this orbital change could trigger 2023 RN3's cometary activity is unclear. Our estimate for the maximum dust production metric of Afrho ~400 cm is consistent with previous measurements for the Jupiter-family comet and Centaur populations., Comment: 20 pages, 9 figures

Galactic bars and their associated resonances play a significant role in shaping galaxy evolution. Resulting resonance-driven structures, like the vertically extended Boxy/Peanut X-Feature (BPX), then serve as a useful probe of the host galaxy's history. In this study, we quantify the impact of a classical bulge on the evolution of the bar and the growth of bar resonance structures. This is accomplished with a suite of isolated N-body disk galaxy simulations with bulge mass fractions ranging from 0% to 16% of the disk mass. We apply frequency analysis to the stellar orbits to analyze the variations in resonance structure evolution. Our findings indicate that a more massive initial bulge leads to the formation of a stronger and more extended bar and that each bar drives the formation of a prominent associated BPX through resonance passage. In this work, we present evidence that the formation of a BPX is driven by planar, bar-supporting orbits evolving through interaction with horizontal and vertical bar-resonances. More orbits become vertically extended when these resonances overlap, and the rate of the orbits passing through resonance is moderated by the overall fraction of vertically extended orbits. A significant bulge stabilizes the fraction of vertically extended orbits, preventing sudden resonance-induced changes. Crucially, neither sudden resonance overlap nor prolonged resonance trapping is required for BPX formation., Comment: 13 pages, 9 figures, Submitted to MNRAS, Comments welcome!

Wearable sensor systems have demonstrated a great potential for real-time, objective monitoring of physiological health to support behavioral interventions. However, obtaining accurate labels in free-living environments remains difficult due to limited human supervision and the reliance on self-labeling by patients, making data collection and supervised learning particularly challenging. To address this issue, we introduce CUDLE (Cannabis Use Detection with Label Efficiency), a novel framework that leverages self-supervised learning with real-world wearable sensor data to tackle a pressing healthcare challenge: the automatic detection of cannabis consumption in free-living environments. CUDLE identifies cannabis consumption moments using sensor-derived data through a contrastive learning framework. It first learns robust representations via a self-supervised pretext task with data augmentation. These representations are then fine-tuned in a downstream task with a shallow classifier, enabling CUDLE to outperform traditional supervised methods, especially with limited labeled data. To evaluate our approach, we conducted a clinical study with 20 cannabis users, collecting over 500 hours of wearable sensor data alongside user-reported cannabis use moments through EMA (Ecological Momentary Assessment) methods. Our extensive analysis using the collected data shows that CUDLE achieves a higher accuracy of 73.4%, compared to 71.1% for the supervised approach, with the performance gap widening as the number of labels decreases. Notably, CUDLE not only surpasses the supervised model while using 75% less labels, but also reaches peak performance with far fewer subjects., Comment: 8 pages, 5 figures, 1 table

Oral traditions, vital to cultural identity, are losing relevance among youth due to the dominance of modern media. This study addresses the revitalization of these traditions by reconnecting young people with folklore. We introduce Anansi the Spider VR, a novel virtual space that combines first-person virtual reality (VR) with generative artificial intelligence (Gen-AI)-driven narrative personalization. This space immerses users in the Anansi Spider story, empowering them to influence the narrative as they envision themselves as the `protagonists,' thereby enhancing personal reflection. In a 2 by 2 between-subjects study with 48 participants, we employed a mixed-method approach to measure user engagement and changes in interest, complemented by semi-structured interviews providing qualitative insights into personalization and immersion. Our results indicate that personalization in VR significantly boosts engagement and cultural learning interest. We recommend that future studies using VR and Gen-AI to revitalize oral storytelling prioritize respecting cultural integrity and honoring original storytellers and communities.

