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We examine the efficiency of traditional school districts versus charter schools in providing students with teachers who meet their demographic and education needs. Using panel data from the state of Michigan, we estimate the relationship between enrollment of Black, Hispanic, special education, and English learner students and the presence of Black, Hispanic, Special Education, and ESL teachers, and test whether this relationship differs at charter and traditional district-run schools. Because charter schools typically have less market power in hiring than large districts, we compare charter school employment practices to traditional public schools in districts of comparable size. Our results suggest that charter schools are more likely to employ same race teachers for Black students but not Hispanic students, and districts schools are slightly better at providing ESL and SPED teachers. We conclude that charter autonomy does not necessary generate better student-teacher matches, but Michigan charters may occupy a market niche by serving Black students and staffing Black teachers.

Background: Healthcare has many manual processes that can benefit from automation and augmentation with Generative Artificial Intelligence (AI), the medical billing and coding process. However, current foundational Large Language Models (LLMs) perform poorly when tasked with generating accurate International Classification of Diseases, 10th edition, Clinical Modification (ICD-10-CM) and Current Procedural Terminology (CPT) codes. Additionally, there are many security and financial challenges in the application of generative AI to healthcare. We present a strategy for developing generative AI tools in healthcare, specifically for medical billing and coding, that balances accuracy, accessibility, and patient privacy. Methods: We fine tune the PHI-3 Mini and PHI-3 Medium LLMs using institutional data and compare the results against the PHI-3 base model, a PHI-3 RAG application, and GPT-4o. We use the post operative surgical report as input and the patients billing claim the associated ICD-10, CPT, and Modifier codes as the target result. Performance is measured by accuracy of code generation, proportion of invalid codes, and the fidelity of the billing claim format. Results: Both fine-tuned models performed better or as well as GPT-4o. The Phi-3 Medium fine-tuned model showed the best performance (ICD-10 Recall and Precision: 72%, 72%; CPT Recall and Precision: 77%, 79%; Modifier Recall and Precision: 63%, 64%). The Phi-3 Medium fine-tuned model only fabricated 1% of ICD-10 codes and 0.6% of CPT codes generated. Conclusions: Our study shows that a small model that is fine-tuned on domain-specific data for specific tasks using a simple set of open-source tools and minimal technological and monetary requirements performs as well as the larger contemporary consumer models., Comment: 21 pages, 3 figures, 2 tables

Research on test accommodations provides valuable information that informs policy and practice. This report presents a synthesis of the research literature published in 2022 on testing accommodations for U.S. elementary and secondary students (K-12). The National Center on Educational Outcomes (NCEO) has reported on accommodations research since 1999. In 2022, NCEO identified four research studies on testing accommodations that met the criteria for inclusion. This was fewer studies than in previous years. The limited number of published studies focusing on accommodations may be the result of gaps in testing data due to COVID-19, as well as other lingering effects of the pandemic. All the studies published in 2022 that met the criteria were dissertations. The studies presented in this report provide an update on the state of the research on testing accommodations. These studies addressed several topics including research on teacher, student, and parent perceptions and perspectives regarding accommodations. Primary data were collected for all of these studies, and most of the studies used qualitative methods. The studies published in 2022 included studies conducted at the elementary, middle school, and high school levels. The studies addressed the use of accommodations on mathematics, reading, and science assessments; one study did not specify the content area.

In the context of an ongoing national conversation about teacher shortages, we build on prior literature on the efficacy of teacher certification pathways by comparing entry and exit patterns based on age at the time of certification. All trainees who complete a state certification process have invested substantial time and resources into entering teaching. Competing employment opportunities and expectations might vary with age. We use both linear regression and discrete-time hazard models to examine employment and subsequent exit of newly certified teacher trainees in Michigan from 2011 to 2023. We find that while mid-career entrants in their 30s and 40s compose a small share of new certificates, they are more likely to enter a teaching position and no more likely to subsequently exit than counterparts who were certified in their early 20s. Mid-career pathways also contribute to teacher diversity by attracting more Black and male teachers who enter and persist.

Despite increasing recognition of the importance of high-quality school leadership, we know remarkably little about principal skill development. Using administrative data from Tennessee, Oregon, and New York City, we estimate the returns to principal experience as measured by student outcomes, teacher hiring and retention patterns, and teacher and supervisor ratings of principals. The typical principal leads a school for only 3-5 years and leaves the principalship after 6-7 years. We find little evidence that school performance improves as principals gain experience, despite substantial improvement in supervisor ratings. Our results suggest that strategies intended to increase principal retention are unlikely to improve school outcomes absent more comprehensive efforts to strengthen the link between principal skill development and student and school outcomes.

