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Teacher shortages and lack of teacher diversity have led to growing efforts nationally to recruit teaching assistants (TAs) to be classroom teachers. Substitute teachers are not typically considered in these efforts. We pair longitudinal administrative data from a mid-sized urban district with survey follow-up to address how TAs and substitute teachers contribute to filling staffing shortages and diversifying the teacher workforce. While substitute teachers are rarely included in formal Grow-Your-Own efforts, they bring racial and ethnic diversity to the district in similar ways to TAs yet face fewer barriers to becoming classroom teachers. As a result, they do so at much higher rates and help prevent vacancies in hard-to-staff classrooms.

Recent observations from JWST have revealed an abundant population of active galactic nuclei (AGN) and so-called ``Little Red Dots'' (LRDs) at $2\lesssim z \lesssim 11$, many of which are characterized by V-shaped UV-to-optical continua with turnovers around the Balmer limit. The physical nature of these LRDs is unclear, and it remains debated whether the peculiar spectral shape originates from AGN, compact galaxies, or both. We present the analysis of new NIRSpec-IFU data from the BlackTHUNDER JWST Large Programme and archival NIRSpec-MSA data of a lensed LRD at $z=7.04$. The spectra confirm the presence of a smooth Balmer break and a broad H$\beta$ tracing the Broad Line Region (BLR) of an AGN. The small velocity dispersion of the H$\beta$ narrow component indicates a small dynamical mass of the host galaxy of $M_{\rm dyn}<4 \times 10^8~M_{\odot}$, which implies that the stellar population cannot contribute more than 10% to the optical continuum. We show that the Balmer break can be well described by an AGN continuum absorbed by very dense ($n_{\rm H}\sim 10^{10}~{\rm cm^{-3}}$) and nearly dust-free gas along our line-of-sight (possibly gas in the BLR or its surrounding). The same gas is expected to produce H$\beta$ absorption, at a level consistent with a tentative detection ($3\sigma$) in the high-resolution spectrum. Such a non-stellar origin of the Balmer break may apply to other LRDs, and would alleviate the issue of extremely high stellar mass surface densities inferred in the case of a stellar interpretation of the Balmer break. We note that this is a rare case of a black hole that is overmassive relative to both the host galaxy stellar and dynamical masses. We finally report indications of variability and the first attempt of AGN reverberation mapping at such an early epoch., Comment: 34 pages, 24 figures, submitted to MNRAS

Job Corps is the largest and most comprehensive education and job training program in the United States for young people ages 16 to 24 who are not in school and are not working. To deepen the Job Corps program's ability to generate and use evidence to improve the labor market trajectories of eligible young people, this report discusses ways Job Corps can meet its statutory requirement to evaluate the program, as it is operating, every five years, as well as discussing types of research and evaluation activities it can use to build evidence over time to improve outcomes for young people. The current report proposes that Job Corps alternate between two types of comprehensive studies of its operations and its effectiveness: (1) A quasi-experimental impact study analyzing existing data, paired with an implementation study based on program data and data from a center director survey; and (2) A more rigorous impact study, paired with an in-depth implementation study, to address a broader set of questions, generate more in-depth answers to those questions, and yield more rigorous impact estimates.

