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The Mastery-Based Learning Collaborative (MBLC) is a demonstration project taking place in 47 schools across Washington to help decision makers understand what quality mastery-based (or competency-based) learning looks like, how long it takes to implement, and what resources are necessary. The schools receive funding, coaching, and professional learning as part of a statewide network. In its first three years, the initiative has led to deeper Mastery-Based Learning (MBL) implementation and has shown positive impacts on early outcomes such as school climate, cultural responsiveness, and student engagement. Almost all school leaders said they would recommend the MBLC to other school leaders. The Aurora Institute is conducting a six-year evaluation of the MBLC to identify policies, practices, and system changes that can support successful mastery-based and culturally responsive learning. The report shares findings from the first three years of the first cohort of MBLC schools, based on extensive surveys and interviews of educators, school leaders, students, state leaders, and professional learning providers. This evaluation report offers insights and strategies for anyone working to shift K-12 schools, districts, and states toward more student-centered, equitable, and culturally responsive approaches.

Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in patients with dense breast tissue or fibrocystic conditions. Contrast-Enhanced Spectral Mammography (CESM), a second-level imaging technique, offers enhanced accuracy in tumor detection. Nonetheless, its application is restricted due to higher radiation exposure, the use of contrast agents, and limited accessibility. As a result, CESM is typically reserved for select cases, leaving many patients to rely solely on FFDM despite the superior diagnostic performance of CESM. While biopsy remains the gold standard for definitive diagnosis, it is an invasive procedure that can cause discomfort for patients. We introduce a multimodal, multi-view deep learning approach for virtual biopsy, integrating FFDM and CESM modalities in craniocaudal and mediolateral oblique views to classify lesions as malignant or benign. To address the challenge of missing CESM data, we leverage generative artificial intelligence to impute CESM images from FFDM scans. Experimental results demonstrate that incorporating the CESM modality is crucial to enhance the performance of virtual biopsy. When real CESM data is missing, synthetic CESM images proved effective, outperforming the use of FFDM alone, particularly in multimodal configurations that combine FFDM and CESM modalities. The proposed approach has the potential to improve diagnostic workflows, providing clinicians with augmented intelligence tools to improve diagnostic accuracy and patient care. Additionally, as a contribution to the research community, we publicly release the dataset used in our experiments, facilitating further advancements in this field.

Federated learning (FL) is a popular privacy-preserving edge-to-cloud technique used for training and deploying artificial intelligence (AI) models on edge devices. FL aims to secure local client data while also collaboratively training a global model. Under standard FL, clients within the federation send model updates, derived from local data, to a central server for aggregation into a global model. However, extensive research has demonstrated that private data can be reliably reconstructed from these model updates using gradient inversion attacks (GIAs). To protect client data from server-side GIAs, previous FL schemes have employed fully homomorphic encryption (FHE) to secure model updates while still enabling popular aggregation methods. However, current FHE-based FL schemes either incur substantial computational overhead or trade security and/or model accuracy for efficiency. We introduce BlindFL, a framework for global model aggregation in which clients encrypt and send a subset of their local model update. With choice over the subset size, BlindFL offers flexible efficiency gains while preserving full encryption of aggregated updates. Moreover, we demonstrate that implementing BlindFL can substantially lower space and time transmission costs per client, compared with plain FL with FHE, while maintaining global model accuracy. BlindFL also offers additional depth of security. While current single-key, FHE-based FL schemes explicitly defend against server-side adversaries, they do not address the realistic threat of malicious clients within the federation. By contrast, we theoretically and experimentally demonstrate that BlindFL significantly impedes client-side model poisoning attacks, a first for single-key, FHE-based FL schemes., Comment: 12 pages, 14 figures

We report upgraded Giant Metrewave radio telescope and Karl J. Jansky Very Large Array radio observations of a low-mass merging galaxy cluster PSZ2 G181.06+48.47. This exceptional galaxy cluster hosts two megaparsec-scale diffuse sources, symmetrically located with respect to the cluster center and separated by about 2.6 Mpc. We detect these low surface brightness sources in our new high-frequency observations (0.3-2 GHz) and classify them as radio relics associated with merger-driven shock fronts. The southwest relic exhibits an inverted morphology and shows evidence of spectral steepening in the post-shock region, potentially tracing a high Mach number shock ($\sim 4$) under the framework of diffusive shock acceleration. The northeast relic is found to be highly polarized with a 22% average polarization fraction at 1.5 GHz and aligned magnetic field vectors. Its spectral and polarization properties, along with the presence of a nearby tailed galaxy, support re-acceleration scenarios. The merger axis defined by the two relics is tilted by $\sim 45$ degree with respect to the plane of the sky, which implies an unprecedented projected separation of $\sim 3.5$ Mpc. We also detect a possible faint radio halo, suggesting weak turbulence in the central cluster region. We conclude that the faint double relics can be best explained by two outward moving shock waves in which particles are (re-)accelerated and that the cluster is in an evolved merger state. PSZ2 G181.06+48.47 presents a unique opportunity to investigate particle acceleration in low mass systems characterized by large relic separations., Comment: 23 pages, 11 figures, Submitted to ApJ

