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Assigning upper elementary grade teachers to teach their strongest subjects to multiple classes ("departmentalizing"), rather than teaching all subjects to a single class, ("self-contained instruction") could mean more specialized instructional expertise in the classroom or focus for teacher planning time and professional development. This study examined the experiences of 90 schools that either voluntarily switched to departmentalized instruction for up to two years or continued with self-contained teaching in 4th and 5th grade classes beginning in fall 2019. The findings show that schools that switched were able to implement the key steps needed to departmentalize instruction but struggled with aspects of the approach. Teachers' reported experiences were consistent with some of both the benefits and challenges that prior research had hypothesized. Although the pandemic significantly disrupted instruction and the study activities, school and teacher experiences during this challenging time may inform schools and districts considering adopting departmentalized instruction.

This appendix is a companion to the report "Evaluation of Departmentalized Instruction in Elementary Schools: Exploring Implementation Experiences." The appendix provides additional information about the study, which sought to explore schools' implementation of departmentalized instruction, teachers' experiences with the strategy, and how the strategy was related to key outcomes. This additional information includes details on departmentalized instruction (Section A), study design and execution (Section B), key findings presented in the report (Section C), and additional findings that are not included in the report but provide important context (Section D).

The "Report on the Condition of Education" is an annual report from the National Center for Education Statistics (NCES) that is mandated by the United States Congress. Using data from NCES and other sources, NCES compiles a set of "indicators" of the condition of education in the United States at all levels, from prekindergarten through postsecondary, as well as labor force outcomes and international comparisons. The full contents of the Condition of Education Indicator System can be accessed online or by downloading PDFs for the individual indicators. The "Report on the Condition of Education 2024" comprises key findings from the Indicator System. This summary report provides a brief overview of information available on various topics as well as direct links to the online versions of indicators discussed. This year's report also includes select content from sources outside the Indicator System, such as the National Assessment of Educational Progress (NAEP) and the Integrated Postsecondary Education System (IPEDS).

Active galactic nucleus (AGN) growth in disk-dominated, merger-free galaxies is poorly understood, largely due to the difficulty in disentangling the AGN emission from that of the host galaxy. By carefully separating this emission, we examine the differences between AGNs in galaxies hosting a (possibly) merger-grown, classical bulge, and AGNs in secularly grown, truly bulgeless disk galaxies. We use GALFIT to obtain robust, accurate morphologies of 100 disk-dominated galaxies imaged with the Hubble Space Telescope. Adopting an inclusive definition of classical bulges, we detect a classical bulge component in $53.3 \pm 0.5$ per cent of the galaxies. These bulges were not visible in Sloan Digital Sky Survey photometry, however these galaxies are still unambiguously disk-dominated, with an average bulge-to-total luminosity ratio of $0.1 \pm 0.1$. We find some correlation between bulge mass and black hole mass for disk-dominated galaxies, though this correlation is significantly weaker in comparison to the relation for bulge-dominated or elliptical galaxies. Furthermore, a significant fraction ($\gtrsim 90$ per cent) of our black holes are overly massive when compared to the relationship for elliptical galaxies. We find a weak correlation between total stellar mass and black hole mass for the disk-dominated galaxies, hinting that the stochasticity of black hole-galaxy co-evolution may be higher disk-dominated than bulge-dominated systems., Comment: 14 pages, 6 figures, 2 tables. Submitted to MNRAS