School districts in northeastern Tennessee have had persistently low proficiency rates in grade 3 English language arts, which were exacerbated by disruptions in schooling due to the Covid-19 pandemic. In response, the Niswonger Foundation, a technical assistance provider that supports these districts, developed Project On-Track, a high-dosage, small-group literacy tutoring program for students in grade 1-3. Its online adaptive program, Amplify Reading, groups students by skill level and generates mini-lessons aligned to the science of reading that are delivered by tutors. Although the content of the tutoring sessions is highly structured, Project On-Track offers schools flexibility in how they implement the program, including when they provide tutoring, who provides tutoring, in which grade levels they offer tutoring, and how they identify students within a grade level for tutoring. This flexibility can make it easier for schools to adopt the program, particularly rural schools, which may face greater challenges in hiring tutors or delivering tutoring outside of school hours. However, variation in implementation may also affect program effectiveness. To inform future implementation of the program, this study describes the characteristics of students who participated in a full year of Project On-Track and how schools implemented the program, with a focus on three implementation features: when and how frequently tutoring is offered and who provides it. By reporting on the association between variations in implementation and student literacy scores, the study offers important insights to inform future program implementation. The study found no differences in student literacy scores based on timing or frequency of tutoring. Most schools (66 percent) offered tutoring during school and more than twice a week (64 percent). Rural schools were more likely to offer tutoring during school (92 percent) than were nonrural schools (47 percent). Most tutors were current teachers (55 percent) or retired teachers (12 percent). This study does not provide evidence of differences in student literacy scores based on tutor qualifications. More than half the students who participated in a full year of Project On-Track tutoring started the year with literacy assessment scores identifying them as most at risk for reading difficulties, and 42 percent of them improved to a lower risk category after one year of tutoring. Although this study uses descriptive methods and cannot assess effectiveness, the findings suggest that schools and districts using a highly structured tutoring program like Project On-Track might be able to exercise flexibility in when and how often tutoring is offered and by whom without compromising program quality and benefits to students.

In 1974, Erd\H{o}s asked the following question: given a graph $G$ and a directed graph $\vec{H}$, how many ways are there to orient the edges of $G$ such that it does not contain $\vec{H}$ as a subgraph. We denote this value by $D(G, \vec{H})$. Further, we let $D(n, \vec{H})$ denote the maximum of $D(G, \vec{H})$ over all graphs $G$ on $n$ vertices. In 2006, Alon and Yuster gave an exact answer when $\vec{H}$ is a tournament. In 2023, Buci\'c, Janzer, and Sudakov gave asymptotic answers for all directed graphs $\vec{H}$, and in the same paper, they gave an exact answer when $\vec{H}$ is a directed cycle. In this paper, we give a better bound for some specific non-bipartite directed graphs. Further, we obtain exact values of $D(G, \vec{H})$ for some small non-edge-critical directed graphs $\vec{H}$. Finally, for these graphs, we classify all graphs $G$ that attain the bound $D(G, \vec{H}) = D(n, \vec{H})$., Comment: 35 pages

We present JWST NIRSpec integral field spectroscopy observations of the z=5.89 quasar NDWFS J1425+3254 from 0.6-5.3 microns, covering the rest-frame ultraviolet and optical at spectral resolution R~100. The quasar has a black hole mass of $M_{\rm{BH}}=(1.4\substack{+3.1\\-1.0})\times10^9 M_\odot$ and an Eddington ratio $L_{\rm{Bol}}/L_{\rm{Edd}}=0.3\substack{+0.6\\-0.2}$, as implied from the broad Balmer H$\alpha$ and H$\beta$ lines. We find that the quasar host has significant ongoing obscured star formation, as well as a quasar-driven outflow with velocity $6050\substack{+460\\-630}$ km/s, possibly one of the most extreme outflows in the early Universe. The data also reveals that two companion galaxies are merging with the quasar host. The north-eastern companion galaxy is relatively old and very massive, with luminosity-weighted stellar age $65\substack{+9\\-4}$ Myr, stellar mass $(3.6\substack{+0.6\\-0.3})\times10^{11} M_\odot$, and SFR $< (16.1\pm1.9) M_\odot$/yr. A bridge of gas connects this companion galaxy and the host, confirming their ongoing interaction. A second merger is occurring between the quasar host and a much younger companion galaxy to the south, with stellar age $6.7\pm1.8$ Myr, stellar mass $(1.9\pm0.4)\times10^{10} M_\odot$, and SFR $< (93\substack{+17\\-16}) M_\odot$/yr. There is also another galaxy in the field that is likely in the foreground at z=1.135, that could be gravitationally lensing the quasar with magnification $1<\mu<2$, and so <0.75 mag. Overall, the system is a "train-wreck" merger of three galaxies, with star formation and extreme quasar activity that were likely triggered by these ongoing interactions., Comment: Submitted to A&A