Relics are diffuse, highly-polarized radio sources that trace merger-driven shocks at the periphery of merging galaxy clusters. The LOFAR survey recently discovered a rare example of double relics in the low-mass cluster PSZ2 G181.06+48.47. Through a detailed exploration of new Chandra and XMM-Newton observations, we reveal that PSZ2 G181.06+48.47 has a lower mass ($M_{500,X}=2.32^{+0.29}_{-0.25}\times10^{14}$ M$_{\odot}$) than previously thought. Despite its cool global temperature of $kT_{500}=3.62^{+0.15}_{-0.07}$ keV, PSZ2 G181.06+48.47 is one of the most disturbed clusters in the Planck sample, with a complex morphological and thermodynamic structure. We discover a set of three discontinuities within <500 kpc of the cluster center, and, from a surface brightness analysis, place $5\sigma$ upper limits of $M_{NE}<1.43$ and $M_{SW}<1.57$ for any shock associated with the relic locations. We also revise established scaling relations for double radio-relics by adding 12 new systems not included in previous work. The PSZ2 G181.06+48.47 relics have the widest separation (scaled for $r_{500}$) of all known double-relic systems. The exceptional distance from the cluster center ($>r_{200}$), indicates the relics may be associated with shocks in the ``run-away" phase. We propose that this late-stage, post-apocenter merger is captured as the two subclusters with a mass ratio of 1.2-1.4 fall back into each other. The outer relic shocks were likely produced at the first core passage, while the inner discontinuities are associated with the second infall., Comment: Resubmitted to ApJ on Feb 4, 2025, after addressing the referee comments. 28 pages, 10 figures. Companion paper discussing the radio properties can be found at arXiv:2501.08390. Companion weak-lensing reconstruction paper can be found at arXiv:2501.09067

A growing number of directly-imaged companions have been recently characterised, with robust constraints on carbon-to-oxygen ratios and even isotopic ratios. Many companions and isolated targets have also shown spectral variability. In this work we observed the super-Jupiter AB~Pictoris~b across four consecutive nights using VLT/CRIRES+ as part of the ESO SupJup survey, exploring how the constraints on chemical composition and temperature profile change over time using spectral line shape variations between nights. We performed atmospheric retrievals of the high-resolution observations and found broadly consistent results across all four nights, but there were differences for some parameters. We clearly detect H$_2$O, $^{12}$CO and $^{13}$CO in each night, but abundances varied by $\sim2\sigma$, which was correlated to the deep atmosphere temperature profiles. We also found differences in the $^{12}$C$/^{13}$C ratios in each night by up to $\sim3\sigma$, which seemed to be correlated with the cloud deck pressure. Our combined retrieval simultaneously analysing all nights together constrained broadly the average of each night individually, with the C/O$=0.59\pm0.01$, consistent with solar composition, and $^{12}$C$/^{13}$C~$ = 102\pm8$, slightly higher than the ISM and Solar System values. We also find a low projected rotational velocity, suggesting that AB~Pictoris~b is either intrinsically a slow rotator due to its young age or that the spin axis is observed pole-on with a $\sim90^\circ$ misalignment with its orbit inclination. Future observations will be able to further explore the variability and orbit of AB~Pictoris~b as well as for other companions., Comment: 22 pages, 17 figures, 4 tables, accepted for publication in MNRAS

Large X-ray observatories such as Chandra and XMM-Newton have been delivering scientific breakthroughs in research fields as diverse as our Solar System, the astrophysics of stars, stellar explosions and compact objects, accreting super-massive black holes, and large-scale structures traced by the hot plasma permeating and surrounding galaxy groups and clusters. The recently launched observatory XRISM is opening in earnest the new observational window of non-dispersive high-resolution spectroscopy. However, several quests are left open, such as the effect of the stellar radiation field on the habitability of nearby planets, the Equation-of-State regulating matter in neutron stars, the origin and distribution of metals in the Universe, the processes driving the cosmological evolution of the baryons locked in the gravitational potential of Dark Matter and the impact of supermassive black hole growth on galaxy evolution, just to mention a few. Furthermore, X-ray astronomy is a key player in multi-messenger astrophysics. Addressing these quests experimentally requires an order-of-magnitude leap in sensitivity, spectroscopy and survey capabilities with respect to existing X-ray observatories. This paper succinctly summarizes the main areas where high-energy astrophysics is expected to contribute to our understanding of the Universe in the next decade and describes a new mission concept under study by the European Space Agency, the scientific community worldwide and two International Partners (JAXA and NASA), designed to enable transformational discoveries: NewAthena. This concept inherits its basic payload design from a previous study carried out until 2022, Athena., Comment: Published in Nature Astronomy. 9 pages, 4 figures