Cerebrovascular diseases (CVD) can lead to stroke and dementia. Stroke is the second leading cause of death world wide and dementia incidence is increasing by the year. There are several markers of CVD that are visible on brain imaging, including: white matter hyperintensities (WMH), acute and chronic ischaemic stroke lesions (ISL), lacunes, enlarged perivascular spaces (PVS), acute and chronic haemorrhagic lesions, and cerebral microbleeds (CMB). Brain atrophy also occurs in CVD. These markers are important for patient management and intervention, since they indicate elevated risk of future stroke and dementia. We systematically reviewed automated systems designed to support radiologists reporting on these CVD imaging findings. We considered commercially available software and research publications which identify at least two CVD markers. In total, we included 29 commercial products and 13 research publications. Two distinct types of commercial support system were available: those which identify acute stroke lesions (haemorrhagic and ischaemic) from computed tomography (CT) scans, mainly for the purpose of patient triage; and those which measure WMH and atrophy regionally and longitudinally. In research, WMH and ISL were the markers most frequently analysed together, from magnetic resonance imaging (MRI) scans; lacunes and PVS were each targeted only twice and CMB only once. For stroke, commercially available systems largely support the emergency setting, whilst research systems consider also follow-up and routine scans. The systems to quantify WMH and atrophy are focused on neurodegenerative disease support, where these CVD markers are also of significance. There are currently no openly validated systems, commercially, or in research, performing a comprehensive joint analysis of all CVD markers (WMH, ISL, lacunes, PVS, haemorrhagic lesions, CMB, and atrophy)., Comment: 62 pages, 10 figures

Localized surface plasmons (LSPs) are collective oscillations of free electrons in metal nanoparticles that confine electromagnetic waves into subwavelength regions, making them an ideal platform for light-matter coupling. To design and understand plasmonic structures, numerical computations of Maxwell's equations are commonly used. However, obtaining physical insight from these numerical solutions can be challenging, especially for complex-shaped nanoparticles. To circumvent this, we introduce mode decomposition strategies within the boundary element method (BEM). By employing singular value decomposition (SVD) and quasi-normal mode (QNM) decomposition, we break down optical responses into elementary modes. QNMs offer deeper insights into frequency and damping, while SVD modes allow for more accurate spectral reconstruction with fast computation. These techniques provide a deeper understanding of LSPs and facilitates the design of metal nanoparticles for efficient light-matter interaction.

Supernova remnants (SNRs) remain prime candidates for hadronic particle acceleration within our galaxy, accounting for much of the Cosmic Ray flux. Next-generation instruments such as the Southern Wide-field Gamma-ray Observatory (SWGO) will be of crucial importance in identifying new candidate SNRs. SWGO will observe two-thirds of the gamma-ray sky, covering the energy range between a few hundreds GeV and a PeV. In this work, we apply a model of SNR evolution to predict their gamma-ray spectra. Furthermore, we use our model in combination with the target SWGO sensitivity range to explore the SNR emission phase space and quantify detection prospects for SWGO. Finally, we validate our model for sources observed with current-generation instruments, fitting it using a Monte-Carlo Markov Chain technique to the observed gamma-ray emission from four SNRs. We anticipate that at least 8 SNRs will be detected by SWGO within 1 year., Comment: 24 pages, 16 figures. Submitted to JCAP

Collecting high-quality translations is crucial for the development and evaluation of machine translation systems. However, traditional human-only approaches are costly and slow. This study presents a comprehensive investigation of 11 approaches for acquiring translation data, including human-only, machineonly, and hybrid approaches. Our findings demonstrate that human-machine collaboration can match or even exceed the quality of human-only translations, while being more cost-efficient. Error analysis reveals the complementary strengths between human and machine contributions, highlighting the effectiveness of collaborative methods. Cost analysis further demonstrates the economic benefits of human-machine collaboration methods, with some approaches achieving top-tier quality at around 60% of the cost of traditional methods. We release a publicly available dataset containing nearly 18,000 segments of varying translation quality with corresponding human ratings to facilitate future research.