A regular-graph design is a block design for which a pair $\{a,b\}$ of distinct points occurs in $\lambda+1$ or $\lambda$ blocks depending on whether $\{a,b\}$ is or is not an edge of a given $\delta$-regular graph. Our paper describes a specific construction for regular-graph designs with $\lambda = 1$ and block size $\delta + 1$. We show that for $\delta \in \{2,3\}$, certain necessary conditions for the existence of such a design with $n$ points are sufficient, with two exceptions in each case and two possible exceptions when $\delta = 3$. We also construct designs of orders 105 and 117 for connected 4-regular graphs., Comment: 18 pages. The paper will be submitted to a journal. Some basic definitions have been lifted from other papers by the same authors

Since 2010, the State Higher Education Executive Officers Association's (SHEEO) Strong Foundations survey has reported on the evolution and value of postsecondary student unit record systems (PSURSs) by illuminating the condition of state postsecondary data in the U.S. In the "Strong Foundations 2023" survey, which was administered from February through March of 2023, SHEEO asked about the numbers of PSURSs existing in states, the types of data state postsecondary agencies are collecting in their PSURSs, their connections to other state agency data systems and state longitudinal data systems (SLDSs), and the value of those data to inform and improve state goals and student outcomes. Seventy-three SHEEO member and non-member agencies from all 50 states and the District of Columbia responded to the survey. As expected, PSURSs connections with K-12 and workforce agency data systems were among the most reported connections; yet there are growing connections with other state agencies, including health and human services, foster care, and corrections agencies. The most commonly reported barriers to efficient data sharing between agencies included data privacy concerns, coordination with other state authorities and administrators, lack of time for agency staff to link and analyze shared data, a lack of common identifiers and crosswalks, and a lack of fiscal resources. Among the new data elements SHEEO included for reporting this year are tribal affiliation, student parent/caregiver status, foster care status, refugee status, justice system involvement, and other data elements about historically underrepresented groups in higher education. The highlights in this report underscore the significance PSURSs have in improving mandated reporting, informing state goals and student outcomes, bolstering communication with stakeholders, creating greater transparency and trust, and garnering support and funding.

We use satellite observations of atmospheric methane from the TROPOMI instrument to estimate total annual methane emissions for 2019-2023 from four large Southeast US landfills with gas collection and control systems. The emissions are on average 6$\times$ higher than the values reported by the landfills to the US Greenhouse Gas Reporting Program (GHGRP) which are used by the US Environmental Protection Agency (EPA) for its national Greenhouse Gas Inventory (GHGI). We find increasing emissions over the 2019-2023 period whereas the GHGRP reports a decrease. The GHGRP requires gas-collecting landfills to estimate their annual emissions either with a recovery-first model (estimating emissions as a function of methane recovered) or a generation-first model (estimating emissions from a first-order-decay applied to waste-in-place). All four landfills choose to use the recovery-first model, which yields emissions that are one-quarter of those from the generation-first model and decreasing over 2019-2023, in contrast with the TROPOMI observations. Our TROPOMI estimates for two of the landfills agree with the generation-first model, with increasing emissions over 2019-2023 due to increasing waste-in-place or decreasing methane recovery, and are still higher than the generation-first model for the other two landfills. Further examination of the GHGRP emissions from all reporting landfills in the US shows that the 19% decrease in landfill emissions reported by the GHGI over 2005-2022 reflects an increasing preference for the recovery-first model by the reporting landfills, rather than an actual emission decrease. The generation-first model would imply an increase in landfill emissions over 2013-2022, and this is more consistent with atmospheric observations.

This paper explores the innovative application of Large Language Models (LLMs) in Virtual Reality (VR) environments to promote heritage education, focusing on traditional Scottish curling presented in the game ``Scottish Bonspiel VR''. Our study compares the effectiveness of LLM-based chatbots with pre-defined scripted chatbots, evaluating key criteria such as usability, user engagement, and learning outcomes. The results show that LLM-based chatbots significantly improve interactivity and engagement, creating a more dynamic and immersive learning environment. This integration helps document and preserve cultural heritage and enhances dissemination processes, which are crucial for safeguarding intangible cultural heritage (ICH) amid environmental changes. Furthermore, the study highlights the potential of novel technologies in education to provide immersive experiences that foster a deeper appreciation of cultural heritage. These findings support the wider application of LLMs and VR in cultural education to address global challenges and promote sustainable practices to preserve and enhance cultural heritage.

