Search

The Career and Occupational Development (COD) assessment of the National Assessment of Educational Progress (NAEP) was made up of about 70 percent free response exercises requiring hand scoring. This paper describes the techniques used in developing the "scoring guides" for these exercises and summarizes the results of two empirical studies of the application of these scoring guides. The guides used in the hand scoring were sets of nominal (descriptive) category systems. No attempt was made to arrange the categories along any ordinal continuum according to either quality or content. However, categories were considered to be either acceptable or unacceptable. The readers were given a scoring guide in which each category is given a descriptive title and illustrated by a number of sample responses. (RC) Primary type of information provided by report: Procedures (Scoring).

In August 2017, Hurricane Harvey ravaged the Houston area, causing the homelessness of nearly 24,000 students in the Houston Independent School District (Houston ISD) alone. Additionally, nearly all Houston ISD schools sustained damage of some kind, resulting in school closures, campus relocations, and even the delaying of the start of classes for some students. In the first brief of this two-part series, the authors examine the characteristics of students who became homeless due to Harvey. They found that students who became temporarily homeless for a year or less due to Harvey tended to fare as well as or better on educational outcomes than even their never-homeless peers.

In August 2017, the Houston area was ravaged by one of the costliest natural disasters in history--Hurricane Harvey. In this brief, the first in a two-part series, the authors examine the effects of Harvey on student homelessness in the Houston Independent School District (Houston ISD). The authors find that student homelessness in Houston ISD quadrupled due to Harvey, and most students experiencing homelessness lived, at least temporarily, in unsheltered contexts, such as sleeping in a car or on the street. Unlike other high-profile storms such as Hurricane Katrina, students who became homeless due to Harvey tended to be broadly representative of the district in terms of their demographic characteristics. However, they differed systematically from students who experienced homelessness for conventional, economic reasons such as job loss and medical debt, who were particularly likely to be Black. The authors conclude with implications of these findings for educational stakeholders in preparation for both generational and "everyday" homelessness crises.

Specialized high schools are an increasingly popular way to prepare young adults for postsecondary experiences and expand school choice. While much literature ex- amines charter school spillover effects and the effects of specialized schools on the students who attend them, little is known about the spillover effects of specialized high schools on traditional public schools (TPS). Using an event study design, we show that one type of specialized high school, North Carolina's Cooperative Innovative High Schools, initially attracted students who were higher achieving and more likely to be white than TPS students, but these specialized schools became more representative of the district population over time. On average, the opening of specialized schools had a mix of null and positive spillover effects on TPS student achievement. While there is some evidence of negative spillovers from the first schools that opened, the effects become more positive over time.

With the increasingly congested and contested space environment, safe and effective satellite operation has become increasingly challenging. As a result, there is growing interest in autonomous satellite capabilities, with common machine learning techniques gaining attention for their potential to address complex decision-making in the space domain. However, the "black-box" nature of many of these methods results in difficulty understanding the model's input/output relationship and more specifically its sensitivity to environmental disturbances, sensor noise, and control intervention. This paper explores the use of Deep Reinforcement Learning (DRL) for satellite control in multi-agent inspection tasks. The Local Intelligent Network of Collaborative Satellites (LINCS) Lab is used to test the performance of these control algorithms across different environments, from simulations to real-world quadrotor UAV hardware, with a particular focus on understanding their behavior and potential degradation in performance when deployed beyond the training environment.

Large language models (LLMs) have shown promise in various natural language processing tasks, including their application to proteomics data to classify protein fragments. In this study, we curated a limited mass spectrometry dataset with 1000s of protein fragments, consisting of proteins that appear to be attached to the endoplasmic reticulum in cardiac cells, of which a fraction was cloned and characterized for their impact on SERCA, an ER calcium pump. With this limited dataset, we sought to determine whether LLMs could correctly predict whether a new protein fragment could bind SERCA, based only on its sequence and a few biophysical characteristics, such as hydrophobicity, determined from that sequence. To do so, we generated random sequences based on cloned fragments, embedded the fragments into a retrieval augmented generation (RAG) database to group them by similarity, then fine-tuned large language model (LLM) prompts to predict whether a novel sequence could bind SERCA. We benchmarked this approach using multiple open-source LLMs, namely the Meta/llama series, and embedding functions commonly available on the Huggingface repository. We then assessed the generalizability of this approach in classifying novel protein fragments from mass spectrometry that were not initially cloned for functional characterization. By further tuning the prompt to account for motifs, such as ER retention sequences, we improved the classification accuracy by and identified several proteins predicted to localize to the endoplasmic reticulum and bind SERCA, including Ribosomal Protein L2 and selenoprotein S. Although our results were based on proteomics data from cardiac cells, our approach demonstrates the potential of LLMs in identifying novel protein interactions and functions with very limited proteomic data.