In this paper we establish combinatorial characterisations of symmetry-generic infinitesimally rigid frameworks in the Euclidean plane for rotational groups of order 4 and 6, and of odd order between 5 and 1000, where a joint may lie at the centre of rotation. This extends the corresponding results for these groups in the free action case obtained by R. Ikeshita and S. Tanigawa in 2015, and our recent results for the reflection group and the rotational groups of order 2 and 3 in the non-free action case. The characterisations are given in terms of sparsity counts on the corresponding group-labelled quotient graphs, and are obtained via symmetry-adapted versions of recursive Henneberg-type graph constructions. For rotational groups of even order at least 8, we show that the sparsity counts alone are not sufficient for symmetry-generic infinitesimal rigidity., Comment: 25 pages (excluding references), 7 figures

We study the evolution of populations of binary stars within massive cluster-forming regions. We simulate the formation of young massive star clusters within giant molecular clouds with masses ranging from 2 x 10$^{4}$ to 3.2 x 10$^{5}$ M$_{\odot}$. We use Torch, which couples stellar dynamics, magnetohydrodynamics, star and binary formation, stellar evolution, and stellar feedback through the AMUSE framework. We find that the binary fraction decreases during cluster formation at all molecular cloud masses. The binaries' orbital properties also change, with stronger and quicker changes in denser, more massive clouds. Most of the changes we see can be attributed to the disruption of binaries wider than 100 au, although the close binary fraction also decreases in the densest cluster-forming region. The binary fraction for O stars remains above 90%, but exchanges and dynamical hardening are ubiquitous, indicating that O stars undergo frequent few-body interactions early during the cluster formation process. Changes to the populations of binaries are a by-product of hierarchical cluster assembly: most changes to the binary population take place when the star formation rate is high and there are frequent mergers between sub-clusters in the cluster-forming region. A universal primordial binary distribution based on observed inner companions in the Galactic field is consistent with the binary populations of young clusters with resolved stellar populations, and the scatter between clusters of similar masses could be explained by differences in their formation history., Comment: 15 pages, 8 figures, 3 tables. Submitted to ApJ

The growing volume of data produced by large astronomical surveys necessitates the development of efficient analysis techniques capable of effectively managing high-dimensional datasets. This study addresses this need by demonstrating some applications of manifold learning and dimensionality reduction techniques, specifically the Self-Organizing Map (SOM), on the optical+NIR SED space of galaxies, with a focus on sample comparison, selection biases, and predictive power using a small subset. To this end, we utilize a large photometric sample from the five CANDELS fields and a subset with spectroscopic measurements from the KECK MOSDEF survey in two redshift bins at $z\sim1.5$ and $z\sim2.2$. We trained SOM with the photometric data and mapped the spectroscopic data onto it as our study case. We found that MOSDEF targets do not cover all SED shapes existing in the SOM. Our findings reveal that Active Galactic Nuclei (AGN) within the MOSDEF sample are mapped onto the more massive regions of the SOM, confirming previous studies and known selection biases towards higher-mass, less dusty galaxies. Furthermore, SOM were utilized to map measured spectroscopic features, examining the relationship between metallicity variations and galaxy mass. Our analysis confirmed that more massive galaxies exhibit lower [OIII]/H$\beta$ and [OIII]/[OII] ratios and higher H$\alpha$/H$\beta$ ratios, consistent with the known mass-metallicity relation. These findings highlight the effectiveness of SOM in analyzing and visualizing complex, multi-dimensional datasets, emphasizing their potential in data-driven astronomical studies.

Polycyclic aromatic hydrocarbons (PAHs) are among the most ubiquitous compounds in the universe, accounting for up to ~25% of all interstellar carbon. Since most unsubstituted PAHs do not possess permanent dipole moments, they are invisible to radio astronomy. Constraining their abundances relies on the detection of polar chemical proxies, such as aromatic nitriles. We report the detection of 2- and 4-cyanopyrene, isomers of the recently detected 1-cyanopyrene. We find that these isomers are present in an abundance ratio of ~2:1:2, which mirrors the number of equivalent sites available for CN addition. We conclude that there is evidence that the cyanopyrene isomers formed by direct CN addition to pyrene under kinetic control in hydrogen-rich gas at 10 K and discuss constraints on the H/CN ratio for PAHs in TMC-1., Comment: Submitted version to comply with licensing agreements