Graph clustering is a fundamental technique in data analysis with applications in many different fields. While there is a large body of work on clustering undirected graphs, the problem of clustering directed graphs is much less understood. The analysis is more complex in the directed graph case for two reasons: the clustering must preserve directional information in the relationships between clusters, and directed graphs have non-Hermitian adjacency matrices whose properties are less conducive to traditional spectral methods. Here we consider the problem of partitioning the vertex set of a directed graph into $k\ge 2$ clusters so that edges between different clusters tend to follow the same direction. We present an iterative algorithm based on spectral methods applied to new Hermitian representations of directed graphs. Our algorithm performs favourably against the state-of-the-art, both on synthetic and real-world data sets. Additionally, it is able to identify a "meta-graph" of $k$ vertices that represents the higher-order relations between clusters in a directed graph. We showcase this capability on data sets pertaining food webs, biological neural networks, and the online card game Hearthstone., Comment: 18 pages, 8 figures

The spectrum of the nonbacktracking matrix associated to a network is known to contain fundamental information regarding percolation properties of the network. Indeed, the inverse of its leading eigenvalue is often used as an estimate for the percolation threshold. However, for many networks with nonbacktracking centrality localised on a few nodes, such as networks with a core-periphery structure, this spectral approach badly underestimates the threshold. In this work, we study networks that exhibit this localisation effect by looking beyond the leading eigenvalue and searching deeper into the spectrum of the nonbacktracking matrix. We identify that, when localisation is present, the threshold often more closely aligns with the inverse of one of the sub-leading real eigenvalues: the largest real eigenvalue with a "delocalised" corresponding eigenvector. We investigate a core-periphery network model and determine, both theoretically and experimentally, a regime of parameters for which our approach closely approximates the threshold, while the estimate derived using the leading eigenvalue does not. We further present experimental results on large scale real-world networks that showcase the usefulness of our approach., Comment: 20 pages, 9 figures

Guesstimation, the task of making approximate quantity estimates, is a common real-world challenge. However, it has been largely overlooked in large language models (LLMs) and vision language models (VLMs) research. We introduce a novel guesstimation dataset, MARBLES. This dataset requires one to estimate how many items (e.g., marbles) can fit into containers (e.g., a one-cup measuring cup), both with and without accompanying images. Inspired by the social science concept of the ``Wisdom of Crowds'' (WOC) - taking the median from estimates from a crowd), which has proven effective in guesstimation, we propose ``WOC decoding'' strategy for LLM guesstimation. We show that LLMs/VLMs perform well on guesstimation, suggesting that they possess some level of a "world model" necessary for guesstimation. Moreover, similar to human performance, the WOC decoding method improves LLM/VLM guesstimation accuracy. Furthermore, the inclusion of images in the multimodal condition enhances model performance. These results highlight the value of WOC decoding strategy for LLMs/VLMs and position guesstimation as a probe for evaluating LLMs/VLMs' world model. As LLMs' world model is a fundamental prerequisite for many real-world tasks, e.g., human-AI teaming, our findings have broad implications for the AI community.

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. We present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

Experiments have reached a monumental capacity for designing and synthesizing microscopic particles for self-assembly, making it possible to precisely control particle concentrations, shapes, and interactions. However, we lack a comprehensive inverse-design framework for tuning these particle-level attributes to obtain desired system-level assembly outcomes, like the yield of a user-specified target structure. This severely limits our ability to take full advantage of this vast design space to assemble nanomaterials with complex structure and function. Here we show that a hidden mathematical architecture controls equilibrium assembly outcomes and provides a single comprehensive view of the entire design space. This architecture predicts which structures can be assembled at high yield and reveals constraints that govern the coexistence of structures, which we verify through detailed, quantitative assembly experiments of nanoscale particles synthesized using DNA origami. Strong experimental agreement confirms the importance of this underlying architecture and motivates its use as a predictive tool for the rational design of self-assembly. These results uncover a universal core logic underpinning all equilibrium self-assembly that forms the basis for a robust inverse-design framework, applicable to a wide array of systems from biological protein complexes to synthetic nanomachines.