Since the recent discovery of the directly imaged super-Jovian planet AF Lep b, several studies have been conducted to characterize its atmosphere and constrain its orbital parameters. AF Lep b has a measured dynamical mass of $3.68 \pm 0.48$ MJup, a radius of $1.3 \pm 0.15$ RJup, a nearly circular orbit in spin-orbit alignment with the host star, a relatively high metallicity, and a near-solar to super-solar C/O ratio. However, key parameters such as the rotational velocity and radial velocity could not be estimated as they require high-resolution spectroscopic data that is impossible to obtain with classical spectrographs. AF Lep b was recently observed with the new HiRISE visitor instrument at the VLT, with the goal of obtaining high-resolution (R~140,000) spectroscopic observations to better constrain the orbital and atmospheric parameters of the young giant exoplanet. We compare the extracted spectrum of AF Lep b to self-consistent atmospheric models using ForMoSA. We then use our measurements of the radial velocity of the planet to provide new constraints on the orbit of the planet. From the forward modeling, we find a C/O ratio that aligns with previous low-resolution analyses, and we confirm the super-solar metallicity. We also confirm unambiguously the presence of methane in the atmosphere of the companion. Based on all available relative astrometry and radial velocity measurements of the host star, we show that two distinct orbital populations are possible for the companion. We derive the radial velocity of AF Lep b to be $10.51 \pm 1.03$ km/s, and show that this value agrees well with one of the two orbital solutions, allowing us to rule out an entire family of orbits. Additionally, assuming that the rotation and orbit are coplanar, the derived planet's rotation rate is consistent with the observed trend of increasing spin velocity with higher planet mass., Comment: Accepted for publication in A&A

This paper addresses the problem of satellite inspection, where one or more satellites (inspectors) are tasked with imaging or inspecting a resident space object (RSO) due to potential malfunctions or anomalies. Inspection strategies are often reduced to a discretized action space with predefined waypoints, facilitating tractability in both classical optimization and machine learning based approaches. However, this discretization can lead to suboptimal guidance in certain scenarios. This study presents a comparative simulation to explore the tradeoffs of passive versus active strategies in multi-agent missions. Key factors considered include RSO dynamic mode, state uncertainty, unmodeled entrance criteria, and inspector motion types. The evaluation is conducted with a focus on fuel utilization and surface coverage. Building on a Monte-Carlo based evaluator of passive strategies and a reinforcement learning framework for training active inspection policies, this study investigates conditions under which passive strategies, such as Natural Motion Circumnavigation (NMC), may perform comparably to active strategies like Reinforcement Learning based waypoint transfers.

If $\Gamma$ is a graph for which every edge is in exactly one clique of order $\omega$, then one can form a new graph with vertex set equal to these cliques. This is a generalization of the line graph of $\Gamma$. We discover many general results and classifications related to these clique graph that will be useful to researchers studying these objects. In particular, we find bounds on its eigenvalues (with exact results when $\Gamma$ is $k$-regular) and some complete classifications when $\Gamma$ is strongly regular. We apply our results to many examples, including Conway's 99-graph problem and the existence problem for other strongly regular graphs.

Tarski's theorem states that every monotone function from a complete lattice to itself has a fixed point. We analyze the query complexity of finding such a fixed point on the $k$-dimensional grid of side length $n$ under the $\leq$ relation. In this setting, there is an unknown monotone function $f: \{0,1,\ldots, n-1\}^k \to \{0,1,\ldots, n-1\}^k$ and an algorithm must query a vertex $v$ to learn $f(v)$. The goal is to find a fixed point of $f$ using as few oracle queries as possible. We show that the randomized query complexity of this problem is $\Omega\left( \frac{k \cdot \log^2{n}}{\log{k}} \right)$ for all $n,k \geq 2$. This unifies and improves upon two prior results: a lower bound of $\Omega(\log^2{n})$ from [EPRY 2019] and a lower bound of $\Omega\left( \frac{k \cdot \log{n}}{\log{k}}\right)$ from [BPR 2024], respectively., Comment: 32 pages, 6 figures