Polycyclic aromatic hydrocarbons (PAHs) are expected to be the most abundant class of organic molecules in space. Their interstellar lifecycle is not well understood, and progress is hampered by difficulties detecting individual PAH molecules. Here, we present the discovery of CN-functionalized pyrene, a 4-ring PAH, in the dense cloud TMC-1 using the 100-m Green Bank Telescope. We derive an abundance of 1-cyanopyrene of ~1.52 x $10^{12}$ cm$^{-2}$, and from this estimate that the un-substituted pyrene accounts for up to ~0.03-0.3% of the carbon budget in the dense interstellar medium which trace the birth sites of stars and planets. The presence of pyrene in this cold (~10 K) molecular cloud agrees with its recent measurement in asteroid Ryugu where isotopic clumping suggest a cold, interstellar origin. The direct link to the birth site of our solar system is strengthened when we consider the solid state pyrene content in the pre-stellar materials compared to comets, which represent the most pristine material in the solar system. We estimate that solid state pyrene can account for 1% of the carbon within comets carried by this one single organic molecule. The abundance indicates pyrene is an "island of stability" in interstellar PAH chemistry and suggests a potential cold molecular cloud origin of the carbon carried by PAHs that is supplied to forming planetary systems, including habitable worlds such as our own., Comment: Version of manuscript revised to comply with licensing requirements

Increased healthcare demand is significantly straining European services. Digital solutions including advanced modelling techniques offer a promising solution to optimising patient flow without impacting day-to-day healthcare provision. In this work we outline an ongoing project that aims to optimise healthcare resources using agent-based simulations., Comment: 6 pages, 2 figures

Under high-intensity rail operations, rail tracks endure considerable stresses resulting in various defects such as corrugation and spellings. Failure to effectively detect defects and provide maintenance in time would compromise service reliability and public safety. While advanced models have been developed in recent years, efficiently identifying small-scale rail defects has not yet been studied, especially for categories such as Dirt or Squat on rail surface. To address this challenge, this study utilizes Swin Transformer (SwinT) as baseline and incorporates the Convolutional Block Attention Module (CBAM) for enhancement. Our proposed method integrates CBAM successively within the swin transformer blocks, resulting in significant performance improvement in rail defect detection, particularly for categories with small instance sizes. The proposed framework is named CBAM-Enhanced Swin Transformer in Block Level (CBAM-SwinT-BL). Experiment and ablation study have proven the effectiveness of the framework. The proposed framework has a notable improvement in the accuracy of small size defects, such as dirt and dent categories in RIII dataset, with mAP-50 increasing by +23.0% and +38.3% respectively, and the squat category in MUET dataset also reaches +13.2% higher than the original model. Compares to the original SwinT, CBAM-SwinT-BL increase overall precision around +5% in the MUET dataset and +7% in the RIII dataset, reaching 69.1% and 88.1% respectively. Meanwhile, the additional module CBAM merely extend the model training speed by an average of +0.04s/iteration, which is acceptable compared to the significant improvement in system performance., Comment: 27 pages, 17 figures

Blue straggler stars in stellar clusters are a subset of stars that are bluer and appear younger than other cluster members, seemingly straggling behind in their evolution. They offer a unique opportunity to understand the stellar dynamics and populations within their hosts. In the collisional formation scenario, a persistent challenge is the excessive angular momentum in the collision product. The consequent significant mass loss during transition to a stable state leads to a star with too low a mass to be a blue straggler, unless it spins down efficiently. While many proposed spin-down mechanisms involve boosted angular momentum loss via magnetic braking within the collision product, the existence or strength of these magnetic fields has not been confirmed. Here, we report three-dimensional magnetohydrodynamical simulations of collisions between two low-mass main-sequence stars and investigate magnetic field amplification. Magnetic field energy is amplified by a factor of $10^{8}-10^{10}$, resulting in the magnetic field strength of $10^{7}-10^{8}$G at the core of the collision product, independent of collision parameters. The surface magnetic field strengths have increased up to $10-10^{4}$ G. In addition, a distinctly flattened, rotating gas structure appears around the collision products in off-axis collisions, which may be a hint of possible disk formation. Such significant magnetic amplification and potential disk formation suggest the possibility of efficient spin-down of collision products via magnetic braking and magnetic disk locking, which can result in their appearance as blue stragglers., Comment: 27 pages, 10 figures, 1 table. Comments welcome! Videos: https://www.youtube.com/watch?v=lkpTEJTI1Ew&list=PLxLK3qI02cQef6wX5wi-QMSFyxBGK4tGL