In this paper we introduce the clus model, which has been newly implemented in the X-ray spectral fitting software package SPEX. Based on the 3D radial profiles of the gas density, temperature, metal abundance, turbulent, and inflow/outflow velocities, the clus model creates spectra for a chosen projected region on the sky. Additionally, it can also take into account the resonant scattering. We show a few applications of the clus model on simulated spectra of the massive elliptical galaxy NGC 4636, and galaxy clusters A383, A2029, A1795, A262, and the Perseus cluster. We quantify the effect of projection, as well as resonant scattering on inferred profiles of the iron abundance and temperature, assuming the resolution similar to Chandra ACIS-S and XRISM Resolve. Our results show that, depending on the mass of the object, as well as the projected distance from its core, neither a single-temperature, double-temperature, nor the Gaussian-shaped differential emission measure models can accurately describe the input emission measure distribution of these massive objects. The largest effect of projection as well as resonant scattering is seen for projected profiles of iron abundance of NGC 4636, where we are able to reproduce the observed iron abundance drop in its inner-most few kiloparsecs. Furthermore, we find that projection effects also influence the best-fit temperature, and the magnitude of this effect varies depending on the underlying hydrodynamical profiles of individual objects. In the core, the projection effects are the largest for A1795 and NGC 4636, while in the outskirts the largest difference between 2D and 3D temperature profiles are for Perseus and A1795, regardless of the instrumental resolution. These findings might potentially have an impact on cross-calibration studies between different instruments, as well as on the precision cosmology., Comment: 18 pages, 11 figures, 3 tables, accepted to Astronomy and Astrophysics (Sect. 15. Numerical methods and codes)

GraphNeuralNetworks.jl is an open-source framework for deep learning on graphs, written in the Julia programming language. It supports multiple GPU backends, generic sparse or dense graph representations, and offers convenient interfaces for manipulating standard, heterogeneous, and temporal graphs with attributes at the node, edge, and graph levels. The framework allows users to define custom graph convolutional layers using gather/scatter message-passing primitives or optimized fused operations. It also includes several popular layers, enabling efficient experimentation with complex deep architectures. The package is available on GitHub: \url{https://github.com/JuliaGraphs/GraphNeuralNetworks.jl}., Comment: Submitted to JMLR OSS

Despite calls to modernize education preparation, the way we train, support, and grow educators has remained largely unchanged for decades. But some educator training and professional development organizations are taking a different approach by offering educators flexible and job-embedded learning opportunities that recognize and validate learning through demonstrations of competence. Educators earn "micro-credentials" in the form of digital badges, which capture both the skill the educator demonstrated and the evidence they used to prove their mastery of that skill. This case study offers a look at one micro-credential program, developed by UCLA's ExcEL Leadership Academy that has been approved for ESOL teacher certification in Rhode Island. The program offers a progression of 12 micro-credentials focused on the skills and competencies educators need to serve multilingual learners (MLLs) effectively. Additionally, the case study offers recommendations for other states that hope to offer their educators high-quality competency-based pathways to certification and/or professional growth.

Diabetes, particularly Type 2 diabetes (T2D), poses a substantial global health burden, compounded by its associated complications such as cardiovascular diseases, kidney failure, and vision impairment. Early detection of T2D is critical for improving healthcare outcomes and optimizing resource allocation. In this study, we address the gap in early T2D detection by leveraging machine learning (ML) techniques on gene expression data obtained from T2D patients. Our primary objective was to enhance the accuracy of early T2D detection through advanced ML methodologies and increase the model's trustworthiness using the explainable artificial intelligence (XAI) technique. Analyzing the biological mechanisms underlying T2D through gene expression datasets represents a novel research frontier, relatively less explored in previous studies. While numerous investigations have focused on utilizing clinical and demographic data for T2D prediction, the integration of molecular insights from gene expression datasets offers a unique and promising avenue for understanding the pathophysiology of the disease. By employing six ML classifiers on data sourced from NCBI's Gene Expression Omnibus (GEO), we observed promising performance across all models. Notably, the XGBoost classifier exhibited the highest accuracy, achieving 97%. Our study addresses a notable gap in early T2D detection methodologies, emphasizing the importance of leveraging gene expression data and advanced ML techniques., Comment: 8 pages

Recent advances in Large Language Models (LLMs) have helped facilitate exciting progress for robotic planning in real, open-world environments. 3D scene graphs (3DSGs) offer a promising environment representation for grounding such LLM-based planners as they are compact and semantically rich. However, as the robot's environment scales (e.g., number of entities tracked) and the complexity of scene graph information increases (e.g., maintaining more attributes), providing the 3DSG as-is to an LLM-based planner quickly becomes infeasible due to input token count limits and attentional biases present in LLMs. Inspired by the successes of Retrieval-Augmented Generation (RAG) methods that retrieve query-relevant document chunks for LLM question and answering, we adapt the paradigm for our embodied domain. Specifically, we propose a 3D scene subgraph retrieval framework, called EmbodiedRAG, that we augment an LLM-based planner with for executing natural language robotic tasks. Notably, our retrieved subgraphs adapt to changes in the environment as well as changes in task-relevancy as the robot executes its plan. We demonstrate EmbodiedRAG's ability to significantly reduce input token counts (by an order of magnitude) and planning time (up to 70% reduction in average time per planning step) while improving success rates on AI2Thor simulated household tasks with a single-arm, mobile manipulator. Additionally, we implement EmbodiedRAG on a quadruped with a manipulator to highlight the performance benefits for robot deployment at the edge in real environments.