We construct the twisted covariant form hierarchies (TCFH) of (massive) type IIA supergravity for common sector, D-brane and warped product AdS supersymmetric backgrounds and show that the Killing spinor bilinears satisfy a generalisation of the conformal Killing-Yano equation with respect to the TCFH connections. The Killing-St\"ackel, Killing-Yano and closed conformal Killing-Yano tensors of all spherically symmetric (massive) type IIA brane backgrounds are computed and one demonstrates that the geodesic flow on these solutions is completely integrable by giving all independent charges in involution. The Killing spinor form bilinears that generate hidden symmetries for spinning particle and string probe actions on such backgrounds are identified. The interplay between TCFHs and hidden symmetries of probes propagating on these backgrounds is investigated and used to explore the question of whether charges constructed from these bilinears are sufficient to prove the integrability of such probes on this class of backgrounds. Additionally, some of the properties of TCFHs, such as the reduced holonomy of the minimal TCFH connections for generic backgrounds, are investigated. After this, the algebra of holonomy symmetries of sigma models propagating on supersymmetric heterotic backgrounds with a non-compact holonomy group is determined. One demonstrates that these close as a W-algebra that is specified by a Lie algebra structure on the space of covariantly constant forms that generate the holonomy symmetries. In addition, the chiral anomalies associated with these symmetries are identified. Finally, it is argued that these anomalies are consistent and can be cancelled up to two loops in the sigma model perturbation theory., Comment: PhD thesis, King's College London. 148 pages, 1 table

Robust statistics aims to compute quantities to represent data where a fraction of it may be arbitrarily corrupted. The most essential statistic is the mean, and in recent years, there has been a flurry of theoretical advancement for efficiently estimating the mean in high dimensions on corrupted data. While several algorithms have been proposed that achieve near-optimal error, they all rely on large data size requirements as a function of dimension. In this paper, we perform an extensive experimentation over various mean estimation techniques where data size might not meet this requirement due to the high-dimensional setting.

We introduce LLM-Lasso, a novel framework that leverages large language models (LLMs) to guide feature selection in Lasso $\ell_1$ regression. Unlike traditional methods that rely solely on numerical data, LLM-Lasso incorporates domain-specific knowledge extracted from natural language, enhanced through a retrieval-augmented generation (RAG) pipeline, to seamlessly integrate data-driven modeling with contextual insights. Specifically, the LLM generates penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model. Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model, while less relevant features are assigned higher penalties, reducing their influence. Importantly, LLM-Lasso has an internal validation step that determines how much to trust the contextual knowledge in our prediction pipeline. Hence it addresses key challenges in robustness, making it suitable for mitigating potential inaccuracies or hallucinations from the LLM. In various biomedical case studies, LLM-Lasso outperforms standard Lasso and existing feature selection baselines, all while ensuring the LLM operates without prior access to the datasets. To our knowledge, this is the first approach to effectively integrate conventional feature selection techniques directly with LLM-based domain-specific reasoning., Comment: 21 pages, 16 figures

Consensus, synchronization, formation control, and power grid balance are all examples of virtuous dynamical states that may arise in networks. Here we focus on how such states can be destabilized from a fundamental perspective; namely, we address the question of how one or a few intruder agents within an otherwise functioning network may compromise its dynamics. We show that a single adversarial node coupled via adversarial couplings to one or more other nodes is sufficient to destabilize the entire network, which we prove to be more efficient than targeting multiple nodes. Then, we show that concentrating the attack on a single low-indegree node induces the greatest instability, challenging the common assumption that hubs are the most critical nodes. This leads to a new characterization of the vulnerability of a node, which contrasts with previous work, and identifies low-indegree nodes (as opposed to the hubs) as the most vulnerable components of a network. Our results are derived for linear systems but hold true for nonlinear networks, including those described by the Kuramoto model. Finally, we derive scaling laws showing that larger networks are less susceptible, on average, to single-node attacks. Overall, these findings highlight an intrinsic vulnerability of technological systems such as autonomous networks, sensor networks, power grids, and the internet of things, with implications also to the realm of complex social and biological networks.