Valvular heart disease accounts for up to 20% of cardiac surgery in the United States. Computer simulation of "virtual interventions" may inform optimal valve repair for a given patient prior to intervention. However, there is a paucity of methods to noninvasively determine in vivo mechanical properties of valve tissue from clinically acquired 3D images, limiting the accuracy of computer prediction and translational potential of in silico valve repairs. Here, we propose ADEPT, A noninvasive method for Determining Elastic Properties of valve Tissue, to overcome this methodological gap. Our framework combines image registration and physics-informed neural networks (PINNs) to estimate material properties of valve tissue from 3D echocardiograms (3DE). The PINN model was validated on a series of benchmarks before being applied to the 3DE of the tricuspid valve in a child with congenital heart disease. Our approach yielded accurate material parameter estimations in the examples accompanying this work.

We examine star-formation and dust properties for a sample of 660 galaxies at $1.37\leq z\leq 2.61$ in the MOSDEF survey by dividing them into groups with similarly-shaped spectral energy distributions (SEDs). For each group, we combine the galaxy photometry into a finely-sampled composite SED, and stack their spectra. This method enables the study of more complete galaxy samples, including galaxies with very faint emission lines. We fit these composite SEDs with Prospector to measure the stellar attenuation and SED-based star-formation rates (SFRs). We also derive emission-line properties from the spectral stacks, including Balmer decrements, dust-corrected SFRs, and metallicities. We find that stellar attenuation correlates most strongly with mass, while nebular attenuation correlates strongly with both mass and SFR. Furthermore, the excess of nebular compared to stellar attenuation correlates most strongly with SFR. The highest SFR group has 2 mag of excess nebular attenuation. Our results are consistent with a model in which star-forming regions become more dusty as galaxy mass increases. To explain the increasing excess nebular attenuation, we require a progressively larger fraction of star formation to occur in highly-obscured regions with increasing SFR. This highly-obscured star formation could occur in dusty clumps or central starbursts. Additionally, as each galaxy group represents a different evolutionary stage, we study their locations on the UVJ and SFR-mass diagrams. As mass increases, metallicity and dust attenuation increase, while sSFR decreases. However, the most massive group moves towards the quiescent region of the UVJ diagram, while showing less obscuration, potentially indicating removal of dust., Comment: 21 pages, 7 figures, accepted for publication in ApJ

We simulate mergers between star clusters embedded within their natal giant molecular cloud. We extract initial conditions from cloud-scale simulations of cluster formation and introduce different prescriptions for primordial binaries. We find that simulations that do not include primordial binaries result in a larger fraction of unbound stars than simulations which include a prescription for binaries based on observations. We also find a preferred direction of motion for stars that become unbound during the merger. Sub-cluster mergers within realistic gas environments promote binary disruption while mergers between idealized, gas-rich spherical clusters do not produce the same disruption. Binary systems with smaller semi-major axes are disrupted in simulations of sub-cluster mergers within their natal environment compared to simulations that do not include the realistic gas environment. We conclude that binary disruption and the production of an anisotropic distribution of unbound stars are the natural consequences of sub-cluster mergers during star cluster assembly., Comment: 13 pages, 8 figures. Re-submitted to ApJ following favourable referee report