A B-mode polarization signal in the cosmic microwave background (CMB) is widely regarded as smoking gun evidence for gravitational waves produced during inflation. Here we demonstrate that tensor perturbations from a cosmological phase transition can produce a B-mode signal whose strength rivals that of testable inflationary predictions across a range of observable scales. Although phase transitions arise from causal sub-horizon physics, they nevertheless exhibit a white noise power spectrum on super-horizon scales. Power is suppressed on the large scales relevant for CMB B-mode polarization, but it is not necessarily negligible. For appropriately chosen phase transition parameters, the maximal B-mode amplitude can compete with inflationary predictions that can be tested with current and future experiments. These scenarios can be differentiated by performing measurements on multiple angular scales, since the phase transition signal predicts peak power on smaller scales., Comment: 13 pages, 5 figures; v2: corrected technical details relating to super-horizon scaling, main results unchanged

Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims at developing models that can process instances as they arrive, so models constantly adapt to new concepts and the temporal evolution in the stream. In multi-label data stream environments where instances have the peculiarity of belonging simultaneously to more than one class, the problem becomes even more complex and poses unique challenges such as different concept drifts impacting different labels at simultaneous or distinct times, higher class imbalance, or new labels emerging in the stream. This paper proposes a novel approach to multi-label data stream classification called Multi-Label Hoeffding Adaptive Tree (MLHAT). MLHAT leverages the Hoeffding adaptive tree to address these challenges by considering possible relations and label co-occurrences in the partitioning process of the decision tree, dynamically adapting the learner in each leaf node of the tree, and implementing a concept drift detector that can quickly detect and replace tree branches that are no longer performing well. The proposed approach is compared with other 18 online multi-label classifiers on 41 datasets. The results, validated with statistical analysis, show that MLHAT outperforms other state-of-the-art approaches in 12 well-known multi-label metrics.

The Standard Model of particle physics does not explain the origin of the universe's baryon asymmetry or its primordial fluctuations. The Affleck-Dine mechanism is a well-motivated scenario for generating the baryon asymmetry through the post-inflationary dynamics of a complex scalar field with baryon number. The curvaton mechanism is a popular approach for producing curvature perturbations through the dynamics of a light spectator field which decays after inflation. We demonstrate that the same complex field can viably perform both roles without any modifications to the minimal realization of Affleck-Dine baryogenesis. This scenario can also accommodate appreciable levels of primordial non-Gaussianity, beyond those achievable with only a real-valued curvaton field, and may be observable with future CMB experiments., Comment: 6 pages, 2 figures

Robotic planning and execution in open-world environments is a complex problem due to the vast state spaces and high variability of task embodiment. Recent advances in perception algorithms, combined with Large Language Models (LLMs) for planning, offer promising solutions to these challenges, as the common sense reasoning capabilities of LLMs provide a strong heuristic for efficiently searching the action space. However, prior work fails to address the possibility of hallucinations from LLMs, which results in failures to execute the planned actions largely due to logical fallacies at high- or low-levels. To contend with automation failure due to such hallucinations, we introduce ConceptAgent, a natural language-driven robotic platform designed for task execution in unstructured environments. With a focus on scalability and reliability of LLM-based planning in complex state and action spaces, we present innovations designed to limit these shortcomings, including 1) Predicate Grounding to prevent and recover from infeasible actions, and 2) an embodied version of LLM-guided Monte Carlo Tree Search with self reflection. In simulation experiments, ConceptAgent achieved a 19% task completion rate across three room layouts and 30 easy level embodied tasks outperforming other state-of-the-art LLM-driven reasoning baselines that scored 10.26% and 8.11% on the same benchmark. Additionally, ablation studies on moderate to hard embodied tasks revealed a 20% increase in task completion from the baseline agent to the fully enhanced ConceptAgent, highlighting the individual and combined contributions of Predicate Grounding and LLM-guided Tree Search to enable more robust automation in complex state and action spaces.

Spiking Neural Networks (SNNs) and neuromorphic computing offer bio-inspired advantages such as sparsity and ultra-low power consumption, providing a promising alternative to conventional networks. However, training deep SNNs from scratch remains a challenge, as SNNs process and transmit information by quantizing the real-valued membrane potentials into binary spikes. This can lead to information loss and vanishing spikes in deeper layers, impeding effective training. While weight initialization is known to be critical for training deep neural networks, what constitutes an effective initial state for a deep SNN is not well-understood. Existing weight initialization methods designed for conventional networks (ANNs) are often applied to SNNs without accounting for their distinct computational properties. In this work we derive an optimal weight initialization method specifically tailored for SNNs, taking into account the quantization operation. We show theoretically that, unlike standard approaches, this method enables the propagation of activity in deep SNNs without loss of spikes. We demonstrate this behavior in numerical simulations of SNNs with up to 100 layers across multiple time steps. We present an in-depth analysis of the numerical conditions, regarding layer width and neuron hyperparameters, which are necessary to accurately apply our theoretical findings. Furthermore, our experiments on MNIST demonstrate higher accuracy and faster convergence when using the proposed weight initialization scheme. Finally, we show that the newly introduced weight initialization is robust against variations in several network and neuron hyperparameters.