Wereportthefirstmulti-epochmilliarcsecondresolutionimagingofthe6.7-GHzclassIImethanolmaseremissionassociated with the high-mass protocluster system NGC6334I. The observations cover 4 epochs over a 10-year period between March 2010 and March 2020. We confirm the emergence of a number of new regions of 6.7-GHz methanol maser emission in the molecular gas surrounding NGC6334-MM1, which lies north of the previously known class II methanol maser sites which are associated with NGC6334-MM3 and -MM2. The new maser emission is located close to the strongest (sub)millimetre source in the NGC6334I cluster MM1B which experienced a sudden increase in intensity in 2015, produced by an episodic accretion event. We are able to compare the location and intensity of the 6.7-GHz methanol maser emission before, during, and after the flare, providing new insights into the relationship between maser flares and periodic accretion events in high-mass stars.

We detail the use of the University of Tasmania's (UTAS) optical and radio telescopes to conduct observations of near-Earth asteroids from 2021 to 2024. The Canberra Deep Space Communication Complex transmitted a radio signal at 7159.45 MHz, with the radar echo detected by the UTAS radio telescopes. The method of accounting for the Doppler shift between the stations and the near-Earth object is described so that others can implement a similar program. We present our results, with confirmed detections of 1994 PC1 and 2003 UC20 asteroids using the Hobart and Katherine 12-m antennas, demonstrating the feasibility of using small radio telescopes for these observations. Additionally, the recently upgraded Ceduna 30 m antenna was used to detect 2024 MK. Data collected from other observatories, such as Tidbinbilla, as well as the UTAS radar tracking of the moon are also presented in the context of demonstrating the means of applying these Doppler corrections and the accuracy of each method. Optical observations conducted in this period are also detailed as they complement radar observations and aid in refining the orbit parameters., Comment: 21 pages, 16 figures, published in Remote Sensing

Carbon-oxygen (CO) detonation with reactions terminating either after burning of C$^{12}$ in the leading C$^{12}$ + C$^{12}$ reaction or after burning of C$^{12}$ and O$^{16}$ to Si-group elements may occur in the low-density outer layers of exploding white dwarfs and be responsible for the production of intermediate-mass elements observed in the outer layers of Type Ia supernovae. Basic one-dimensional properties of CO-detonations have been summarized in our previous work. This paper presents the results of two- and three-dimensional numerical simulations of low-density CO-detonations and discusses their multidimensional stability, cellular structure, and propagation through a constant low-density background. We find three-dimensional CO detonations to be strikingly different from their one-dimensional and two-dimensional counterparts. Three-dimensional detonations are significantly more robust and capable of propagating without decay compared to highly unstable and marginal one- and two- dimensional detonations. The detonation cell size and whether burning of C$^{12}$ in a three-dimensional detonation wave is followed by the subsequent O$^{16}$ burning is sensitive to both the background density and the initial C$^{12}$ to O$^{16}$ mass ratio. We also discuss the possible implications for understanding the observed early time bumps in light-curves., Comment: 29 pages, 15 Figures; ApJ, submitted (11/26/2024), revised (1/14/2025), accepted(1/25/2025), in press

We study functions on the set of all integral ideals in a Dedekind domain. The set of all such functions is a commutative ring isomorphic to a ring of formal power series in infinitely many variables. The set of all totally multiplicative functions has a ringed space structure, which, after identifying functions with the same prime ideal zeros, determines the Dedekind domain up to isomorphism., Comment: 21 pages

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures

Multi-plane light converters (MPLCs) - also known as linear diffractive neural networks - are an emerging optical technology, capable of converting an orthogonal set of optical fields into any other orthogonal set via a unitary transformation. MPLC design is a non-linear problem typically solved by optimising a digital model of the optical system. However, inherently high levels of design complexity mean that even a minor mismatch between this digital model and the physically realised MPLC leads to a severe reduction in real-world performance. Here we address this challenge by creating a self-configuring free-space MPLC. Despite the large number of parameters to be optimised (typically tens of thousands or more), our proof-of-principle device converges in minutes using a method in which light only needs to be transmitted in one direction through the MPLC. Two innovations make this possible. Firstly, we devise an in-situ optimisation algorithm combining wavefront shaping with the principles of wavefront matching that would conventionally be used to inverse-design MPLCs offline in simulation. Secondly, we introduce a new MPLC platform incorporating a microelectromechanical system (MEMS) phase-only light modulator - allowing rapid MPLC switching at up to kiloHertz rates. Our scheme automatically accounts for the physical characteristics of all system components and absorbs any unknown misalignments and aberrations into the final design. We demonstrate self-configured MPLCs capable of mapping random orthogonal speckle input fields to well-defined Laguerre-Gaussian and Hermite-Gaussian output modes, as well as universal mode sorters. Our work paves the way towards large-scale ultra-high-fidelity fast-switching MPLCs and diffractive neural networks, which promises to unlock new applications in areas ranging from optical communications to optical computing and imaging., Comment: 14 pages, 7 figures, 1 table