Automation and semi-automation through computational tools like LLMs are also making their way to deployment in research synthesis and secondary research, such as systematic reviews. In some steps of research synthesis, this has the opportunity to provide substantial benefits by saving time that previously was spent on repetitive tasks. The screening stages in particular may benefit from carefully vetted computational support. However, this position paper argues for additional caution when bringing in such tools to the analysis and synthesis phases, where human judgement and expertise should be paramount throughout the process., Comment: Accepted at CHI 2024 workshop: "LLMs as Research Tools: Applications and Evaluations in HCI Data Work" (https://doi.org/10.1145/3613905.3636301)

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 3,000 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: 25 pages, 6 figures

Traditionally, autonomous reconnaissance applications have acted on explicit sets of historical observations. Aided by recent breakthroughs in generative technologies, this work enables robot teams to act beyond what is currently known about the environment by inferring a distribution of reasonable interpretations of the scene. We developed a map predictor that inpaints the unknown space in a multi-agent 2D occupancy map during an exploration mission. From a comparison of several inpainting methods, we found that a fine-tuned latent diffusion inpainting model could provide rich and coherent interpretations of simulated urban environments with relatively little computation time. By iteratively inferring interpretations of the scene throughout an exploration run, we are able to identify areas that exhibit high uncertainty in the prediction, which we formalize with the concept of generative entropy. We prioritize tasks in regions of high generative entropy, hypothesizing that this will expedite convergence on an accurate predicted map of the scene. In our study we juxtapose this new paradigm of task ranking with the state of the art, which ranks regions to explore by those which maximize expected information recovery. We compare both of these methods in a simulated urban environment with three vehicles. Our results demonstrate that by using our new task ranking method, we can predict a correct scene significantly faster than with a traditional information-guided method.

The focus on managing problems that can arise for older adults has meant that extant HCI and Ageing research has not given the concepts of 'age' and 'ageing' the explicit theoretical attention they deserve. Attending to this gap, we critically examine a ten-year corpus of CHI publications through the lens of an existing typology which we have further developed to analyse how age is understood, interpreted and constructed in the field of HCI. Our resulting multidimensional typology of age in HCI elucidates the distinctive characteristics of older adults considered when designing with and for this user group, but also highlights the need for a more critical, reflexive, social constructivist approach to age in HCI. Applying this approach, we explore age as a multidimensional system of stratification to better understand the phenomenon of the age-based digital divide., Comment: 34 pages, plus 5 pages of appendix, and 13 pages of supplemental bibliography; 4 tables, 2 figures

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.

This study investigated discrepancies between measured and simulated x-ray drive in Inertial Confinement Fusion (ICF) hohlraums at the National Ignition Facility (NIF). Despite advances in radiation-hydrodynamic simulations, a consistent "drive deficit" remains. Experimentally measured ICF capsule bang-times are systematically 400-700 ps later than simulations predict. The Build-A-Hohlraum (BAH) campaign explored potential causes for this discrepancy by systematically varying hohlraum features, including laser entrance hole (LEH) windows, capsules, and gas fills. Overall, the agreement between simulated and experimental x-ray drive was found to be largely unaffected by these changes. The data allows us to exclude some hypotheses put forward to potentially explain the discrepancy. Errors in the local thermodynamic equilibrium (LTE) atomic modeling, errors in the modeling of LEH closure and errors due to a lack of plasma species mix physics in simulations are shown to be inconsistent with our measurements. Instead, the data supports the hypothesis that errors in NLTE emission modeling are a significant contributor to the discrepancy. X-ray emission in the 2 - 4 keV range is found to be approximately 30% lower than in simulations. This is accompanied by higher than predicted electron temperatures in the gold bubble region, pointing to errors in non-LTE modeling. Introducing an opacity multiplier of 0.87 on energy groups above 1.8 keV improves agreement with experimental data, reducing the bang-time discrepancy from 300 ps to 100 ps. These results underscore the need for refined NLTE opacity models to enhance the predictive power of hohlraum simulations.

We confirm the planetary nature of a pair of transiting sub-Neptune exoplanets orbiting the bright F-type sub-giant star TOI-6054 ($V=8.02$, $K=6.673$) as a part of the OrCAS radial velocity survey using WIYN/NEID observations. We find that TOI-6054b and TOI-6054c have radii of $2.65 \pm 0.15$ $R_{\oplus}$ and $2.81 \pm 0.18$ $R_{\oplus}$, respectively, and masses of $12.4 \pm 1.7$ $M_{\oplus}$ and $9.2 \pm 2.0$ $M_{\oplus}$. The planets have zero-albedo equilibrium temperatures of $1360 \pm 33$ K and $1144 \pm 28$ K. The host star has expanded and will evolve off of the Main Sequence within the next $\sim$500 Myr, and the resulting increase in stellar luminosity has more than doubled the stellar flux the two planets receive compared to the start of the host star's main sequence phase. Consequently, TOI-6054b may be losing some of its primordial H/He atmosphere -- if it has one. Based on dynamical simulations performed using the orbital parameters of the two planets, TOI-6054b, and TOI-6054c are very likely in a 5:3 mean motion resonance. The TOI-6054 system thus has the potential to be an excellent candidate for future atmospheric follow-up observations, with two similarly sized sub-Neptunes around a bright star. We also estimate that if TOI-6054b is currently losing its H/He atmosphere this should be observable from space and from the ground., Comment: 23 pages, 11 figures, submitted to AJ, partially updated with referee's comments