As robotic systems become increasingly integrated into complex real-world environments, there is a growing need for approaches that enable robots to understand and act upon natural language instructions without relying on extensive pre-programmed knowledge of their surroundings. This paper presents PLATO, an innovative system that addresses this challenge by leveraging specialized large language model agents to process natural language inputs, understand the environment, predict tool affordances, and generate executable actions for robotic systems. Unlike traditional systems that depend on hard-coded environmental information, PLATO employs a modular architecture of specialized agents to operate without any initial knowledge of the environment. These agents identify objects and their locations within the scene, generate a comprehensive high-level plan, translate this plan into a series of low-level actions, and verify the completion of each step. The system is particularly tested on challenging tool-use tasks, which involve handling diverse objects and require long-horizon planning. PLATO's design allows it to adapt to dynamic and unstructured settings, significantly enhancing its flexibility and robustness. By evaluating the system across various complex scenarios, we demonstrate its capability to tackle a diverse range of tasks and offer a novel solution to integrate LLMs with robotic platforms, advancing the state-of-the-art in autonomous robotic task execution. For videos and prompt details, please see our project website: https://sites.google.com/andrew.cmu.edu/plato, Comment: 7 pages, 4 figures, submitted to ICRA 2025

Characterising drivers of SARS-CoV-2 circulation is crucial for understanding COVID-19 because of the severity of control measures adopted during the pandemic. Whole genome sequence data augmented with demographic metadata provides the best opportunity to do this. We use Random Forest Decision Tree models to analyse a combination of over 4000 SARS-CoV2 sequences from a densely sampled, mixed urban and rural population (Tayside) in Scotland in the period from August 2020 to July 2021, with fine scale geographical and socio-demographic metadata. Comparing periods in versus out of "lockdown" restrictions, we show using genetic distance relationships that individuals from more deprived areas are more likely to get infected during lockdown but less likely to spread the infection further. As disadvantaged communities were the most affected by both COVID-19 and its restrictions, our finding has important implications for informing future approaches to control future pandemics driven by similar respiratory infections., Comment: 186 pages, approx. 48 figures (including Methods ans Supplementary Information)

High-velocity outflows are ubiquitous in compact, massive (M$_* \sim$ 10$^{11}$ M$_{\odot}$), z $\sim$ 0.5 galaxies with extreme star formation surface densities ($\Sigma_{SFR} \sim$ 2000 M$_{\odot}$ yr$^{-1}$ kpc$^{-2}$). We have previously detected and characterized these outflows using MgII absorption lines. To probe their full extent, we present Keck/KCWI integral field spectroscopy of the [OII] and MgII emission nebulae surrounding all of the 12 galaxies in this study. We find that [OII] is more effective than MgII in tracing low surface brightness, extended emission in these galaxies. The [OII] nebulae are spatially extended beyond the stars, with radial extent R$_{90}$ between 10 and 40 kpc. The nebulae exhibit non-gravitational motions, indicating galactic outflows with maximum blueshifted velocities ranging from -335 to -1920 km s$^{-1}$. The outflow kinematics correlate with the bursty star formation histories of these galaxies. Galaxies with the most recent bursts of star formation (within the last $<$ 3 Myr) exhibit the highest central velocity dispersions ($\sigma >$ 400 km s$^{-1}$), while the oldest bursts have the lowest-velocity outflows. Many galaxies exhibit both high-velocity cores and more extended, slower-moving gas indicative of multiple outflow episodes. The slower, larger outflows occurred earlier and have decelerated as they propagate into the CGM and mix on timescales $>$ 50 Myr., Comment: Accepted for publication in The Astrophysical Journal

The growth of deep learning (DL) relies heavily on huge amounts of labelled data for tasks such as natural language processing and computer vision. Specifically, in image-to-text or image-to-image pipelines, opinion (sentiment) may be inadvertently learned by a model from human-generated image captions. Additionally, learning may be affected by the variety and diversity of the provided captions. While labelling large datasets has largely relied on crowd-sourcing or data-worker pools, evaluating the quality of such training data is crucial. This study proposes an evaluation method focused on sentiment and semantic richness. That method was applied to the COCO-MS dataset, comprising approximately 150K images with segmented objects and corresponding crowd-sourced captions. We employed pre-trained models (Twitter-RoBERTa-base and BERT-base) to extract sentiment scores and variability of semantic embeddings from captions. The relation of the sentiment score and semantic variability with object categories was examined using multiple linear regression. Results indicate that while most captions were neutral, about 6% of the captions exhibited strong sentiment influenced by specific object categories. Semantic variability of within-image captions remained low and uncorrelated with object categories. Model-generated captions showed less than 1.5% of strong sentiment which was not influenced by object categories and did not correlate with the sentiment of the respective human-generated captions. This research demonstrates an approach to assess the quality of crowd- or worker-sourced captions informed by image content.