We present an atmospheric characterization and orbital analysis of HD 206893 B, an exceptionally red, L/T-transition substellar companion in a multiplanetary system, via Keck Planet Imager and Characterizer (KPIC) high-resolution (R $\sim$ 35,000) K-band spectroscopy. Using PHOENIX atmospheric models in a forward-model framework that fits the spectrum of the companion and diffracted starlight simultaneously, we detect HD 206893 B at $>8\sigma$ significance via cross-correlation in two epochs. We find an effective temperature for the companion of $1634^{+72}_{-38}$ K and a log(g) of $4.55^{+0.17}_{-0.22}$. Only accounting for statistical uncertainties, we measure the carbon-oxygen ratio (C/O) of this companion to be $0.57 \pm 0.02$, or near-solar while assuming solar metallicity. The C/O ratio we measure fits the tentative trend of $>4 M_{Jup}$ companions having near-solar C/O ratios while less massive companions have greater-than-solar C/O ratios. Using substellar evolution models, we find an age of $112^{+36}_{-22}$ Myr, a mass of $22.7^{+2.5}_{-1.7} M_{Jup}$, and a radius of $1.11 \pm 0.03 R_{Jup}$ for this companion. We also use KPIC radial velocity data to fit the orbit of HD 206893 B and analyze the orbital stability of this system. We find that the orbital stability is relatively independent of the mass of HD 206893 B, and favors an orbital configuration where B and its interior planetary companion, HD 206893 c, are co-planar. The measured C/O ratio coupled with the current architecture of the system cannot rule out a core accretion scenario, nor a disk fragmentation scenario regarding the formation pathway of HD 206893 B., Comment: 37 pages, 23 figures

This paper presents a time-constrained model predictive control strategy for the six degree-of-freedom autonomous rendezvous, proximity, operations and docking problem between a controllable "deputy" satellite and an uncontrolled "chief" satellite. The objective is to achieve a docking configuration defined by both the translational and attitudinal states of the deputy relative to the chief, whose dynamics are respectively governed by both the Clohessy-Wiltshire equations and Euler's second law of motion. The proposed control strategy explicitly addresses computational time constraints that are common to state-of-the-art space vehicles. Thus, a time-constrained model predictive control strategy is implemented on a space-grade processor. Although suboptimal with regards to energy consumption when compared to conventional optimal RPO trajectories, it is empirically demonstrated via numerical simulations that the deputy spacecraft still achieves a successful docking configuration while subject to computational time constraints., Comment: arXiv admin note: text overlap with arXiv:2211.11653

Nitrogen Vacancy diamond nanoparticles (NVNPs) are increasingly integrated with methods for optical detection of magnetic resonance (ODMR), providing new opportunities in magnetic characterization that span the visualization of magnetic fields in microelectronic circuits, environmental sensing and biology. However, only a small number of studies utilize aggregates of NVNPs for surface-wide magnetometry being that spin orientations in aggregate NVNPs are inherently misaligned, precluding their use for proper magnetic field detection compared with expensive monocrystalline diamonds. A postprocessing method for layering NVNPs with aligned NV center orientations can potentially facilitate superior NV magnetometry by allowing sensitive detection combined with simplified probe preparation. We present a novel technology for creating densely stacked monolayers of NVNP with inherent interlayer alignment for sensitive measurement of local magnetic field perturbations in microelectronic traces. We establish spatial characteristics of deposited aggregates and demonstrate their ability to capture magnetic dipoles from conducting microwires via ODMR. Our approach forms a novel accessible protocol that can be used for broad applications in micromagnetometry.