The atmospheres of planets between the size of Earth and Neptune at short orbital periods have been under intense scrutiny. Of the ~dozen planets in this regime with atmospheres studied so far, a few appear to have prominent molecular features while others appear relatively void of detectable atmospheres. Further work is therefore needed to understand the atmospheres of these planets, starting with observing a larger sample. To this end, we present the 3-5 micron transmission spectrum of TOI-776 c, a warm (Teq ~420 K), ~2 Rearth, ~7 Mearth planet orbiting an M1V star, measured with JWST NIRSpec/G395H. By combining two visits, we measure a median transit precision of ~18 ppm and ~32 ppm in the NRS1 and NRS2 detectors, respectively. We compare the transmission spectrum to both non-physical and physical models, and find no strong evidence for molecular features. For cloud-top pressures larger than 10^-3 bar, we rule out atmospheric metallicities less than 180-240x solar (depending on the reduction and modeling technique), which corresponds to a mean molecular weight of ~6-8 g/mol. However, we find simple atmosphere mixture models (H2O+H2/He or CO2+H2/He) give more pessimistic constraints, and caution that mean molecular weight inferences are model dependent. We compare TOI-776 c to the similar planet TOI-270 d, and discuss possible options for further constraining TOI-776 c's atmospheric composition. Overall, we suggest these TOI-776 c observations may represent a combination of planetary and stellar parameters that fall just below the threshold of detectable features in small planet spectra; finding this boundary is one of the main goals of the COMPASS program., Comment: 21 pages, 15 figures, 4 tables. Accepted for publication in AJ

Large language models (LLMs) are known to perpetuate stereotypes and exhibit biases. Various strategies have been proposed to mitigate potential harms that may result from these biases, but most work studies biases in LLMs as a black-box problem without considering how concepts are represented within the model. We adapt techniques from representation engineering to study how the concept of "gender" is represented within LLMs. We introduce a new method that extracts concept representations via probability weighting without labeled data and efficiently selects a steering vector for measuring and manipulating the model's representation. We also present a projection-based method that enables precise steering of model predictions and demonstrate its effectiveness in mitigating gender bias in LLMs.

Large Language Models (LLMs) have shown remarkable potential in recommending everyday actions as personal AI assistants, while Explainable AI (XAI) techniques are being increasingly utilized to help users understand why a recommendation is given. Personal AI assistants today are often located on ultra-small devices such as smartwatches, which have limited screen space. The verbosity of LLM-generated explanations, however, makes it challenging to deliver glanceable LLM explanations on such ultra-small devices. To address this, we explored 1) spatially structuring an LLM's explanation text using defined contextual components during prompting and 2) presenting temporally adaptive explanations to users based on confidence levels. We conducted a user study to understand how these approaches impacted user experiences when interacting with LLM recommendations and explanations on ultra-small devices. The results showed that structured explanations reduced users' time to action and cognitive load when reading an explanation. Always-on structured explanations increased users' acceptance of AI recommendations. However, users were less satisfied with structured explanations compared to unstructured ones due to their lack of sufficient, readable details. Additionally, adaptively presenting structured explanations was less effective at improving user perceptions of the AI compared to the always-on structured explanations. Together with users' interview feedback, the results led to design implications to be mindful of when personalizing the content and timing of LLM explanations that are displayed on ultra-small devices.