Neutral sodium (Na I) is an alkali metal with a favorable absorption cross section such that tenuous gases are easily illuminated at select transiting exoplanet systems. We examine both the time-averaged and time-series alkali spectral flux individually, over 4 nights at a hot Saturn system on a $\sim$ 2.8 day orbit about a Sun-like star WASP-49 A. Very Large Telescope/ESPRESSO observations are analyzed, providing new constraints. We recover the previously confirmed residual sodium flux uniquely when averaged, whereas night-to-night Na I varies by more than an order of magnitude. On HARPS/3.6-m Epoch II, we report a Doppler redshift at $v_{ \Gamma, \mathrm{NaD}} =$ +9.7 $\pm$ 1.6 km/s with respect to the planet's rest frame. Upon examining the lightcurves, we confirm night-to-night variability, on the order of $\sim$ 1-4 % in NaD rarely coinciding with exoplanet transit, not readily explained by stellar activity, starspots, tellurics, or the interstellar medium. Coincident with the $\sim$+10 km/s Doppler redshift, we detect a transient sodium absorption event dF$_{\mathrm{NaD}}$/F$_{\star}$ = 3.6 $\pm$ 1 % at a relative difference of $\Delta F_{\mathrm{NaD}} (t) \sim$ 4.4 $\pm$ 1 %, enduring $\Delta t_{\mathrm{NaD}} \gtrsim$ 40 minutes. Since exoplanetary alkali signatures are blueshifted due to the natural vector of radiation pressure, estimated here at roughly $\sim$ -5.7 km/s, the radial velocity is rather at +15.4 km/s, far larger than any known exoplanet system. Given that the redshift magnitude v$_{\Gamma}$ is in between the Roche limit and dynamically stable satellite orbits, the transient sodium may be a putative indication of a natural satellite orbiting WASP-49 A b., Comment: Accepted to ApJ Letters (2024 August 2)

High-resolution spectroscopic characterization of young super-Jovian planets enables precise constraints on elemental and isotopic abundances of their atmospheres. As part of the ESO SupJup Survey, we present high-resolution spectral observations of two wide-orbit super-Jupiters in YSES 1 (or TYC 8998-760-1) using the upgraded VLT/CRIRES+ (R~100,000) in K-band. We carry out free atmospheric retrieval analyses to constrain chemical and isotopic abundances, temperature structures, rotation velocities, and radial velocities. We confirm the previous detection of 13CO in YSES 1 b at a higher significance of 12.6{\sigma}, but point to a higher 12CO/13CO ratio of 88+/-13 (1{\sigma} confidence interval), consistent with the primary's isotope ratio 66+/-5. We retrieve a solar-like composition in YSES 1 b with a C/O=0.57+/-0.01, indicating a formation via gravitational instability or core accretion beyond the CO iceline. Additionally, the observations lead to detections of H2O and CO in the outer planet YSES 1 c at 7.3{\sigma} and 5.7{\sigma}, respectively. We constrain the atmospheric C/O ratio of YSES 1 c to be either solar or subsolar (C/O=0.36+/-0.15), indicating the accretion of oxygen-rich solids. The two companions have distinct vsini, 5.34+/-0.14 km/s for YSES 1 b and 11.3+/-2.1 km/s for YSES 1 c, despite their similar natal environments. This may indicate different spin axis inclinations or effective magnetic braking by the long-lived circumplanetary disk around YSES 1 b. YSES 1 represents an intriguing system for comparative studies of super-Jovian companions and linking present atmospheres to formation histories., Comment: 25 pages, 11 figure, accepted for publication in AJ. The extracted CRIRES+ spectra of the YSES-1 system can be found at https://doi.org/10.5281/zenodo.13664032

Clustering algorithms rely on complex optimisation processes that may be difficult to comprehend, especially for individuals who lack technical expertise. While many explainable artificial intelligence techniques exist for supervised machine learning, unsupervised learning -- and clustering in particular -- has been largely neglected. To complicate matters further, the notion of a ``true'' cluster is inherently challenging to define. These facets of unsupervised learning and its explainability make it difficult to foster trust in such methods and curtail their adoption. To address these challenges, we propose a new, model-agnostic technique for explaining clustering algorithms with counterfactual statements. Our approach relies on a novel soft-scoring method that captures the spatial information utilised by clustering models. It builds upon a state-of-the-art Bayesian counterfactual generator for supervised learning to deliver high-quality explanations. We evaluate its performance on five datasets and two clustering algorithms, and demonstrate that introducing soft scores to guide counterfactual search significantly improves the results.

Coastal marine ecosystems face mounting pressures from anthropogenic activities and climate change, necessitating advanced monitoring and modeling approaches for effective management. This paper pioneers the development of a Marine Digital Twin Platform aimed at modeling the Mar Menor Coastal Lagoon Ecosystem in the Region of Murcia. The platform leverages Artificial Intelligence to emulate complex hydrological and ecological models, facilitating the simulation of what-if scenarios to predict ecosystem responses to various stressors. We integrate diverse datasets from public sources to construct a comprehensive digital representation of the lagoon's dynamics. The platform's modular design enables real-time stakeholder engagement and informed decision-making in marine management. Our work contributes to the ongoing discourse on advancing marine science through innovative digital twin technologies.

