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The garnet compound Yb$_3$Ga$_5$O$_{12}$ is a fascinating material that is considered highly suitable for low-temperature refrigeration, via the magnetocaloric effect, in addition to enabling the exploration of quantum states with long-range dipolar interactions. It has previously been theorized that the magnetocaloric effect can be enhanced, in Yb$_3$Ga$_5$O$_{12}$ , via magnetic soft mode excitations which in the hyperkagome structure would be derived from an emergent magnetic structure formed from nanosized 10-spin loops. We study the magnetic field dependence of bands of magnetic soft mode excitations in the effective spin $S = 1/2$ hyperkagome compound Yb$_3$Ga$_5$O$_{12}$ using single crystal inelastic neutron scattering. We probe the magnetically short ranged ordered state, in which we determine magnetic nanoscale structures coexisting with a fluctuating state, and the magnetically saturated state. We determine that Yb$_3$Ga$_5$O$_{12}$ can be described as a quantum dipolar magnet with perturbative weak near-neighbor and inter-hyperkagome exchange interaction. The magnetic excitations, under the application of a magnetic field, reveal highly robust soft modes with distinctive signatures of the quantum nature of the Yb3+ spins. Our results enhance our understanding of soft modes in topological frustrated magnets that drive both the unusual physics of quantum dipolar systems and future refrigerant material design.

Healthcare simulations help learners develop teamwork and clinical skills in a risk-free setting, promoting reflection on real-world practices through structured debriefs. However, despite video's potential, it is hard to use, leaving a gap in providing concise, data-driven summaries for supporting effective debriefing. Addressing this, we present TeamVision, an AI-powered multimodal learning analytics (MMLA) system that captures voice presence, automated transcriptions, body rotation, and positioning data, offering educators a dashboard to guide debriefs immediately after simulations. We conducted an in-the-wild study with 56 teams (221 students) and recorded debriefs led by six teachers using TeamVision. Follow-up interviews with 15 students and five teachers explored perceptions of its usefulness, accuracy, and trustworthiness. This paper examines: i) how TeamVision was used in debriefing, ii) what educators found valuable and challenging, and iii) perceptions of its effectiveness. Results suggest TeamVision enables flexible debriefing and highlights the challenges and implications of using AI-powered systems in healthcare simulation., Comment: Accepted to CHI 2025

Magnetization noise measurements on the spin ice Dy${}_2$Ti${}_2$O${}_7$ have revealed a remarkable `pink noise' power spectrum $S(f,T)$ below 4 K, including evidence of magnetic monopole excitations diffusing in a fractal landscape. However, at higher temperatures, the reported values of the anomalous exponent $b(T)$ describing the high frequency tail of $S(f,T)$ are not easy to reconcile with other results in the literature, which generally suggest significantly smaller deviations from the Brownian motion value of $b=2$, that become negligible above $T=20$ K. We accurately estimate $b(T)$ at temperatures between 2~K and 20~K, using a.c. susceptibility measurements that, crucially, stretch up to the relatively high frequency of $f = 10^6$ Hz. We show that previous noise measurements underestimate $b(T)$ and we suggest reasons for this. Our results establish deviations in $b(T)$ from $b=2$ up to about 20 K. However studies on different samples confirms that $b(T)$ is sample dependent: the details of this dependence agree in part, though not completely, with previous studies of the effect of crystal defects on monopole population and diffusion. Our results establish the form of $b(T)$ which characterises the subtle, and evolving, nature of monopole diffusion in the dense Coulomb fluid, a highly correlated state, where several dynamical processes combine. They do not rule out the importance of a fractal landscape picture emerging at lower temperatures where the monopole gas is dilute.

Learning analytics dashboards (LADs) simplify complex learner data into accessible visualisations, providing actionable insights for educators and students. However, their educational effectiveness has not always matched the sophistication of the technology behind them. Explanatory and interactive LADs, enhanced by generative AI (GenAI) chatbots, hold promise by enabling dynamic, dialogue-based interactions with data visualisations and offering personalised feedback through text. Yet, the effectiveness of these tools may be limited by learners' varying levels of GenAI literacy, a factor that remains underexplored in current research. This study investigates the role of GenAI literacy in learner interactions with conventional (reactive) versus scaffolding (proactive) chatbot-assisted LADs. Through a comparative analysis of 81 participants, we examine how GenAI literacy is associated with learners' ability to interpret complex visualisations and their cognitive processes during interactions with chatbot-assisted LADs. Results show that while both chatbots significantly improved learner comprehension, those with higher GenAI literacy benefited the most, particularly with conventional chatbots, demonstrating diverse prompting strategies. Findings highlight the importance of considering learners' GenAI literacy when integrating GenAI chatbots in LADs and educational technologies. Incorporating scaffolding techniques within GenAI chatbots can be an effective strategy, offering a more guided experience that reduces reliance on learners' GenAI literacy.