Elemental abundances in the solar corona differ from those in the photosphere, with low first ionization potential (FIP) elements being enhanced, a phenomenon known as the FIP effect. This enhancement is attributed to ponderomotive forces linked to magnetohydrodynamic (MHD) waves, particularly incompressible transverse waves. Our study investigates the relationship between coronal abundance fractionation and chromospheric transverse MHD waves by examining the spatial correlation between FIP fractionation and these waves and by analyzing their properties to test the ponderomotive force model. We used H alpha data from the Fast Imaging Solar Spectrograph at the Goode Solar Telescope to detect chromospheric transverse MHD waves and \ion{Si}{X} (low FIP) and \ion{S}{X} (high FIP) spectra from Hinode EUV Imaging Spectrometer to determine relative abundances in an active region. Extrapolated linear force free magnetic fields from Solar Dynamics Observatory/Helioseismic and Magnetic Imager magnetograms further linked the observed chromospheric waves with coronal composition. Approximately 400 wave packets were identified and characterized by their period, velocity amplitude, propagation speed, and direction. These incompressible or weakly compressible waves were mainly observed near loop footpoints in the sunspot penumbra and superpenumbral fibrils. Regions of high FIP fractionation coincided with closed magnetic fields where these waves were present, and low-frequency, downward-propagating waves comprised about 43/% of the total. Our results demonstrate a strong correlation between coronal abundance fractionation and chromospheric transverse MHD waves, supporting the view that the FIP effect is driven by the ponderomotive force from these waves., Comment: 13 pages, 10 figures, submitted to A&A

Scientific research is inherently global. However, the vast majority of academic journals are published exclusively in English, creating barriers for non-native-English-speaking researchers. In this study, we leverage large language models (LLMs) to translate published scientific articles while preserving their native JATS XML formatting, thereby developing a practical, automated approach for implementation by academic journals. Using our approach, we translate articles across multiple scientific disciplines into 28 languages. To evaluate translation accuracy, we introduce a novel question-and-answer (QA) benchmarking method, in which an LLM generates comprehension-based questions from the original text and then answers them based on the translated text. Our benchmark results show an average performance of 95.9%, showing that the key scientific details are accurately conveyed. In a user study, we translate the scientific papers of 15 researchers into their native languages, finding that the authors consistently found the translations to accurately capture the original information in their articles. Interestingly, a third of the authors found many technical terms "overtranslated," expressing a preference to keep terminology more familiar in English untranslated. Finally, we demonstrate how in-context learning techniques can be used to align translations with domain-specific preferences such as mitigating overtranslation, highlighting the adaptability and utility of LLM-driven scientific translation. The code and translated articles are available at https://hankleid.github.io/ProjectMundo.

We study the one-dimensional Fermi gas subject to dissipative reactions. The dynamics is governed by the quantum master equation, where the Hamiltonian describes coherent motion of the particles, while dissipation accounts for irreversible reactions. For lattice one-dimensional fermionic systems, emergent critical behavior has been found in the dynamics in the reaction-limited regime of weak dissipation. Here, we address the question whether such features are present also in a gas in continuum space. We do this in the weakly dissipative regime by applying the time-dependent generalized Gibbs ensemble method. We show that for two body $2A\to \emptyset$ and three $3A\to \emptyset$ body annihilation, as well as for coagulation $A+A\to A$, the density features an asymptotic algebraic decay in time akin to the lattice problem. In all the cases, we find that upon increasing the temperature of the initial state the density decay accelerates, but the asymptotic algebraic decay exponents are not affected. We eventually consider the competition between branching $A\to A+A$ and the decay processes $A\to \emptyset$ and $2A\to \emptyset$. We find a second-order absorbing-state phase transition in the mean-field directed percolation universality class. This analysis shows that emergent behavior observed in lattice quantum reaction-diffusion systems is present also in continuum space, where it may be probed using ultra-cold atomic physics., Comment: 36 pages, 11 figures