Electronic structure and magnetic interactions of a Tb adatom on graphene are investigated from first principles using combination of density functional theory and multiconfigurational quantum chemistry techniques including spin-orbit coupling. We determine that the six-fold symmetry hollow site is the preferred adsorption site and we investigate electronic spectrum for different adatom oxidation states including Tb$^{3+}$, Tb$^{2+}$, Tb$^{1+}$, and Tb$^{0}$. For all charge states, the Tb $4f^8$ configuration is retained with other adatom valence electrons being distributed over $5d_{xy}$, $5d_{x2+y2}$, and $6s/5d_0$ single-electron orbitals. We find strong intra-site adatom exchange coupling that ensures that the $5d6s$ spins are parallel to the $4f$ spin. For Tb$^{3+}$, the energy levels can be described by the $J=6$ multiplet split by the graphene crystal field. For other oxidation states, the interaction of $4f$ electrons with spin and orbital degrees of freedom of $6s5d$ electrons in the presence of spin-orbit coupling results in the low-energy spectrum composed closely lying effective multiplets that are split by the graphene crystal field. Stable magnetic moment is predicted for Tb$^{3+}$ and Tb$^{2+}$ adatoms due to uniaxial magnetic anisotropy and effective anisotropy barrier around 440 cm$^{-1}$ controlled by the temperature assisted quantum tunneling of magnetization through the third excited doublet. On the other hand, in-plane magnetic anisotropy is found for Tb$^{1+}$ and Tb$^{0}$ adatoms. Our results indicate that the occupation of the $6s5d$ orbitals can dramatically affect the magnetic anisotropy and magnetic moment stability of rare earth adatoms.

In the search for new particles in high-energy physics, it is crucial to select the Signal Region (SR) in such a way that it is enriched with signal events if they are present. While most existing search methods set the region relying on prior domain knowledge, it may be unavailable for a completely novel particle that falls outside the current scope of understanding. We address this issue by proposing a method built upon a model-agnostic but often realistic assumption about the localized topology of the signal events, in which they are concentrated in a certain area of the feature space. Considering the signal component as a localized high-frequency feature, our approach employs the notion of a low-pass filter. We define the SR as an area which is most affected when the observed events are smeared with additive random noise. We overcome challenges in density estimation in the high-dimensional feature space by learning the density ratio of events that potentially include a signal to the complementary observation of events that closely resemble the target events but are free of any signals. By applying our method to simulated $\mathrm{HH} \rightarrow 4b$ events, we demonstrate that the method can efficiently identify a data-driven SR in a high-dimensional feature space in which a high portion of signal events concentrate., Comment: 5 pages, 2 figures

Estimation of the amount of cosmic-ray induced background events is a challenging task for Imaging Atmospheric Cherenkov Telescopes (IACTs). Most approaches rely on a model of the background signal derived from archival observations, which is then normalised to the region of interest (ROI) and respective observation conditions using emission-free regions in the observation.This is, however, disadvantageous for the analysis of large, extended $\gamma$-ray structures, where no sufficient source free region can be found. We aim to address this issue by estimating the normalisation of a 3-dimensional background model template from separate, matched observations of emission-free sky regions. As a result, the need for a emission-free region in the field of view of the observation becomes unnecessary. For this purpose, we implement an algorithm to identify observation pairs with as close as possible observation conditions. The open-source analysis package Gammapy is utilized for estimating the background rate, facilitating seamless adaptation of the framework to many $\gamma$-ray detection facilities. Public data from the High Energy Stereoscopic System (H.E.S.S.) is employed to validate this methodology. The analysis demonstrates that employing a background rate estimated through this run-matching approach yields results consistent with those obtained using the standard application of the background model template. Furthermore, the compatibility of the source parameters obtained through this approach with previous publications and an analysis employing the background model template approach is confirmed, along with an estimation of the statistical and systematic uncertainties introduced by this method., Comment: 11+9 pages, 8+11 figures. Accepted for publication in A&A