This work investigates a class of moving boundary problems related to a nonlinear evolution equation featuring an exponential source term. We establish a connection to Stefan-type problems, for different boundary conditions at the fixed face, through the application of a reciprocal transformation alongside the Cole-Hopf transformation. For specific cases, we derive explicit similarity solutions in parametric form. This innovative approach enhances our understanding of the underlying dynamics and offers valuable insights into the behavior of these systems.

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

Programmable self-assembly has recently enabled the creation of complex structures through precise control of the interparticle interactions and the particle geometries. Targeting ever more structurally complex, dynamic, and functional assemblies necessitates going beyond the design of the structure itself, to the measurement and control of the local flexibility of the inter-subunit connections and its impact on the collective mechanics of the entire assembly. In this study, we demonstrate a method to infer the mechanical properties of multisubunit assemblies using cryogenic electron microscopy (cryo-EM) and RELION's multi-body refinement. Specifically, we analyze the fluctuations of pairs of DNA-origami subunits that self-assemble into tubules. By measuring the fluctuations of dimers using cryo-EM, we extract mechanical properties such as the bending modulus and interparticle spring constant. These properties are then applied to elastic models to predict assembly outcomes, which align well with experimental observations. This approach not only provides a deeper understanding of nanoparticle mechanics, but also opens new pathways to refining subunit designs to achieve precise assembly behavior. This methodology could have broader applications in the study of nanomaterials, including protein assemblies, where understanding the interplay of mechanical properties and subunit geometry is essential for controlling complex self-assembled structures., Comment: 10 Pages, 4 Figures, Supplement 18 Pages, 11 Figures, 2 Tables

We present the results of the Distant Giants Survey, a three-year radial velocity (RV) campaign to search for wide-separation giant planets orbiting Sun-like stars known to host an inner transiting planet. We defined a distant giant to have $a$ = 1--10 AU and $M_{p} \sin i = 70-4000$ \mearth~ = 0.2-12.5 \mj, and required transiting planets to have $a<1$ AU and $R_{p} = 1-4$ \rearth. We assembled our sample of 47 stars using a single selection function, and observed each star at monthly intervals to obtain $\approx$30 RV observations per target. The final catalog includes a total of twelve distant companions: four giant planets detected during our survey, two previously known giant planets, and six objects of uncertain disposition identified through RV/astrometric accelerations. Statistically, half of the uncertain objects are planets and the remainder are stars/brown dwarfs. We calculated target-by-target completeness maps to account for missed planets. We found evidence for a moderate enhancement of distant giants (DG) in the presence of close-in small planets (CS), P(DG|CS) = $30^{+14}_{-12}\%$, over the field rate of P(DG) = $16^{+2}_{-2}\%$. No enhancement is disfavored ($p \sim$ 8%). In contrast to a previous study, we found no evidence that stellar metallicity enhances P(DG|CS). We found evidence that distant giant companions are preferentially found in systems with multiple transiting planets and have lower eccentricities than randomly selected giant planets. This points toward dynamically cool formation pathways for the giants that do not disturb the inner systems., Comment: 32 pages, 20 figures, 4 tables. Comments welcome

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

Increasing HPC cluster sizes and large-scale simulations that produce petabytes of data per run, create massive IO and storage challenges for analysis. Deep learning-based techniques, in particular, make use of these amounts of domain data to extract patterns that help build scientific understanding. Here, we demonstrate a streaming workflow in which simulation data is streamed directly to a machine-learning (ML) framework, circumventing the file system bottleneck. Data is transformed in transit, asynchronously to the simulation and the training of the model. With the presented workflow, data operations can be performed in common and easy-to-use programming languages, freeing the application user from adapting the application output routines. As a proof-of-concept we consider a GPU accelerated particle-in-cell (PIConGPU) simulation of the Kelvin- Helmholtz instability (KHI). We employ experience replay to avoid catastrophic forgetting in learning from this non-steady process in a continual manner. We detail challenges addressed while porting and scaling to Frontier exascale system., Comment: 12 pages, 9 figures