The Single Aperture Large Telescope for Universe Studies (SALTUS) is a far-infrared space mission concept with unprecedented spatial and spectral resolution. Saltus consists of a 14-m inflatable primary, providing 16 times the sensitivity and 4 times the angular resolution of Herschel, and two cryogenic detectors spanning a wavelength range of 34-660 microns and spectral resolving power of 300 - 1e7. Spectroscopic observations in the far-infrared offer many unique windows into the processes of star and planet formation. These include observations of low energy water transitions, the H2 mass tracer HD, many CHONS constraining molecules such as NH3 and H2S, and emission lines from the phonon modes of molecular ices. Observing these species will allow us to build a statistical sample of protoplanetary disk masses, characterize the water snowline, identify Kuiper Belt like debris rings around other stars, and trace the evolution CHONS from prestellar cores, through to protoplanetary disks and debris disks. This paper details details several key star and planet formation science goals achievable with SALTUS., Comment: 48 pages, 8 figures, submitted to SPIE JATIS

We report the detection of a dwarf satellite candidate (Triangulum IV: Tri IV) of the Triangulum galaxy (M33) using the deep imaging of Subaru/Hyper Suprime-Cam (HSC). From the apparent magnitude of the horizontal branch in Tri IV, the heliocentric distance of Tri IV is estimated to be $932^{+49}_{-43}$ kpc, indicating that Tri IV is located at the distance of $75^{+48}_{-40}$ kpc from the M33 center. This means that Tri IV is the probable satellite of M33, because its distance from M33 is within the virial radius of M33. We also estimate its surface brightness of $\mu_{\it V} = 29.72^{+0.10}_{-0.10}$ mag arcsec$^{-2}$, and half-light radius of $r_h = 1749^{+523}_{-425}$ pc, suggesting that Tri IV is an ultra-diffuse galaxy or dynamically heated galaxy. The surface brightness of Tri IV is too low to be detected in the previous survey, so this detection suggests that much fainter satellites may be present in the outskirts of M33., Comment: 9 pages, 3 figures, 1 tables, submitted to ApJL

Modern quantum technologies using matter are designed as closed quantum systems to isolate them from interactions with the environment. This design paradigm greatly constrains the scalability and limits practical implementation of such systems. Here, we introduce a novel computing paradigm, entropy computing, that works by conditioning a quantum reservoir thereby enabling the stabilization of a ground state. In this work, we experimentally demonstrate the feasibility of entropy computing by building a hybrid photonic-electronic computer that uses measurement-based feedback to solve non-convex optimization problems. The system functions by using temporal photonic modes to create qudits in order to encode probability amplitudes in the time-frequency degree of freedom of a photon. This scheme, when coupled with electronic interconnects, allows us to encode an arbitrary Hamiltonian into the system and solve non-convex continuous variables and combinatorial optimization problems. We show that the proposed entropy computing paradigm can act as a scalable and versatile platform for tackling a large range of NP-hard optimization problems.

Nancy Grace Roman Space Telescope will revolutionize our understanding of the Galactic Bulge with its Galactic Bulge Time Domain survey. At the same time, Rubin Observatories's Legacy Survey of Space and Time (LSST) will monitor billions of stars in the Milky Way. The proposed Roman survey of the Galactic Plane, with its NIR passbands and exquisite spacial resolution, promises groundbreaking insights for a wide range of time-domain galactic astrophysics. In this white paper, we describe the scientific returns possible from the combination of the Roman Galactic Plane Survey with the data from LSST., Comment: 8 pages, 1 figure, submitted to the 2024 Call for Community Input into the Definition of a Roman Galactic Plane Survey

Learning analytics has begun to use physiological signals because these have been linked with learners' cognitive and affective states. These signals, when interpreted through machine learning techniques, offer a nuanced understanding of the temporal dynamics of student learning experiences and processes. However, there is a lack of clear guidance on the optimal time window to use for analyzing physiological signals within predictive models. We conducted an empirical investigation of different time windows (ranging from 60 to 210 seconds) when analysing multichannel physiological sensor data for predicting cognitive load. Our results demonstrate a preference for longer time windows, with optimal window length typically exceeding 90 seconds. These findings challenge the conventional focus on immediate physiological responses, suggesting that a broader temporal scope could provide a more comprehensive understanding of cognitive processes. In addition, the variation in which time windows best supported prediction across classifiers underscores the complexity of integrating physiological measures. Our findings provide new insights for developing educational technologies that more accurately reflect and respond to the dynamic nature of learner cognitive load in complex learning environments., Comment: Presented at PhysioCHI: Towards Best Practices for Integrating Physiological Signals in HCI, May 11, 2024, Honolulu, HI, USA