Large Language Models (LLMs) are transforming writing, reading, teaching, and knowledge retrieval in many academic fields. However, concerns regarding their misuse and erroneous outputs have led to varying degrees of trust in LLMs within academic communities. In response, various academic organizations have proposed and adopted policies regulating their usage. However, these policies are not based on substantial quantitative evidence because there is no data about use patterns and user opinion. Consequently, there is a pressing need to accurately quantify their usage, user trust in outputs, and concerns about key issues to prioritize in deployment. This study addresses these gaps through a quantitative user study of LLM usage and trust in academic research and education. Specifically, our study surveyed 125 individuals at a private R1 research university regarding their usage of LLMs, their trust in LLM outputs, and key issues to prioritize for robust usage in academia. Our findings reveal: (1) widespread adoption of LLMs, with 75% of respondents actively using them; (2) a significant positive correlation between trust and adoption, as well as between engagement and trust; and (3) that fact-checking is the most critical concern. These findings suggest a need for policies that address pervasive usage, prioritize fact-checking mechanisms, and accurately calibrate user trust levels as they engage with these models. These strategies can help balance innovation with accountability and help integrate LLMs into the academic environment effectively and reliably.

The X-ray Integral Field Unit (X-IFU) is an instrument of ESA's future NewAthena space observatory, with the goal to provide high-energy resolution ($<$ 4 eV at X-ray energies up to 7 keV) and high-spatial resolution (9") spectroscopic imaging over the X-ray energy range from 200 eV to 12 keV, by means of an array of about 1500 transition-edge sensors (TES) read out via SQUID time-division multiplexing (TDM). A TDM-based laboratory test-bed has been assembled at SRON, hosting an array of $75\times 75\ \upmu$m$^2$ TESs that are read out via 2-column $\times$ 32-row TDM. A system component that is critical to high-performance operation is the wiring harness that connects the room-temperature electronics to the cryogenic readout componentry. We report here on our characterization of such a test-bed, whose harness has a length close to what envisioned for X-IFU, which allowed to achieve a co-added energy resolution at a level of 2.7~eV FWHM at 6~keV via 32-row readout. In addition, we provide an outlook on the integration of TDM readout into the X-IFU Focal-Plane Assembly Development Model., Comment: Submitted for publication to Journal of Astronomical Telescopes, Instrumentation and Systems. arXiv admin note: text overlap with arXiv:2403.02978

Due to the unprecedented signal strengths offered by the newest high-resolution spectrographs on 10-m class telescopes, exploring the 3D nature of exoplanets is possible with an unprecedented level of precision. In this paper, we present a new technique to probe the vertical structure of exoplanetary winds and dynamics using ensembles of planet absorption lines of varying opacity, and apply it to the well-studied ultra-hot Jupiter WASP-76b. We then compare these results to state-of-the-art global circulation models (GCMs) with varying magnetic drag prescriptions. We find that the known asymmetric velocity shift in Fe I absorption during transit persists at all altitudes, and observe tentative trends for stronger blueshifts and more narrow line profiles deeper in the atmosphere. By comparing three different model prescriptions (a hydrodynamical model with no drag, a magnetic drag model, and a uniform drag model) we are able to rule out the uniform drag model due to inconsistencies with observed trends in the data. We find that the magnetic model is slightly favored over the the hydrodynamic model, and note that this 3-Gauss kinematic magnetohydrodynamical GCM is also favored when compared to low-resolution data. Future generation high-resolution spectrographs on Extremely large telescopes (ELTs) will greatly increase signals and make methods like these possible with higher precision and for a wider range of objects., Comment: 20 pages, 10 figures, accepted to ApJ

The Aurora Institute has released state policy recommendations to enable our education system to transition from an industrial age "one-size-fits-all" model, to a future-focused model that supports education innovation, student agency, builds knowledge, and prioritizes mastery of skills and knowledge over seat time. The six identified policy shifts must occur to transform education for all learners--especially those who have been underserved by the current system. The priorities include: (1) establish a vision by developing a Profile of a Graduate; (2) create the conditions for equitable learner-centered, competency-based education systems; (3) transform systems of assessments; (4) align accountability and data system; (5) support educators to thrive in a competency-based system; and (6) redesign learning experiences.

Calorimetric determination of enthalpies of mixing ($\Delta$H$_{\rm mix}$) of multicomponent molten salts often employs empirical models that lack parameters with clear physical interpretation (e.g., coordination numbers, molar volumes, and pair potentials). Although such physics informed models are not always needed, a thermodynamic understanding of the relationships between excess energies of mixing and local to intermediate solvation structures is particularly important for pyrochemical separation, as is the case for lanthanides (Ln), which are common neutron poisons and critical industrial elements found in spent nuclear fuels. Here we implement the molecular interaction volume model (MIVM) to synthesize information from experimentally measured $\Delta$H$_{\rm mix}$ (using high temperature melt drop calorimetry) and the distribution of solvation structures from ab initio molecular dynamics (AIMD) simulations. This was demonstrated by a case study of molten salt system consisted of LaCl$_3$ mixing with a eutectic LiCl-KCl (58mol% to 42mol%) at 873 K and 1133 K. The parameters modelled from MIVM were used to extrapolate excess Gibbs energy ($\Delta$G$_{\rm mix}$), and compositional dependence of La$^{3+}$ activity in the LaCl$_3$-(LiCl-KCl) system. In contrast, by AIMD or polarizable ion model (PIM) simulations, a significant deviation regarding the predicted $\Delta$H$_{\rm mix}$ was seen if computed directly from the molecular dynamic trajectories. The integrated experimental and simulation data within the MIVM formalism are generalizable to a wide variety of molten salts and demonstrate a significant improvement over currently employed methods to study molten salts for nuclear and separations sciences.