Rendering algorithms typically integrate light paths over path space. However, integrating over this one unified space is not necessarily the most efficient approach, and we show that partitioning path space and integrating each of these partitioned spaces with a separate estimator can have advantages. We propose an approach for partitioning path space based on analyzing paths from a standard Monte Carlo estimator and integrating these partitioned path spaces using a Markov Chain Monte Carlo (MCMC) estimator. This also means that integration happens within a sparser subset of path space, so we propose the use of guided proposal distributions in image space to improve efficiency. We show that our method improves image quality over other MCMC integration approaches at the same number of samples.

Over the last decade, the field of programmable self-assembly has seen an explosion in the diversity of crystal lattices that can be synthesized from DNA-coated colloidal nanometer- and micrometer-scale particles. The prevailing wisdom has been that a particular crystal structure can be targeted by designing the DNA-mediated interactions, to enforce binding between specific particle pairs, and the particle diameters, to control the packing of the various species. In this article, we show that other ubiquitous nonspecific interactions can play equally important roles in determining the relative stability of different crystal polymorphs and therefore what crystal structure is most likely to form in an experiment. For a binary mixture of same-sized DNA-coated colloidal micrometer-scale particles, we show how changing the magnitudes of nonspecific steric and van der Waals interactions gives rise to a family of binary body-centered tetragonal crystals, including both cesium-chloride and copper-gold crystals. Simulations using pair potentials that account for these interactions reproduce our experimental observations quantitatively, and a theoretical model reveals how a subtle balance between specific and nonspecific forces determines the equilibrium crystal structure. These results highlight the importance of accounting for nonspecific interactions in the crystal-engineering design process., Comment: Includes supplementary information

Common features of sub-Neptunes atmospheres observed to date include signatures of aerosols at moderate equilibrium temperatures (~500-800 K), and a prevalence of high mean molecular weight atmospheres, perhaps indicating novel classes of planets such as water worlds. Here we present a 0.83-5 micron JWST transmission spectrum of the sub-Neptune TOI-421 b. This planet is unique among previously observed counterparts in its high equilibrium temperature ($T_{eq} \approx 920$) and its Sun-like host star. We find marked differences between the atmosphere of TOI-421 b and those of sub-Neptunes previously characterized with JWST, which all orbit M stars. Specifically, water features in the NIRISS/SOSS bandpass indicate a low mean molecular weight atmosphere consistent with solar metallicity, and no appreciable aerosol coverage. Hints of SO$_2$ and CO (but not CO$_2$ or CH$_4$) also exist in our NIRSpec/G395M observations, but not at sufficient signal-to-noise to draw firm conclusions. Our results support a picture in which sub-Neptunes hotter than ~850 K do not form hydrocarbon hazes due to a lack of methane to photolyze. TOI-421 b additionally fits the paradigm of the radius valley for planets orbiting FGK stars being sculpted by mass loss processes, which would leave behind primordial atmospheres overlying rock/iron interiors. Further observations of TOI-421 b and similar hot sub-Neptunes will confirm whether haze-free atmospheres and low mean molecular weights are universal characteristics of such objects., Comment: Submitted to ApJ Letters, comments welcome

The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of 8x compared to double-precision in extreme compute-intensive workloads, remain largely untapped in most scientific applications. A growing number of efforts have shown that mixed-precision algorithmic innovations can deliver superior performance without sacrificing accuracy. These developments should prompt computational scientists to seriously consider whether their scientific modeling and simulation applications could benefit from the acceleration offered by new hardware and mixed-precision algorithms. In this article, we review the literature on relevant applications, existing mixed-precision algorithms, theories, and the available software infrastructure. We then offer our perspective and recommendations on the potential of mixed-precision algorithms to enhance the performance of scientific simulation applications. Broadly, we find that mixed-precision methods can have a large impact on computational science in terms of time-to-solution and energy consumption. This is true not only for a few arithmetic-dominated applications but also, to a more moderate extent, to the many memory bandwidth-bound applications. In many cases, though, the choice of algorithms and regions of applicability will be domain-specific, and thus require input from domain experts. It is helpful to identify cross-cutting computational motifs and their mixed-precision algorithms in this regard. Finally, there are new algorithms being developed to utilize AI hardware and and AI methods to accelerate first-principles computational science, and these should be closely watched as hardware platforms evolve., Comment: Submitted to IJHPCA