Video compression plays a pivotal role in managing and transmitting large-scale display data, particularly given the growing demand for higher resolutions and improved video quality. This paper proposes an optimized memory system architecture for Video Electronics Standards Association (VESA) Display Compression-M (VDC-M) decoder, characterized by its substantial on-chip buffer requirements. We design and analyze three architectures categorized by optimization levels and management complexity. Our strategy focuses on enhancing line buffer access scheduling and minimizing reconstruction buffer, targeting prediction and multi-slice operation that are the major resource consumers in the decoder. By adjusting line delay and segmenting SRAM bank alongside reconstructed block forwarding, we achieve a 33.3% size reduction in the line buffer and 77.3% in the reconstruction buffer compared to Baseline VDC-M decoder. Synthesized using a 28 nm CMOS process, the proposed architecture achieves a 31.5% reduction in gate count of the decoder backend hardware, supporting real-time performance with up to 96.45 fps for 4K UHD resolution at 200 MHz operating frequency and a throughput of 4 pixels per cycle., Comment: Accepted by 2025 IEEE International Symposium on Circuits and Systems (ISCAS)

The UltraViolet EXplorer (UVEX) is a wide-field ultraviolet space telescope selected as a NASA Medium-Class Explorer (MIDEX) mission for launch in 2030. UVEX will undertake deep, cadenced surveys of the entire sky to probe low mass galaxies and explore the ultraviolet (UV) time-domain sky, and it will carry the first rapidly deployable UV spectroscopic capability for a broad range of science applications. One of UVEX's prime objectives is to follow up gravitational wave (GW) binary neutron star mergers as targets of opportunity (ToOs), rapidly scanning across their localization regions to search for their kilonova (KN) counterparts. Early-time multiband ultraviolet light curves of KNe are key to explaining the interplay between jet and ejecta in binary neutron star mergers. Owing to high Galactic extinction in the ultraviolet and the variation of GW distance estimates over the sky, the sensitivity to kilonovae can vary significantly across the GW localization and even across the footprint of a single image given UVEX's large field of view. Good ToO observing strategies to trade off between area and depth are neither simple nor obvious. We present an optimal strategy for GW follow-up with UVEX in which exposure time is adjusted dynamically for each field individually to maximize the overall probability of detection. We model the scheduling problem using the expressive and powerful mathematical framework of mixed integer linear programming (MILP), and employ a state-of-the-art MILP solver to automatically generate observing plan timelines that achieve high probabilities of kilonova detection. We have implemented this strategy in an open-source astronomical scheduling software package called the Multi-Mission Multi-Messenger Observation Planning Toolkit (M4OPT), on GitHub at https://github.com/m4opt/m4opt., Comment: For data release, see https://zenodo.org/records/14825979

Purpose: T2* quantification from gradient echo magnetic resonance imaging is particularly affected by subject motion due to the high sensitivity to magnetic field inhomogeneities, which are influenced by motion and might cause signal loss. Thus, motion correction is crucial to obtain high-quality T2* maps. Methods: We extend our previously introduced learning-based physics-informed motion correction method, PHIMO, by utilizing acquisition knowledge to enhance the reconstruction performance for challenging motion patterns and increase PHIMO's robustness to varying strengths of magnetic field inhomogeneities across the brain. We perform comprehensive evaluations regarding motion detection accuracy and image quality for data with simulated and real motion. Results: Our extended version of PHIMO outperforms the learning-based baseline methods both qualitatively and quantitatively with respect to line detection and image quality. Moreover, PHIMO performs on-par with a conventional state-of-the-art motion correction method for T2* quantification from gradient echo MRI, which relies on redundant data acquisition. Conclusion: PHIMO's competitive motion correction performance, combined with a reduction in acquisition time by over 40% compared to the state-of-the-art method, make it a promising solution for motion-robust T2* quantification in research settings and clinical routine., Comment: Under Review

Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This study examines driver perceptions, knowledge sources, and usage patterns of ADAS in passenger vehicles. A nationwide survey collected data from a diverse sample of U.S. drivers. Machine learning models predicted ADAS adoption, with SHAP (SHapley Additive Explanations) identifying key influencing factors. Findings indicate that higher trust levels correlate with increased ADAS usage, while concerns about reliability remain a barrier. Specific features, such as Forward Collision Warning and Driver Monitoring Systems, significantly influence adoption likelihood. Demographic factors (age, gender) and driving habits (experience, frequency) also shape ADAS acceptance. Findings emphasize the influence of socioeconomic, demographic, and behavioral factors on ADAS adoption, offering guidance for automakers, policymakers, and safety advocates to improve awareness, trust, and usability.

Large Language Models (LLMs) are becoming increasingly capable across global languages. However, the ability to communicate across languages does not necessarily translate to appropriate cultural representations. A key concern is US-centric bias, where LLMs reflect US rather than local cultural values. We propose a novel methodology that compares LLM-generated response distributions against population-level opinion data from the World Value Survey across four languages (Danish, Dutch, English, and Portuguese). Using a rigorous linear mixed-effects regression framework, we compare two families of models: Google's Gemma models (2B--27B parameters) and successive iterations of OpenAI's turbo-series. Across the families of models, we find no consistent relationships between language capabilities and cultural alignment. While the Gemma models have a positive correlation between language capability and cultural alignment across languages, the OpenAI models do not. Importantly, we find that self-consistency is a stronger predictor of multicultural alignment than multilingual capabilities. Our results demonstrate that achieving meaningful cultural alignment requires dedicated effort beyond improving general language capabilities.

Machine learning methods are commonly used to solve inverse problems, wherein an unknown signal must be estimated from few measurements generated via a known acquisition procedure. In particular, neural networks perform well empirically but have limited theoretical guarantees. In this work, we study an underdetermined linear inverse problem that admits several possible solution mappings. A standard remedy (e.g., in compressed sensing) establishing uniqueness of the solution mapping is to assume knowledge of latent low-dimensional structure in the source signal. We ask the following question: do deep neural networks adapt to this low-dimensional structure when trained by gradient descent with weight decay regularization? We prove that mildly overparameterized deep linear networks trained in this manner converge to an approximate solution that accurately solves the inverse problem while implicitly encoding latent subspace structure. To our knowledge, this is the first result to rigorously show that deep linear networks trained with weight decay automatically adapt to latent subspace structure in the data under practical stepsize and weight initialization schemes. Our work highlights that regularization and overparameterization improve generalization, while overparameterization also accelerates convergence during training.

Since the discovery of unconventional superconductivity in cuprates, unraveling the pairing mechanism of charge carriers in doped antiferromagnets has been a long-standing challenge. Motivated by the discovery of high-T$_c$ superconductivity in nickelate bilayer La$_3$Ni$_2$O$_7$ (LNO), we study a minimal mixed dimensional (MixD) $t-J$ model supplemented with a repulsive Coulomb interaction $V$. When hole-doped, previous numerical simulations revealed that the system exhibits strong binding energies, with a phenomenology resembling a BCS-to-BEC crossover accompanied by a Feshbach resonance between two distinct types of charge carriers. Here, we perform a mean-field analysis that enables a direct observation of the BCS-to-BEC crossover as well as microscopic insights into the crossover region and the pairing symmetry for two-dimensional bilayers. We benchmark our mean-field description by comparing it to density-matrix renormalization group (DMRG) simulations in quasi-one dimensional settings and find remarkably good agreement. For the two-dimensional system relevant to LNO our mean-field calculations predict a BCS pairing gap with an extended $s$-wave symmetry, directly resulting from the pairing mechanism's Feshbach-origin. Our analysis hence gives insights into pairing in unconventional superconductors and, further, can be tested in currently available ultracold atom experiments., Comment: 14 pages, 7 figures