Measurements of the EMC effect in the tritium and helium-3 mirror nuclei are reported. The data were obtained by the MARATHON Jefferson Lab experiment, which performed deep inelastic electron scattering from deuterium and the three-body nuclei, using a cryogenic gas target system and the High Resolution Spectrometers of the Hall A Facility of the Lab. The data cover the Bjorken $x$ range from 0.20 to 0.83, corresponding to a squared four-momentum transfer $Q^2$ range from 2.7 to $11.9\gevsq$, and to an invariant mass $W$ of the final hadronic state greater than 1.84 GeV/${\it c}^2$. The tritium EMC effect measurement is the first of its kind. The MARATHON experimental results are compared to results from previous measurements by DESY-HERMES and JLab-Hall C experiments, as well as with few-body theoretical predictions., Comment: arXiv admin note: text overlap with arXiv:2104.05850

Visual learning analytics (VLA) is becoming increasingly adopted in educational technologies and learning analytics dashboards to convey critical insights to students and educators. Yet many students experienced difficulties in comprehending complex VLA due to their limited data visualisation literacy. While conventional scaffolding approaches like data storytelling have shown effectiveness in enhancing students' comprehension of VLA, these approaches remain difficult to scale and adapt to individual learning needs. Generative AI (GenAI) technologies, especially conversational agents, offer potential solutions by providing personalised and dynamic support to enhance students' comprehension of VLA. This study investigates the effectiveness of GenAI agents, particularly when integrated with scaffolding techniques, in improving students' comprehension of VLA. A randomised controlled trial was conducted with 117 higher education students to compare the effects of two types of GenAI agents: passive agents, which respond to student queries, and proactive agents, which utilise scaffolding questions, against standalone scaffolding in a VLA comprehension task. The results show that passive agents yield comparable improvements to standalone scaffolding both during and after the intervention. Notably, proactive GenAI agents significantly enhance students' VLA comprehension compared to both passive agents and standalone scaffolding, with these benefits persisting beyond the intervention. These findings suggest that integrating GenAI agents with scaffolding can have lasting positive effects on students' comprehension skills and support genuine learning.

The antiferromagnetic structure of Yb$_{3}$Ga$_{5}$O$_{12}$ is identified by neutron diffraction experiments below the previously-known transition at $T_{\lambda}=54$ mK. The magnetic propagation vector is found to be ${\bf k}=(1/2, 1/2, 0)$, an unusual wave-vector in the garnet structure. The associated complex magnetic structure highlights the role of exchange interactions in a nearly isotropic system dominated by dipolar interactions and finds echos with exotic structures theoretically proposed. Reduced values of the ordered moments may indicate significant quantum fluctuations in this effective spin-1/2 geometrically frustrated magnet.

Inclusive electron scattering at carefully chosen kinematics can isolate scattering from short-range correlations (SRCs), produced through hard, short-distance interactions of nucleons in the nucleus. Because the two-nucleon (2N) SRCs arise from the same N-N interaction in all nuclei, the cross section in the SRC-dominated regime is identical up to an overall scaling factor, and the A/2H cross section ratio is constant in this region. This scaling behavior has been used to identify SRC dominance and to map out the contribution of SRCs for a wide range of nuclei. We examine this scaling behavior at lower momentum transfers using new data on $^2$H, $^3$H, and $^3$He which show that the scaling region is larger than in heavy nuclei. Based on the improved scaling, especially for $^3$H/$^3$He, we examine the ratios at kinematics where three-nucleon SRCs may play an important role. The data for the largest initial nucleon momenta are consistent with isolation of scattering from 3N-SRCs, and suggest that the very-highest momentum nucleons in $^3$He have a nearly isospin-independent momentum configuration, or a small enhancement of the proton distribution.