Indicators of Compromise (IoCs) play a crucial role in the rapid detection and mitigation of cyber threats. However, the existing body of literature lacks in-depth analytical studies on the temporal aspects of IoC publication, especially when considering up-to-date datasets related to Common Vulnerabilities and Exposures (CVEs). This paper addresses this gap by conducting an analysis of the timeliness and comprehensiveness of Cyber Threat Intelligence (CTI) pertaining to several recent CVEs. The insights derived from this study aim to enhance cybersecurity defense strategies, particularly when dealing with dynamic cyber threats that continually adapt their Tactics, Techniques, and Procedures (TTPs). Utilizing IoCs sourced from multiple providers, we scrutinize the IoC publication rate. Our analysis delves into how various factors, including the inherent nature of a threat, its evolutionary trajectory, and its observability over time, influence the publication rate of IoCs. Our preliminary findings emphasize the critical need for cyber defenders to maintain a constant state of vigilance in updating their IoCs for any given vulnerability. This vigilance is warranted because the publication rate of IoCs may exhibit fluctuations over time. We observe a recurring pattern akin to an epidemic model, with an initial phase following the public disclosure of a vulnerability characterized by sparse IoC publications, followed by a sudden surge, and subsequently, a protracted period with a slower rate of IoC publication.

DarkSide-20k is a novel liquid argon dark matter detector currently under construction at the Laboratori Nazionali del Gran Sasso (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN) that will push the sensitivity for Weakly Interacting Massive Particle (WIMP) detection into the neutrino fog. The core of the apparatus is a dual-phase Time Projection Chamber (TPC), filled with \SI{50} {tonnes} of low radioactivity underground argon (UAr) acting as the WIMP target. NUV-HD-Cryo Silicon Photomultipliers (SiPM)s designed by Fondazione Bruno Kessler (FBK) (Povo, Trento, Italy) were selected as the photon sensors covering two $10.5~\text{m}^2$ Optical Planes, one at each end of the TPC, and a total of $5~\text{m}^2$ photosensitive surface for the liquid argon veto detectors. This paper describes the Quality Assurance and Quality Control (QA/QC) plan and procedures accompanying the production of FBK~NUV-HD-Cryo SiPM wafers manufactured by LFoundry s.r.l. (Avezzano, AQ, Italy). SiPM characteristics are measured at 77~K at the wafer level with a custom-designed probe station. As of May~2024, 603 of the 1400 production wafers (43\% of the total) for DarkSide-20k were tested, including wafers from all 57 production Lots. The wafer yield is $93.6\pm2.5$\%, which exceeds the 80\% specification defined in the original DarkSide-20k production plan.

Important aspects of QCD factorization theorems are the properties of the objects involved that can be identified as universal. One example is that the definitions of parton densities and fragmentation functions for different types of hadrons differ only in the identity of the nonperturbative states that form the matrix elements, but are otherwise the same. This leads to independence of perturbative calculations on nonperturbative details of external states. It also lends support to interpretations of correlation functions as encapsulations of intrinsic nonperturbative properties. These characteristics have usually been presumed to still hold true in fragmentation functions even when the observed nonperturbative state is a small-mass cluster of $n$ hadrons rather than simply a single isolated hadron. However, the multidifferential aspect of cross sections that rely on these latter types of fragmentation functions complicates the treatment of kinematical approximations in factorization derivations. That has led to recent claims that the operator definitions for fragmentation functions need to be modified from the single hadron case with nonuniversal prefactors. With such concerns as our motivation, we retrace the steps for factorizing the unpolarized semi-inclusive $e^+e^-$ annihilation cross section and confirm that they do apply without modification to the case of a small-mass multihadron observed in the final state. In particular, we verify that the standard operator definition from single hadron fragmentation, with its usual prefactor, remains equally valid for the small-mass $n$-hadron case with the same hard parts and evolution kernels, whereas the more recently proposed definitions with nonuniversal prefactors do not. Our results reaffirm the reliability of most past phenomenological applications of dihadron fragmentation functions., Comment: 32 pages, 7 figures