Properties of Riesz capacity are developed with respect to the kernel exponent $p \in (-\infty,n)$, namely that capacity is monotonic as a function of $p$, that its endpoint limits recover the diameter and volume of the set, and that capacity is left-continuous with respect to $p$ and is right-continuous provided (when $p \geq 0$) that an additional hypothesis holds. Left and right continuity properties of the equilibrium measure are obtained too.

Satellite remote sensing missions have gained popularity over the past fifteen years due to their ability to cover large swaths of land at regular intervals, making them ideal for monitoring environmental trends. The FINCH mission, a 3U+ CubeSat equipped with a hyperspectral camera, aims to monitor crop residue cover in agricultural fields. Although hyperspectral imaging captures both spectral and spatial information, it is prone to various types of noise, including random noise, stripe noise, and dead pixels. Effective denoising of these images is crucial for downstream scientific tasks. Traditional methods, including hand-crafted techniques encoding strong priors, learned 2D image denoising methods applied across different hyperspectral bands, or diffusion generative models applied independently on bands, often struggle with varying noise strengths across spectral bands, leading to significant spectral distortion. This paper presents a novel approach to hyperspectral image denoising using latent diffusion models that integrate spatial and spectral information. We particularly do so by building a 3D diffusion model and presenting a 3-stage training approach on real and synthetically crafted datasets. The proposed method preserves image structure while reducing noise. Evaluations on both popular hyperspectral denoising datasets and synthetically crafted datasets for the FINCH mission demonstrate the effectiveness of this approach., Comment: To appear in 38th Annual Small Satellite Conference

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

State education finance systems play a fundamental role in providing the resources that schools and districts need to ensure student success. But modernizing and improving these systems is often complicated and can take years. Learning from their efforts to revamp education finance systems, Bellwether has found five common conditions that pave the way to success: (1) strong, diverse coalitions; (2) willing political champions; (3) rigorous, accessible research; (4) windows of economic opportunity; and (5) valid legal challenges. This policy playbook builds from that five-condition framework and offers more concrete guidance on how advocates, policymakers, and education leaders can influence K-12 funding reform in their states. It contains guiding principles to help stakeholders identify policy priorities and opportunities. It also offers guidance on how to assess and strengthen the state's readiness for change, including a checklist of questions, potential pitfalls to avoid, and examples from other states.

Enrollment in California public schools has been declining and is projected to fall even more steeply during the next decade. Because funding for school districts is largely based on average daily attendance rates, a decline in enrollment results in a loss of funding. To address budget shortfalls and align services with student counts, many districts have consolidated or closed schools, or they are contemplating doing so. Leaders in declining-enrollment districts will need to consider the benefits and costs when seeking to balance their budgets with new enrollment realities. This report makes three recommendations for how local decision makers, including district officials and school board members, should approach school closures: (1) establish and execute an inclusive, transparent process; (2) implement a strategy to provide displaced students and the broader student community with accessible, high-quality educational opportunities; and (3) develop a long-term plan to address factors--such as housing affordability, gentrification, and economic divestment--contributing to the disproportionate closure of schools serving low-income students and students of color. This report aims to provide evidence and suggestions to help state and local education leaders as they confront declining enrollment and urges them to work with other city and county agencies, including housing and economic development authorities, to increase opportunities for low-income families and communities of color so that fewer enrollment-related school closures are necessary.

Declining student enrollment is leading to a loss of revenue in many California school districts. To address ongoing budget shortfalls, many districts have consolidated or shuttered schools, and others are contemplating doing so. A new report and working paper, summarized in this brief, explore the racial dimensions of school closures and how to address them. The authors find that closures are far more common for schools that serve higher proportions of Black students. Even after controlling for a range of factors, the odds of school closure increase by nearly 25 percent for every 10-percentage-point increase in the share of Black students. This research situates this and other disparities within the U.S. history of segregation, neighborhood disinvestment, and gentrification, and provides evidence and suggestions to help education leaders center equity as they confront declining enrollment-related school closures.