In 2018, the E12-17-003 experiment was conducted at the Thomas Jefferson National Accelerator Facility (JLab) to explore the possible existence of an nnLambda state in the reconstructed missing mass distribution from a tritium gas target [K. N. Suzuki et al., Prog. Theor. Exp. Phys. 2022, 013D01 (2022), B. Pandey et al., Phys. Rev. C 105, L051001 (2022)]. As part of this investigation, data was also collected using a gaseous hydrogen target, not only for a precise absolute mass scale calibration but also for the study of Lambda/Sigma^0 electroproduction. This dataset was acquired at Q^2~0.5 (GeV/c)^2, W=2.14 GeV, and theta_{gamma K}^{c.m.}~8 deg. It covers forward angles where photoproduction data is scarce and a low-Q^2 region that is of interest for hypernuclear experiments. On the other hand, this kinematic region is at a slightly higher Q^2 than previous hypernuclear experiments, thus providing crucial information for understanding the Q^2 dependence of the differential cross sections for Lambda/Sigma^0 hyperon electroproduction. This paper reports on the Q^2 dependence of the differential cross section for the e + p -> e' + K^+ + Lambda/Sigma^0 reaction in the 0.2-0.8 (GeV/c)^2, and provides comparisons with the currently available theoretical models., Comment: 11 pages, 15 figures

Multimodal Learning Analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across various learning settings, little research has been conducted to evaluate these systems in authentic learning contexts, particularly regarding students' perceived fairness, accountability, transparency, and ethics (FATE). Understanding these perceptions is essential to using MMLA effectively without introducing ethical complications or negatively affecting how students learn. This study aimed to address this gap by assessing the FATE of MMLA in an authentic, collaborative learning context. We conducted semi-structured interviews with 14 undergraduate students who used MMLA visualisations for post-activity reflection. The findings highlighted the significance of accurate and comprehensive data representation to ensure visualisation fairness, the need for different levels of data access to foster accountability, the imperative of measuring and cultivating transparency with students, and the necessity of transforming informed consent from dichotomous to continuous and measurable scales. While students value the benefits of MMLA, they also emphasise the importance of ethical considerations, highlighting a pressing need for the LA and MMLA community to investigate and address FATE issues actively., Comment: 16 pages, 1 figure

Using the National Center for Education Statistics (NCES) 2012/17 Beginning Postsecondary Students Longitudinal Study (BPS:12/17), this research study explores the persistence to bachelor's degree attainment of adult students. Specifically, this study looks at adult students who expected to earn a bachelor's degree or higher, and analyzes whether those students who begin their postsecondary education at a 2-year public or private nonprofit institution are more or less likely to attain a bachelor's degree compared to adult students who begin at a 4-year public or 4-year private nonprofit institution. Our findings indicate that, after controlling for common predictors of persistence such as high school GPA, receipt of Pell Grants, and other demographic data, adult students who begin at a 2-year public or private nonprofit institution are no less likely to attain a bachelor's degree compared to adult students who start at a 4-year public institution. In addition, full-time enrollment intensity does not increase the odds of persistence compared to mixed enrollment intensity for adult students.

The growth of human activity in outer space is attracting more International Relations (IR) scholar's attention, enabling an understanding of the involvement of specific groups of actors and the dynamics of political negotiations that lead to concluding agreements on using outer space for peaceful purposes. This article provides analysis based on the triangulation of qualitative data gathered via document analysis and in-depth semi-structured expert interviews to gain insight into the involvement of the actors responsible for the negotiations that led to the Artemis Accords and their diplomatic communication style. The results identified different uses of public and private diplomatic communication for advancing norms of behaviour and transparency. Negotiators used public diplomatic communication in order to influence foreign governments on the need for norms of behaviour and transparency to further peaceful space exploration beyond low Earth orbit. Private diplomatic communication facilitated the inclusion of commercial partners., Comment: post-peer review manuscript, its publication follows the rules by Brill https://brill.com/page/419324?language=en

The integration of unmanned aerial vehicles (UAVs) into shared airspace for beyond visual line of sight (BVLOS) operations presents significant challenges but holds transformative potential for sectors like transportation, construction, energy and defense. A critical prerequisite for this integration is equipping UAVs with enhanced situational awareness to ensure safe operations. Current approaches mainly target single object detection or classification, or simpler sensing outputs that offer limited perceptual understanding and lack the rapid end-to-end processing needed to convert sensor data into safety-critical insights. In contrast, our study leverages radar technology for novel end-to-end semantic segmentation of aerial point clouds to simultaneously identify multiple collision hazards. By adapting and optimizing the PointNet architecture and integrating aerial domain insights, our framework distinguishes five distinct classes: mobile drones (DJI M300 and DJI Mini) and airplanes (Ikarus C42), and static returns (ground and infrastructure) which results in enhanced situational awareness for UAVs. To our knowledge, this is the first approach addressing simultaneous identification of multiple collision threats in an aerial setting, achieving a robust 94% accuracy. This work highlights the potential of radar technology to advance situational awareness in UAVs, facilitating safe and efficient BVLOS operations., Comment: 16 pages, 13 figures, 4 tables