We present an analysis of millimeter CO observations to search and quantify signatures of molecular gas outflows. We exploit the large sample of $0.5 < z < 2.6$ galaxies observed as part of the PHIBSS1/2 surveys with the IRAM Plateau de Bure interferometer, focusing on the 154 typical massive star-forming galaxies with CO detections (mainly CO(3-2), but including also CO(2-1) and CO(6-5)) at signal-to-noise (SNR) > 1.5 and available properties (stellar mass, star formation rate, size) from ancillary data. None of the individual spectra exhibit a compelling signature of CO outflow emission even at high SNR > 7. To search for fainter outflow signatures, we carry out an analysis of stacked spectra, including the full sample, as well as subsets, split in terms of stellar mass, redshift, inclination, offset in star formation rate (SFR) from the main sequence, and AGN activity. None of the physically motivated subsamples show any outflow signature. We report a tentative detection in a subset statistically designed to maximize outflow signatures. We derive upper limits on molecular gas outflow rate and mass loading factors $\eta$ based on our results and find $\eta \leq$ 2.2-35.4, depending on the subsample. Much deeper CO data and observations of alternative tracers are needed to decisively constrain the importance of cold molecular gas component of outflows relative to other gas phases., Comment: 19 pages, 8 figures. Submitted to ApJ

Nature uses elongated shapes and filaments to build stable structures, generate motion, and allow complex geometric interactions. In this Review, we examine the role of biological filaments across different length scales. From the molecular scale, where cytoskeletal filaments provides a robust but dynamic cellular scaffolding, over the scale of cellular appendages like cilia and flagella, to the scale of filamentous microorganisms like cyanobacteria which are among the most successful genera on Earth, and even to the scale of elongated animals like worms and snakes, whose motility modes inspire robotic analogues. We highlight the general mechanisms that couple form and function. Physical principles, such as classical elasticity and the non-reciprocity of active matter can be used to trace unifying themes linking these systems spanning about six orders of magnitude in length.

The shape of the atomic nucleus is a property which underpins our understanding of nuclear systems, impacts the limits of nuclear existence, and enables probes of physics beyond the Standard Model. Nuclei can adopt a variety of shapes, including spheres, axially deformed spheroids, and pear shapes. In some regions of the nuclear chart where a spherical nucleus would naively be expected, deformed nuclear states can result from collective action of constituent protons and neutrons. In a small subset of nuclei both spherical and deformed nuclear states have been experimentally observed, a phenomenon termed shape coexistence. We present spectroscopic evidence for the coexistence of $J^{\pi}=1+$ spherical and deformed states in $^{70}$Co, separated by less than 275~keV. This close degeneracy of levels with the same $J^{\pi}$ and different shapes demonstrates an extreme example of shape coexistence resulting from the interplay of independent particle motion and collective behavior in highly unstable nuclear systems and identifies the Co isotopes as a transition point between deformed ground states observed in the Cr isotopes and spherical configurations observed in the closed-shell Ni isotopes.

The unexpectedly high nitrogen-to-oxygen (N/O) ratios observed in high-redshift (z) galaxies have challenged our understanding of early star formation. Notably, many of these nitrogen-rich galaxies show signatures of active galactic nuclei (AGNs), suggesting a possible connection between black hole formation and nitrogen enrichment. To explore this connection, we analyse stacked spectra of z=4-7 broad-line and narrow-line AGNs using deep NIRSpec data from the JADES survey. We identify a significant Niii] quintuplet and a high electron density ($\sim10^{4}\,\mathrm{cm^{-3}}$) only in the broad-line AGN stack, indicating nitrogen-rich ($\log(\mathrm{N/C})\simeq0.5$, $\log(\mathrm{N/O})>-0.6$) and dense gas similar to the high-z nitrogen-rich galaxies. Our findings suggest that dense nuclear star formation may trap nitrogen-rich gas in proto-globular clusters, in line with the high N/O observed in local globular clusters; associated runaway stellar collisions could produce intermediate-mass black hole seeds, as predicted by some models and simulations, whose accretion results into AGN signatures. These findings support scenarios connecting the early black hole seeding and growth to merging processes within and between proto-globular clusters in primeval galaxies., Comment: Submitted to MNRAS Letter