In recent years, observations with the JWST have started to map out the rapid metal enrichment of the early Universe, while (sub)millimeter observations have simultaneously begun to reveal the ubiquity of dust beyond $z\gtrsim6$. However, the pathways that led to the assembly of early dust reservoirs remain poorly quantified, and require pushing our understanding of key scaling relations between dust, gas and metals into the early Universe. We investigate the dust build-up in twelve $6.5 \lesssim z \lesssim 7.7$ galaxies drawn from the REBELS survey that benefit from (i) JWST/NIRSpec strong-line metallicity measurements, (ii) ALMA [CII]-based redshifts and gas masses, and (iii) dust masses from single- or multi-band ALMA continuum observations. Combining these measurements, we investigate the dust-to-gas (DtG), dust-to-metal (DtM), and dust-to-stellar mass (DtS) ratios of our sample as a function of metallicity. While our analysis is limited by systematic uncertainties related to the [CII]-to-H$_2$ conversion factor and dust temperature, we explore a wide range of possible values, and carefully assess their impact on our results. Under a fiducial set of assumptions, we find an average $\log(\mathrm{DtG}) = -3.02 \pm 0.23$, only slightly below that of local metal-rich galaxies. On the other hand, at fixed metallicity our average $\log(\mathrm{DtS}) = -2.15 \pm 0.42$ is significantly larger than that of low-redshift galaxies. Finally, through a comparison to various theoretical models of high-redshift dust production, we find that assembling the dust reservoirs in massive galaxies at $z\approx7$ likely requires the combination of rapid supernova enrichment and efficient ISM dust growth., Comment: 18 pages + appendices, 5 figures in main text, submitted to MNRAS

We present measurements of the dust attenuation curves of 12 massive ($9~<~\log$($M_{\star}/{M}_{\odot})$ $<~10$) Lyman-break galaxies at $z=6.5-7.7$ derived from James Webb Space Telescope (JWST) NIRSpec integral field unit (IFU) spectroscopy. The galaxies are drawn from the Atacama Large Millimeter/submillimeter Array (ALMA) Reionization Era Bright Emission Line Survey (REBELS) large program. The dust attenuation curves were obtained by fitting spectral energy distribution (SED) models with a flexible dust law to the full galaxy spectra over observed wavelengths $0.6-5.3$ $\mu$m. These attenuation curves show a range of recovered slopes ($-0.39\leq\delta\leq0.08$) that are on average slightly flatter than seen in local sources of the same stellar masses, with none exhibiting very steep slopes. Three galaxies exhibit evidence for a 2175 \r{A} dust bump ($>4\sigma$) and we find SED fitting excluding the bump can overestimate derived stellar masses by up to $0.4$ dex. Correcting for the dust attenuation with our best-fit attenuation curves we recover a range of intrinsic UV-slopes ($-2.5\leq\beta_0\leq-2.2$). The galaxies show moderate reddening ($A_V~=~0.1-0.6$ mag) and the $A_V$ to stellar mass relation is consistent with local sources. The attenuation law slope is found to correlate with $A_V$, while we see no strong correlation with stellar mass, ${M_{\rm UV}}$, or gas-phase metallicity. Overall, our results show little evolution in dust properties in the REBELS sources compared to the local Universe. Comparing our recovered trends to empirical models suggests that the most important factor driving the variation in the attenuation curves in our sample is the dust-star geometry, not the properties of the dust grains themselves., Comment: 17 Pages, 6 Figures, Submitted to MNRAS

Quantum mechanical invariance principles dictate the most general operator structure that can be present in the nucleon-nucleon (NN) interaction. Five independent operators appear in the on-shell NN amplitude together with five corresponding coefficient functions. The usual choice for these coefficient functions is known as the NN Wolfenstein amplitudes. We analyze the order-by-order convergence of each of the five NN Wolfenstein amplitudes predicted by a semi-local coordinate space potential implementation of chiral effective field theory ($\chi$EFT). We do this at laboratory kinetic energies between 25 and 200 MeV for both neutron-proton and proton-proton scattering. Our analysis uses the Gaussian-Process methods developed by the BUQEYE collaboration to describe the contributions of each $\chi$EFT order, and so yields truncation uncertainties for each Wolfenstein amplitude that are correlated across scattering angles. We combine information on the size of different orders in the EFT to infer the $\chi$EFT breakdown scale for each amplitude, finding, on average, $\Lambda_b$ between 750 and 800 MeV. With this choice of $\Lambda_b$, the EFT truncation uncertainties cover both higher-order results and empirical Wolfenstein amplitudes well for all orders other than the leading order., Comment: 16 pages, 22 figures

The Safe Trusted Autonomy for Responsible Space (STARS) program aims to advance autonomy technologies for space by leveraging machine learning technologies while mitigating barriers to trust, such as uncertainty, opaqueness, brittleness, and inflexibility. This paper presents the achievements and lessons learned from the STARS program in integrating reinforcement learning-based multi-satellite control, run time assurance approaches, and flexible human-autonomy teaming interfaces, into a new integrated testing environment for collaborative autonomous satellite systems. The primary results describe analysis of the reinforcement learning multi-satellite control and run time assurance algorithms. These algorithms are integrated into a prototype human-autonomy interface using best practices from human-autonomy trust literature, however detailed analysis of the effectiveness is left to future work. References are provided with additional detailed results of individual experiments.