The safe operation of drone swarms beyond visual line of sight requires multiple safeguards to mitigate the risk of collision between drones flying in close-proximity scenarios. Cooperative navigation and flight coordination strategies that rely on pre-planned trajectories, constant %{satellite and network connectivity and reliable Global Navigation Satellite System (GNSS) positioning are brittle to failure. Drone embedded sense and detect offers a comprehensive mode of separation between drones for deconfliction and collision avoidance. This paper presents the first airborne LiDAR based solution for drone-swarm detection and localization using 3D deep learning model. It adapts an existing deep learning neural network to the air-to-air drone scenario by expanding the scan space vertically. A new sparse convolution is proposed and applied to accelerate the backbone layer, which is the most time-consuming part of the neural network. To collect training data of safety critical, close-proximity multi-drone operations, a scenario Digital Twin is used to augment real datasets with high fidelity synthetic data. The trained model achieves over 80% recall and 96% precision when tested on real-world datasets. By incorporating a tracking-by-detection algorithm the system can reliably monitor the separation distance of multiple drones in challenging environments.

The electromagnetic form factors of the proton and neutron encode information on the spatial structure of their charge and magnetization distributions. While measurements of the proton are relatively straightforward, the lack of a free neutron target makes measurements of the neutron's electromagnetic structure more challenging and more sensitive to experimental or model-dependent uncertainties. Various experiments have attempted to extract the neutron form factors from scattering from the neutron in deuterium, with different techniques providing different, and sometimes large, systematic uncertainties. We present results from a novel measurement of the neutron magnetic form factor using quasielastic scattering from the mirror nuclei $^3$H and $^3$He, where the nuclear effects are larger than for deuterium but expected to largely cancel in the cross-section ratios. We extracted values of the neutron magnetic form factor for low-to-modest momentum transfer, $0.6

Multimodal Learning Analytics (MMLA) innovations make use of rapidly evolving sensing and artificial intelligence algorithms to collect rich data about learning activities that unfold in physical learning spaces. The analysis of these data is opening exciting new avenues for both studying and supporting learning. Yet, practical and logistical challenges commonly appear while deploying MMLA innovations "in-the-wild". These can span from technical issues related to enhancing the learning space with sensing capabilities, to the increased complexity of teachers' tasks and informed consent. These practicalities have been rarely discussed. This paper addresses this gap by presenting a set of lessons learnt from a 2-year human-centred MMLA in-the-wild study conducted with 399 students and 17 educators. The lessons learnt were synthesised into topics related to i) technological/physical aspects of the deployment; ii) multimodal data and interfaces; iii) the design process; iv) participation, ethics and privacy; and v) the sustainability of the deployment., Comment: Submitted to TOCHI

$^{15}$N($\alpha$,$\gamma$)$^{19}$F is believed to be the primary means of stellar nucleosynthesis of fluorine. Here we present the use of a single-fluid bubble chamber to measure the cross section of the time-inverse photo-dissociation reaction. The method benefits from a luminosity increase of several orders of magnitude due to the use of a thicker liquid target -- when compared to thin films or gas targets -- and from the reciprocity theorem. We discuss the results of an experiment at the Thomas Jefferson National Accelerator Facility, where the cross section of the photodisintegration process $^{19}$F($\gamma$, $\alpha$)$^{15}$N was measured by bombarding a superheated fluid of C$_3$F$_8$ with bremsstrahlung $\gamma$-rays produced by impinging a 4 - 5.5 MeV electron beam on a Cu radiator. From the photodissociation yield the cross section was extracted by performing a convolution with a Monte Carlo-generated $\gamma$-ray beam spectrum. The measurement produced a cross section that was then time inverted using the reciprocity theorem. The cross section for the $^{15}$N($\alpha$,$\gamma$)$^{19}$F reaction was determined down to a value in the range of hundreds of picobarns. With further improvements of the experimental setup the technique could potentially push cross section measurements down to the single picobarn range., Comment: Corrected Typos. Accepted for Publication in Physical Review C