We study fuzzy axion dark matter in type IIB string theory, for axions descending from the Ramond-Ramond four-form in compactifications on orientifolds of Calabi-Yau hypersurfaces. Such models can be tested by cosmological measurements if a significant relic abundance of fuzzy dark matter arises, which we argue is most common in models with small numbers of axions. We construct a topologically exhaustive ensemble of more than 350,000 Calabi-Yau compactifications yielding up to seven axions, and in this setting we perform a systematic analysis of misalignment production of fuzzy dark matter. In typical regions of moduli space, the fuzzy axion, the QCD axion, and other axions have comparable decay constants of $f_a \approx 10^{16}$ GeV. We find that overproduction of heavier axions is problematic, except at special loci in moduli space where decay constant hierarchies can occur: without a contrived reheating epoch, it is necessary to fine-tune initial displacements. The resulting dark matter is typically a mix of fuzzy axions and heavier axions, including the QCD axion. Dark photons are typically present as a consequence of the orientifold projection. We examine the signatures of these models by simulating halos with multiple fuzzy axions, and by computing new cosmological constraints on ultralight axions and dark radiation. We also give evidence that cosmic birefringence is possible in this setting. Our findings determine the phenomenological correlates of fuzzy axion dark matter in a corner of the landscape., Comment: 62 pages, 16 figures. Supplementary materials: https://github.com/sheride/fuzzy_axions

Previous Hubble Space Telescope (HST) observations of the star-forming cluster NGC 346 in the Small Magellanic Cloud (SMC) had revealed a large population of pre-main sequence (PMS) candidates, characterised by Halpha excess emission in their photometry. However, without access to spectroscopy, the nature of these objects remained unclear. Using the NIRSpec instrument on board JWST, we studied a sample of these stars, with masses in the range ~0.9-1.8 Msun, effective temperatures in the range 4,500-8,000 K, and PMS ages between ~0.1 and 30 Myr. Here we present the first spectra of solar-mass PMS stars in the metal-poor SMC (Z=1/8 Zsun) and discuss the physical properties of ten representative sources with good signal-to-noise ratio. The observations indicate that even the oldest of these PMS candidates are still accreting gas with typical rates of ~10^{-8} Msun/yr for stars older than ~10 Myr, confirming their PMS nature. The spectra also reveal near-infrared excess and molecular hydrogen excitation lines consistent with the presence of discs around these stars. These findings suggest that in a low-metallicity environment circumstellar discs can live longer than previously thought., Comment: 17 pages, 7 figures, to be published in The Astrophysical Journal, Vol 977, Article 214

It is well-known that the cores of massive stars sustain a stellar dynamo with a complex magnetic field configuration. However, the same cannot be said for the field's strength and geometry at the convective-radiative boundary, which are crucial when performing asteroseismic inference. In this Letter, we present three-dimensional (3D) magnetohydrodynamic (MHD) simulations of a 7 solar mass mid-main sequence star, with particular attention given to the convective-radiative boundary in the near-core region. Our simulations reveal that the toroidal magnetic field is significantly stronger than the poloidal field in this region, contrary to recent assumptions. Moreover, the rotational shear layer, also important for asteroseismic inference, is specifically confined within the extent of the buoyancy frequency peak. These results, which are based on the inferred properties of HD 43317, have widespread implications for asteroseismic studies of rotation, mixing and magnetism in stars. While we expect our results to be broadly applicable across stars with similar buoyancy frequency profiles and stellar masses, we also expect the MHD parameters and the initial stellar rotation rate to impact the geometry of the field and differential rotation at the convective-radiative interface.

The hardware security community relies on databases of known vulnerabilities and open-source designs to develop formal verification methods for identifying hardware security flaws. While there are plenty of open-source designs and verification tools, there is a gap in open-source properties addressing these flaws, making it difficult to reproduce prior work and slowing research. This paper aims to bridge that gap. We provide SystemVerilog Assertions for four common designs: OR1200, Hack@DAC 2018's buggy PULPissimo SoC, Hack@DAC 2019's CVA6, and Hack@DAC 2021's buggy OpenPiton SoCs. The properties are organized by design and tagged with details about the security flaws and the implicated CWE. To encourage more property reporting, we describe the methodology we use when crafting properties., Comment: 7 pages, 6 figures, 1 table, Submitted to Design Automation Conference 2025

The interface between a ferromagnetic metal and an organic molecular semiconductor, commonly referred to as a spinterface, is an important component for advancing spintronic technologies. Hybridization of the ferromagnetic-metal surface d orbitals with the molecular-semiconductor p orbitals induces profound modifications not only in the interfacial molecular layer, but also in the surface ferromagnetic-metal atomic layer. These effects are particularly pronounced at low temperatures, manifesting as substantial modifications in the magnetic properties of thin-film magnetic-metal/organic heterostructures. Despite extensive research and interest, the magnetic-ordering and magnetic-properties of the spinterface remain poorly understood. Using ultrafast time-resolved magneto-optical spectroscopy, to investigate the magnetic dynamics in such heterostructures, we unveil the unique spinterface-magnetism and its universality for a broad variety of cobalt/molecular-semiconductor interfaces. In particular, our findings demonstrate the presence of highly anisotropic low-temperature superparamagnetism at the cobalt/molecular-semiconductor spinterface. This anisotropic interfacial superparamagnetism is likely driven by strong chemical modifications in the cobalt interfacial layer caused by the chemisorbed molecular layer. These results highlight the pivotal role of molecular chemisorption in tuning the magnetic properties at spinterfaces, paving the way for future spintronic applications.