Simulations of coronary hemodynamics have improved non-invasive clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree. This ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating branch-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from CT myocardial perfusion imaging to estimate branch-specific coronary flows. We use adaptive Markov Chain Monte Carlo sampling to estimate the joint posterior distributions of model parameters with simulated noise in the clinical data. Additionally, we determine the posterior predictive distribution for relevant quantities of interest using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. Our framework recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement uncertainty. We substantially shrink the confidence intervals for estimated quantities of interest compared to single-fidelity and state-of-the-art multi-fidelity Monte Carlo methods. This is especially true for quantities that showed limited correlation between the low- and high-fidelity model predictions. Moreover, the proposed estimators are significantly cheaper to compute for a specified confidence level or variance.

This report details exploratory research undertaken by Newcastle University from November 2022 to May 2023. The aim of this exploratory project was to develop, trial and evaluate approaches to providing indications of how outdoor space is being perceived, used and valued by students in four schools -- to ascertain which approach (or approaches) may be best suited for use as part of Building in Use (BiU) reviews. The report details the three digital methods tested, as well as the enablers and barriers encountered while using them. The digital tools developed can be used individually to obtain limited data, but a combination of several methods is most and can be combined with information from the BiU site visit and staff survey in providing an overall BiU assessment. However, there are barriers to this including financial outlay, support from schools, and the time and effort required to create the data collection tools, even with the templates and prototypes that we detail in the report. We recommend initial discussions between the BiU technical adviser (TA) and school about tools, so that these can be chosen to fit with school procedures, especially relating to online access, and also fulfil particular needs of a specific BiU review. This would form part of a developed relationship between TA and school, beyond the existing one of schools accommodating data collection using pre-specified methods, which would also include feedback from the TA to the school.

In early 2020, the global COVID-19 pandemic closed schools for the rest of the 2019-20 school year (fiscal year 2020 or FY20). The United States Congress deployed funds to help K-12 schools adjust and plan for reopening via the Coronavirus Aid, Relief, and Economic Security (CARES) Act, including the initial $13.2 billion installment through the Elementary and Secondary School Emergency Relief Fund (ESSER I) and initial $2.95 billion installment through the Governor's Emergency Education Relief (GEER I) Fund. Private sector and non-profit organizations, including charter schools, were also eligible for loans through the Small Business Administration's Paycheck Protection Program (PPP), most of which were partially or fully forgiven. In this report, we extend our analysis of school funding during FY20 from the 2023 report, "Charter School Funding: Little Progress Toward Equity in the City." In that report, we found that, on average, charter schools receive about 30 percent ($7,147) less funding per pupil compared to traditional public schools (TPS). That analysis excluded COVID relief funding. Here we compare the initial emergency COVID relief funds received by TPS and charter schools in 18 US Cities: Atlanta, Georgia; Boston, Massachusetts; Camden, New Jersey; Chicago, Illinois; Denver, Colorado; Detroit, Michigan; Houston, Texas; Indianapolis, Indiana; Little Rock, Arkansas; Memphis, Tennessee; New Orleans, Louisiana; New York City, New York; Oakland, California; Phoenix, Arizona; San Antonio, Texas; Tulsa, Oklahoma; and Washington, DC. We use data from federal and state sources to address the following research questions: (1) did emergency COVID relief funds allocated to publicly-funded schools in FY20 widen or narrow the preexisting charter school funding gap? and (2) was initial COVID relief funding (allocated for FY20) distributed equitably relative to student poverty?