Let $p_\mathrm{c}$ and $q_\mathrm{c}$ be the threshold and the expectation threshold, respectively, of an increasing family $\mathcal{F}$ of subsets of a finite set $X$, and let $l$ be the size of a largest minimal element of $\mathcal{F}$. Recently, Park and Pham proved the Kahn-Kalai conjecture, which says that $p_\mathrm{c} \leqslant K q_\mathrm{c} \log_2 l$ for some universal constant $K$. Here we slightly strengthen their result by showing that $p_\mathrm{c} \leqslant 1 - \mathrm{e}^{-K q_\mathrm{c} \log_2 l}$. The idea is to apply the Park-Pham Theorem to an appropriate `cloned' family $\mathcal{F}_k$, reducing the general case (of this and related results) to the case where the individual element probability $p$ is small., Comment: 5 pages; minor edits; submitted

IQUAFLOW is a new image quality framework that provides a set of tools to assess image quality. The user can add custom metrics that can be easily integrated. Furthermore, iquaflow allows to measure quality by using the performance of AI models trained on the images as a proxy. This also helps to easily make studies of performance degradation of several modifications of the original dataset, for instance, with images reconstructed after different levels of lossy compression; satellite images would be a use case example, since they are commonly compressed before downloading to the ground. In this situation, the optimization problem consists in finding the smallest images that provide yet sufficient quality to meet the required performance of the deep learning algorithms. Thus, a study with iquaflow is suitable for such case. All this development is wrapped in Mlflow: an interactive tool used to visualize and summarize the results. This document describes different use cases and provides links to their respective repositories. To ease the creation of new studies, we include a cookie-cutter repository. The source code, issue tracker and aforementioned repositories are all hosted on GitHub https://github.com/satellogic/iquaflow.

Latest advances in Super-Resolution (SR) have been tested with general purpose images such as faces, landscapes and objects, mainly unused for the task of super-resolving Earth Observation (EO) images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both Full-Reference and No-Reference Image Quality Assessment (IQA) metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict quality (as a No-Reference metric) by training on any property of the image (i.e. its resolution, its distortions...) and also able to optimize SR algorithms for a specific metric objective. This work is part of the implementation of the framework IQUAFLOW which has been developed for evaluating image quality, detection and classification of objects as well as image compression in EO use cases. We integrated our experimentation and tested our QMRNet algorithm on predicting features like blur, sharpness, snr, rer and ground sampling distance (GSD) and obtain validation medRs below 1.0 (out of N=50) and recall rates above 95\%. Overall benchmark shows promising results for LIIF, CAR and MSRN and also the potential use of QMRNet as Loss for optimizing SR predictions. Due to its simplicity, QMRNet could also be used for other use cases and image domains, as its architecture and data processing is fully scalable., Comment: 29 pages, 13 figures, 9 tables

When protons and neutrons (nucleons) are bound into atomic nuclei, they are close enough together to feel significant attraction, or repulsion, from the strong, short-distance part of the nucleon-nucleon interaction. These strong interactions lead to hard collisions between nucleons, generating pairs of highly-energetic nucleons referred to as short-range correlations (SRCs). SRCs are an important but relatively poorly understood part of nuclear structure and mapping out the strength and isospin structure (neutron-proton vs proton-proton pairs) of these virtual excitations is thus critical input for modeling a range of nuclear, particle, and astrophysics measurements. Hitherto measurements used two-nucleon knockout or ``triple-coincidence'' reactions to measure the relative contribution of np- and pp-SRCs by knocking out a proton from the SRC and detecting its partner nucleon (proton or neutron). These measurementsshow that SRCs are almost exclusively np pairs, but had limited statistics and required large model-dependent final-state interaction (FSI) corrections. We report on the first measurement using inclusive scattering from the mirror nuclei $^3$H and $^3$He to extract the np/pp ratio of SRCs in the A=3 system. We obtain a measure of the np/pp SRC ratio that is an order of magnitude more precise than previous experiments, and find a dramatic deviation from the near-total np dominance observed in heavy nuclei. This result implies an unexpected structure in the high-momentum wavefunction for $^3$He and $^3$H. Understanding these results will improve our understanding of the short-range part of the N-N interaction.