There is increasing evidence for the presence and variability of circumstellar dust and gas around white dwarfs that are polluted with exoplanetary material, although the origin of this dust and gas remains debated. This paper presents the first near-simultaneous observations of both circumstellar dust (via broadband emission) and gas (via emission lines) around a polluted white dwarf. From the optical spectra the gaseous emission lines, notably the calcium infrared triplet and magnesium lines, show significant increases and decreases in their strength over timescales of weeks, while the oxygen and iron lines remain relatively stable. Near-infrared JHKs photometry reveals dust emission changes of up to 0.2 magnitudes in the Ks band over similar timescales, marking the shortest variability timescales observed to date. The two epochs with the strongest emission were correlated between the dust (Ks band brightening) and gas (strengthened calcium and magnesium lines), showing for the first time that the dust and gas must be produced near-simultaneously with a common origin, likely in collisions., Comment: Accepted for publication in MNRAS: Letters 7 pages, 3 figures, 2 tables and 2 supplementary tables

Hydrogen emission lines are routinely used to estimate the mass accretion rate of pre-main-sequence stars. Despite the clear correlation between the accretion luminosity of a star and hydrogen line luminosities, the physical origin of these lines is still unclear, with magnetospheric accretion and magneto-centrifugal winds as the two most often invoked mechanisms. Using a combination of HST photometry and new JWST NIRSpec spectra, we have analysed the SED and emission line spectra of five sources in order to determine their underlying photospheric properties, and to attempt to reveal the physical origin of their hydrogen emission lines. These sources reside in NGC 3603, a Galactic massive star forming region. We have fitted the SED of the five sources employing a Markov Chain Monte Carlo exploration to estimate $T_{eff}$, $R_{*}$, $M_{*}$ and $A(V)$ for each source. We have performed a kinematic analysis across three spectral series of hydrogen lines, Paschen, Brackett, and Pfund, totalling $\ge 15$ lines. The FWHM and optical depth of the spectrally resolved lines have been studied in order to constrain the emission origin. The five sources all have SEDs consistent with young intermediate-mass stars. We have classified three of these sources as Herbig Ae type stars based on their effective temperature. Their hydrogen lines show broad profiles with FWHMs $\ge 200$ km s$^{-1}$. Hydrogen lines with high upper energy levels $n_{up}$ tend to be significantly broader than lines with lower $n_{up}$. The optical depth of the emission lines is also highest for the high velocity component of each line, becoming optically thin in the low velocity component. This is consistent with emission from a magnetospheric accretion flow, and cannot be explained as originating in a magneto-centrifugal wind, or other line emission mechanisms thought to be present in protoplanetary disks.

NGC 3603 is the optically brightest massive star forming region (SFR) in the Milky Way, representing a small scale starburst region. Studying young stars in regions like this allows us to assess how star and planet formation proceeds in a dense clustered environment with high levels of UV radiation. JWST provides the sensitivity, unbroken wavelength coverage, and spatial resolution required to study individual pre-main-sequence (PMS) stars in distant massive SFRs in detail for the first time. Using the Micro-Shutter Assembly (MSA) onboard the Near InfraRed Spectrograph (NIRSpec), multi-object spectroscopy was performed, yielding 100 stellar spectra. We fit the PMS spectra to derive their photospheric properties, extinction, and NIR veiling. From this, we determined the masses and ages of our sources by placing them on the Hertzsprung-Russel diagram (HRD). Their accretion rates were determined by converting the luminosity of hydrogen emission lines to an accretion luminosity. We have classified 42 as actively accreting. Our sources span a range of masses from 0.5 to 7 $M_{\odot}$. Twelve of these accreting sources have ages consistent with $\ge$ 10 Myrs, with four having ages of $\ge$ 15 Myrs. Their mass accretion rates span 5 orders of magnitude and are systematically higher for a given stellar mass than for a comparative sample taken from low-mass SFRs. We report an environmental relationship between $\dot{M}_{acc}$ and the density of ambient molecular gas as traced by nebular $H_2$ emission.