The energy surplus resulting from radiative forcing causes warming of the Earth system. This initial warming drives a myriad of changes including in sea surface temperatures (SSTs), leading to different radiative feedbacks. The relationship between the radiative feedbacks and the pattern of SST changes is referred to as the "pattern effect". The current approach to study the pattern effect relies on diagnosing the response of atmosphere-only models to perturbations in the SST boundary condition. Here, we argue that the fluctuation-dissipation relation (FDR), together with coarse-graining procedures, is a computationally cheap and theoretically grounded alternative to model experiments. We introduce a protocol to study the pattern effect and present its application in a state-of-the-art coupled climate model. By focusing on the coupled dynamics, we unveil the role of the slow ocean component in setting the pattern effect. We present a new "sensitivity map", representing a first, qualitative prediction of the response of the average top-of-the-atmosphere (TOA) radiative flux to perturbations in the SST field. We find negative sensitivity throughout the tropics, in contrast to the current understanding of a positive-negative dipole of sensitivity in the tropical Pacific. Considering only the shortest time scales, the response is dominated by the fast atmospheric variability and we recover results in qualitative agreement with the literature. Therefore, the difference between our results and previous studies, largely comes from including the atmosphere-ocean coupling. The framework offers a conceptually novel perspective on the pattern effect: feedbacks in the coupled system are encoded in a temporally and spatially dependent response operator, rather than time-independent maps as for previous studies.

Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances this capability by integrating diverse data types such as imaging, textual data, and genetic information, leading to more robust and accurate predictive models. In MDL, differently from early and late fusion methods, intermediate fusion stands out for its ability to effectively combine modality-specific features during the learning process. This systematic review aims to comprehensively analyze and formalize current intermediate fusion methods in biomedical applications. We investigate the techniques employed, the challenges faced, and potential future directions for advancing intermediate fusion methods. Additionally, we introduce a structured notation to enhance the understanding and application of these methods beyond the biomedical domain. Our findings are intended to support researchers, healthcare professionals, and the broader deep learning community in developing more sophisticated and insightful multimodal models. Through this review, we aim to provide a foundational framework for future research and practical applications in the dynamic field of MDL.

We introduce the OATMEAL survey, an effort to measure the obliquities of stars with transiting brown dwarf companions. We observed a transit of the close-in ($P_{\rm orb} = 1.74 \,$ days) brown dwarf GPX-1 b using the Keck Planet Finder (KPF) spectrograph to measure the sky-projected angle between its orbital axis and the spin axis of its early F-type host star ($\lambda$). We measured $\lambda = 6.88 \pm 1.72 ^\circ$ (with additional unquantified systematic uncertainty), suggesting an orbit that is prograde and well aligned with the stellar equator. Hot Jupiters around early F stars are frequently found to have highly misaligned orbits, with polar and retrograde orbits being commonplace. It has been theorized that these misalignments stem from dynamical interactions, such as von Zeipel-Kozai-Lidov cycles, and are retained over long timescales due to weak tidal dissipation in stars with radiative envelopes. By comparing GPX-1 to similar systems under the frameworks of different tidal evolution theories, we argued that the rate of tidal dissipation is too slow to have re-aligned the system. This suggests that GPX-1 may have arrived at its close-in orbit via coplanar high-eccentricity migration or migration through an aligned protoplanetary disk. Our result for GPX-1 is one of few measurements of the obliquity of a star with a transiting brown dwarf. By enlarging the number of such measurements and comparing them with hot Jupiter systems, we will more clearly discern the differences between the mechanisms that dictate the formation and evolution of both classes of objects., Comment: 8 pages, 4 figures

Irradiations at Ultra High Dose Rate (UHDR) regimes, exceeding 40 Gy/s in single fractions lasting less than 200 ms, have shown an equivalent antitumor effect compared to conventional radio-therapy with reduced harm to normal tissues. This work details the hardware and software modi-fications implemented to deliver 10 MeV UHDR electron beams with a Linear Accelerator Elekta SL 18 MV and the beam characteristics obtained. GafChromic EBT XD films and an Advanced Markus chamber were used for the dosimetry characterization, while a silicon sensor assessed the machine's beam pulses stability and repeatability. Dose per pulse, average dose rate and instantaneous dose rate in the pulse were evaluated for four experimental settings, varying the source-to-surface dis-tance and the beam collimation, i.e. with and without the use of a cylindrical applicator. Results showed dose per pulse from 0.6 Gy to a few tens of Gy and average dose rate up to 300 Gy/s. The obtained results demonstrate the possibility to perform in-vitro radiobiology experiments and test of new technologies for beam monitoring and dosimetry at the upgraded LINAC, thus contributing to the electron UHDR research field.