Biodiversity data are substantially increasing, spurred by technological advances and community (citizen) science initiatives. To integrate data is, likewise, becoming more commonplace. Open science promotes open sharing and data usage. Data standardization is an instrument for the organization and integration of biodiversity data, which is required for complex research projects and digital twins. However, just like with an actual instrument, there is a learning curve to understanding the data standards field. Here we provide a guide, for data providers and data users, on the logistics of compiling and utilizing biodiversity data. We emphasize data standards, because they are integral to data integration. Three primary avenues for compiling biodiversity data are compared, explaining the importance of research infrastructures for coordinated long-term data aggregation. We exemplify the Biodiversity Digital Twin (BioDT) as a case study. Four approaches to data standardization are presented in terms of the balance between practical constraints and the advancement of the data standards field. We aim for this paper to guide and raise awareness of the existing issues related to data standardization, and especially how data standards are key to data interoperability, i.e., machine accessibility. The future is promising for computational biodiversity advancements, such as with the BioDT project, but it rests upon the shoulders of machine actionability and readability, and that requires data standards for computational communication., Comment: 42 pages, 2 figures, 1 box, 1 table

Chemical cartography of the Galactic disk provides insights to its structure and assembly history over cosmic time. In this work, we use chemical cartography to explore chemical gradients and azimuthal substructure in the Milky Way disk with giant stars from APOGEE DR17. We confirm the existence of a radial metallicity gradient in the disk of $\Delta$[Fe/H]/$\Delta$R $\sim -0.066 \pm 0.0004$ dex/kpc and a vertical metallicity gradient of $\Delta$[Fe/H]/$\Delta$Z $\sim -0.164 \pm 0.001$ dex/kpc. We find azimuthal variations ($\pm0.1$ dex) on top of the radial metallicity gradient that have been previously established with other surveys. The APOGEE giants show strong correlations with stellar age and the intensity of azimuthal variations in iron; older stellar populations show the largest deviations from the radial metallicity gradient. Beyond iron, we show that other elements (e.g., Mg, O) display azimuthal variations at the $\pm0.05$ dex-level across the Galactic disk. We illustrate that moving into the orbit-space could help constrain the mechanisms producing these azimuthal metallicity variations. These results suggest that the spiral arms of the Galaxy are not solely responsible for azimuthal metallicity variations and other Galactic processes are at play., Comment: 19 pages, 11 figures

The SALTUS Probe mission will provide a powerful far-infrared (far-IR) pointed space observatory to explore our cosmic origins and the possibility of life elsewhere. The observatory employs an innovative deployable 14-m aperture, with a sunshield that will radiatively cool the off-axis primary to <45K. This cooled primary reflector works in tandem with cryogenic coherent and incoherent instruments that span the 34 to 660 micron far-IR range at both high and moderate spectral resolutions., Comment: 49 pages, 10 figures, 3 tables, submitted to SPIE JATIS

Educational games are being increasingly used to support self-paced learning. However, educators and system designers often face challenges in monitoring student affect and cognitive load. Existing assessments in game-based learning environments (GBLEs) tend to focus more on outcomes rather than processes, potentially overlooking key aspects of the learning journey that include learner affect and cognitive load. To address this issue, we collected data and trained a model to track learner cognitive load while they used an online literacy game for English. We collected affect-related physiological data and pupil data during gameplay to enable the development of models that identify these latent characteristics of learner processes. Our model indicates the feasibility of using these data to track cognitive load in GBLEs. Our multimodal model distinguished different levels of cognitive load, achieving the highest Kappa (.417) and accuracy (70%). Our model reveals the importance of including affect-related features (i.e., EDA and heart rate) when predicting cognitive load and extends recent findings suggesting the benefit of using multiple channels when modeling latent aspects of learner processes. Findings also suggest that cognitive load tracking could now be used to facilitate the creation of personalized learning experiences., Comment: This work has been accepted by the 17th International Conference on Educational Data Mining