Quantum imaging with undetected photons (QIUP) is an emerging technique that decouples the processes of illuminating an object and projecting its image. The properties of the illuminating and detected light can thus be simultaneously optimised for both contrast on a sample and sensitivity on a camera. Here, we combine QIUP with computed tomography to enable three-dimensional (3D) infrared imaging. The image data is registered with a standard silicon camera at a wavelength of 810 nm, but the extracted 3D images map the sample's absorption at a wavelength of 1550 nm, well beyond the camera's sensitivity. Quantum Undetected Optical Projection Tomography (QUOPT) enables label-free volumetric sensing at difficult to detect wavelengths, such as those that allow molecular imaging contrast, or those within the infrared biological transmission windows.

We present optical photometric and spectroscopic observations of the peculiar Type Ia supernova ASASSN-20jq/SN 2020qxp. It is a low-luminosity object with a peak absolute magnitude of $M_B=-17.1\pm0.5$ mag. Despite its low luminosity, its post-peak light-curve decline rate ($\Delta m_{15}(B)=1.35\pm0.09$ mag) and color-stretch parameter (sBV>0.82) are similar to normal SNe Ia, making it an outlier in the luminosity-width and luminosity-color-stretch relations. Early light curves suggest a "bump" during the first 1.4 days of explosion. ASASSN-20jq synthesized a low radioactive $^{56}$Ni mass of $0.09\pm0.01M_\odot$. Near-maximum light spectra reveal strong Si II absorption lines, indicating a cooler photosphere than normal SNe Ia, but lack Ti II absorption lines. Unusually strong O I $\lambda$7773 and Ca II near-infrared triplet absorption features are present. Nebular spectra show a strong, narrow forbidden [Ca II] $\lambda\lambda$7291,7324 doublet emission, rarely seen in SNe Ia except in some Type Iax events. Marginal detection of [O I] $\lambda\lambda$6300,6364 doublet emission, which is extremely rare, is observed. Both [Ca II] and [O I] lines are redshifted by $\sim2000$ km/s. A strong [Fe II] $\lambda$7155 emission line with a tilted-top profile, identical to the [Fe II] $\lambda$16433 profile, is also observed. These asymmetric [Fe II] profiles and redshifted [Ca II] and [O I] emissions suggest a high central density white dwarf progenitor undergoing an off-center delayed-detonation explosion mechanism, producing roughly equal amounts of $^{56}$Ni in deflagration and detonation phases. This distinguishes ASASSN-20jq from normal and subluminous SNe Ia. ASASSN-20jq's light curve and spectra do not align with any single SNe Ia subclass but show similarities to 2002es-like objects. Thus, we add it as an extreme candidate within the heterogeneous parameter space of 2002es-like SNe Ia., Comment: 27 pages, 20 figures, 5 tables, submitted to A&A

Multi-agent systems are increasingly applied in space missions, including distributed space systems, resilient constellations, and autonomous rendezvous and docking operations. A critical emerging application is collaborative spacecraft servicing, which encompasses on-orbit maintenance, space debris removal, and swarm-based satellite repositioning. These missions involve servicing spacecraft interacting with malfunctioning or defunct spacecraft under challenging conditions, such as limited state information, measurement inaccuracies, and erratic target behaviors. Existing approaches often rely on assumptions of full state knowledge or single-integrator dynamics, which are impractical for real-world applications involving second-order spacecraft dynamics. This work addresses these challenges by developing a distributed state estimation and tracking framework that requires only relative position measurements and operates under partial state information. A novel $\rho$-filter is introduced to reconstruct unknown states using locally available information, and a Lyapunov-based deep neural network adaptive controller is developed that adaptively compensates for uncertainties stemming from unknown spacecraft dynamics. To ensure the collaborative spacecraft regulation problem is well-posed, a trackability condition is defined. A Lyapunov-based stability analysis is provided to ensure exponential convergence of errors in state estimation and spacecraft regulation to a neighborhood of the origin under the trackability condition. The developed method eliminates the need for expensive velocity sensors or extensive pre-training, offering a practical and robust solution for spacecraft servicing in complex, dynamic environments., Comment: 24 pages, 4 Figures, Journal