The use of AI technologies is percolating into the secure development of software-based systems, with an increasing trend of composing AI-based subsystems (with uncertain levels of performance) into automated pipelines. This presents a fundamental research challenge and poses a serious threat to safety-critical domains (e.g., aviation). Despite the existing knowledge about uncertainty in risk analysis, no previous work has estimated the uncertainty of AI-augmented systems given the propagation of errors in the pipeline. We provide the formal underpinnings for capturing uncertainty propagation, develop a simulator to quantify uncertainty, and evaluate the simulation of propagating errors with two case studies. We discuss the generalizability of our approach and present policy implications and recommendations for aviation. Future work includes extending the approach and investigating the required metrics for validation in the aviation domain.

Submillimeter and millimeter wavelengths provide a unique view of the Universe, from the gas and dust that fills and surrounds galaxies to the chromosphere of our own Sun. Current single-dish facilities have presented a tantalising view of the brightest (sub-)mm sources, and interferometers have provided the exquisite resolution necessary to analyse the details in small fields, but there are still many open questions that cannot be answered with current facilities. In this report we summarise the science that is guiding the design of the Atacama Large Aperture Submillimeter Telescope (AtLAST). We demonstrate how tranformational advances in topics including star formation in high redshift galaxies, the diffuse circumgalactic medium, Galactic ecology, cometary compositions and solar flares motivate the need for a 50m, single-dish telescope with a 1-2 degree field of view and a new generation of highly multiplexed continuum and spectral cameras. AtLAST will have the resolution to drastically lower the confusion limit compared to current single-dish facilities, whilst also being able to rapidly map large areas of the sky and detect extended, diffuse structures. Its high sensitivity and large field of view will open up the field of submillimeter transient science by increasing the probability of serendipitous detections. Finally, the science cases listed here motivate the need for a highly flexible operations model capable of short observations of individual targets, large surveys, monitoring programmes, target of opportunity observations and coordinated observations with other observatories. AtLAST aims to be a sustainable, upgradeable, multipurpose facility that will deliver orders of magnitude increases in sensitivity and mapping speeds over current and planned submillimeter observatories., Comment: 47 pages, 12 figures. For further details on AtLAST see https://atlast.uio.no

Recent observations of two nearby black hole low-mass X-ray binaries have suggested possible indication of dark matter density spikes. While the evidence is compelling, one issue with this interpretation is that light black holes formed from stellar collapse are not expected to form dark matter spikes, and so it is unclear how the stellar-mass black holes in these binaries could have acquired such features. Since primordial black holes are expected to form ultra-dense dark matter mini-spikes, in this article we explore the possibility of a primordial origin for these light black holes., Comment: 11 pages, 1 figure; matches published version

In this position paper we address the Software Sustainability from the IN perspective, so that the Software Engineering (SE) community is aware of the need to contribute towards sustainable software companies, which need to adopt a holistic approach to sustainability considering all its dimensions (human, economic and environmental). A series of important challenges to be considered in the coming years are presented, in order that advances in involved SE communities on the subject can be harmonised and used to contribute more effectively to this field of great interest and impact on society.

To facilitate the use and portability of Lab on a chip technology, it is desirable to avoid the use of bulky electronic systems for flow control. Developed self-powered microsystems typically move only small volumes of fluid performing up to one or two hours. We have previously shown that polymeric micropumps combined with plastic microfluidic cartridges constitute a universal self-powered modular microfluidic architecture suitable for moving large volumes of fluids in short times. Herein, we show that polymeric micropumps can provide self-powered flow control for long periods of time in the range of hours and days. The calibration curves of various types of micropumps were obtained including one that maintained the movement of the fluid for 23 hours, advancing the fluid front up to 1.8 meters through a channel with a section of 0.127 mm2. We found that the actuation time of the pump was related to degassing time, the effective surface area and the air recovery rate of the pump. This is the first example of a long operating self-powered microsystem, which may have a great impact in those cases where controlled flow is needed for a long period of time and the use of electronic equipment is not desirable., Comment: 19 pages, 6 figures

Recent conversations in the algorithmic fairness literature have raised several concerns with standard conceptions of fairness. First, constraining predictive algorithms to satisfy fairness benchmarks may lead to non-optimal outcomes for disadvantaged groups. Second, technical interventions are often ineffective by themselves, especially when divorced from an understanding of structural processes that generate social inequality. Inspired by both these critiques, we construct a common decision-making model, using mortgage loans as a running example. We show that under some conditions, any choice of decision threshold will inevitably perpetuate existing disparities in financial stability unless one deviates from the Pareto optimal policy. Then, we model the effects of three different types of interventions. We show how different interventions are recommended depending upon the difficulty of enacting structural change upon external parameters and depending upon the policymaker's preferences for equity or efficiency. Counterintuitively, we demonstrate that preferences for efficiency over equity may lead to recommendations for interventions that target the under-resourced group. Finally, we simulate the effects of interventions on a dataset that combines HMDA and Fannie Mae loan data. This research highlights the ways that structural inequality can be perpetuated by seemingly unbiased decision mechanisms, and it shows that in many situations, technical solutions must be paired with external, context-aware interventions to enact social change.