We present 50-fs, single-shot measurements of the x-ray thermal diffuse scattering (TDS) from copper foils that have been shocked via nanosecond laser-ablation up to pressures above 135~GPa. We hence deduce the x-ray Debye-Waller (DW) factor, providing a temperature measurement. The targets were laser-shocked with the DiPOLE 100-X laser at the High Energy Density (HED) endstation of the European X-ray Free-Electron Laser (EuXFEL). Single x-ray pulses, with a photon energy of 18 keV, were scattered from the samples and recorded on Varex detectors. Despite the targets being highly textured (as evinced by large variations in the elastic scattering), and with such texture changing upon compression, the absolute intensity of the azimuthally averaged inelastic TDS between the Bragg peaks is largely insensitive to these changes, and, allowing for both Compton scattering and the low-level scattering from a sacrificial ablator layer, provides a reliable measurement of $T/\Theta_D^2$, where $\Theta_D$ is the Debye temperature. We compare our results with the predictions of the SESAME 3336 and LEOS 290 equations of state for copper, and find good agreement within experimental errors. We thus demonstrate that single-shot temperature measurements of dynamically compressed materials can be made via thermal diffuse scattering of XFEL radation., Comment: 17 pages, 9 figures in main article; 10 pages, 5 figures in supplementary material

We introduce a numerical method for Brownian dynamics with position dependent diffusion tensor which is second order accurate for sampling the invariant measure while requiring only one force evaluation per timestep. Analysis of the sampling bias is performed using the algebraic framework of exotic aromatic Butcher-series. Numerical experiments confirm the theoretical order of convergence and illustrate the efficiency of the new method., Comment: 31 pages

This First Look report introduces new data for national and state-level public elementary and secondary revenues and expenditures for fiscal year (FY) 2022. Specifically, this report includes the following school finance data: (1) revenue and expenditure totals; (2) revenues by source; (3) expenditures by function, subfunction, and object; (4) current expenditures; (5) revenues and current expenditures per pupil; (6) expenditures from Title I funds; and (7) revenues and expenditures from COVID-19 Federal Assistance Funds. The expenditure functions include instruction, support services, food services, and enterprise operations. The support services function is further broken down into seven subfunctions: instructional staff support services, pupil support services, general administration, school administration, operations and maintenance, student transportation, other support services (such as business services). Objects reported within a function or subfunction include salaries and wages, employee benefits, purchased services, supplies, and equipment. The purpose of a First Look report is to introduce new data through the presentation of tables containing descriptive information. The selected findings chosen for this report demonstrate the range of information available when using NPEFS. They do not represent all of the data and are not meant to emphasize any particular issue. While the tables in this report include data for all NPEFS respondents, the selected findings are limited to the 50 states and the District of Columbia.

This First Look report introduces new data for national and state-level public elementary and secondary revenues and expenditures for fiscal year (FY) 2022. Specifically, this report includes the following school finance data: (1) revenue and expenditure totals; (2) revenues by source; (3) expenditures by function, subfunction, and object; (4) current expenditures; (5) revenues and current expenditures per pupil; (6) expenditures from Title I funds; and (7) revenues and expenditures from COVID-19 Federal Assistance Funds. The expenditure functions include instruction, support services, food services, and enterprise operations. The support services function is further broken down into seven subfunctions: instructional staff support services, pupil support services, general administration, school administration, operations and maintenance, student transportation, other support services (such as business services).1 Objects reported within a function or subfunction include salaries and wages, employee benefits, purchased services, supplies, and equipment. The finance data used in this report are from the National Public Education Financial Survey (NPEFS), a component of the Common Core of Data (CCD). The CCD is one of NCES's primary survey programs on public elementary and secondary education in the United States. State education agencies (SEAs) in each of the 50 states, the District of Columbia, and the five other jurisdictions of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands report these data annually to NCES. The NPEFS instructions ask SEAs to report revenues and expenditures covering prekindergarten through high school public education in regular, special, and vocational schools; charter schools; and state-run education programs (such as special education schools or education programs for incarcerated youth).