Photoacoustic (PA) waves are strongly distorted and attenuated in skull bone. To study these effects on PA imaging, we designed and 3D-printed tissue-mimicking phantoms of human skull. We present a comparison of results in phantom and ex vivo skull., Comment: submitted to European Conferences on Biomedical Optics (2025); Open Data at doi.org/10.5281/zenodo.14876160

This paper examines the legal implications of the explicit mentioning of automation bias (AB) in the Artificial Intelligence Act (AIA). The AIA mandates human oversight for high-risk AI systems and requires providers to enable awareness of AB, i.e., the tendency to over-rely on AI outputs. The paper analyses how this extra-juridical concept is embedded in the AIA, the division of responsibility between AI providers and deployers, and the challenges of legally enforcing this novel awareness requirement. The analysis shows that the AIA's focus on providers does not adequately address design and context as causes of AB, and questions whether the AIA should directly regulate the risk of AB rather than just mandating awareness. As the AIA's approach requires a balance between legal mandates and behavioural science, the paper proposes that harmonised standards should reference the state of research on AB and human-AI interaction. Ultimately, further empirical research will be essential for effective safeguards.

From crop mapping to flood detection, machine learning in remote sensing has a wide range of societally beneficial applications. The commonalities between remote sensing data in these applications present an opportunity for pretrained machine learning models tailored to remote sensing to reduce the labeled data and effort required to solve individual tasks. However, such models must be: (i) flexible enough to ingest input data of varying sensor modalities and shapes (i.e., of varying spatial and temporal dimensions), and (ii) able to model Earth surface phenomena of varying scales and types. To solve this gap, we present Galileo, a family of pretrained remote sensing models designed to flexibly process multimodal remote sensing data. We also introduce a novel and highly effective self-supervised learning approach to learn both large- and small-scale features, a challenge not addressed by previous models. Our Galileo models obtain state-of-the-art results across diverse remote sensing tasks.

Combinatorial pooling schemes have enabled the measurement of thousands of experiments in a small number of reactions. This efficiency is achieved by distributing the items to be measured across multiple reaction units called pools. However, current methods for the design of pooling schemes do not adequately address the need for balanced item distribution across pools, a property particularly important for biological applications. Here, we introduce balanced constant-weight Gray codes for detecting consecutive positives (DCP-CWGCs) for the efficient construction of combinatorial pooling schemes. Balanced DCP-CWGCs ensure uniform item distribution across pools, allow for the identification of consecutive positive items such as overlapping biological sequences, and enable error detection by keeping the number of tests on individual and consecutive positive items constant. For the efficient construction of balanced DCP-CWGCs, we have released an open-source python package codePub, with implementations of the two core algorithms: a branch-and-bound algorithm (BBA) and a recursive combination with BBA (rcBBA). Simulations using codePub show that our algorithms can construct long, balanced DCP-CWGCs that allow for error detection in tractable runtime.

Context and Motivation: Due to their increasing complexity, everyday software systems are becoming increasingly opaque for users. A frequently adopted method to address this difficulty is explainability, which aims to make systems more understandable and usable. Question/problem: However, explanations can also lead to unnecessary cognitive load. Therefore, adapting explanations to the actual needs of a user is a frequently faced challenge. Principal ideas/results: This study investigates factors influencing users' preferred the level of detail and the form of an explanation (e.g., short text or video tutorial) in software. We conducted an online survey with 58 participants to explore relationships between demographics, software usage, app-specific knowledge, as well as their preferred explanation form and level of detail. The results indicate that users prefer moderately detailed explanations in short text formats. Correlation analyses revealed no relationship between app-specific knowledge and the preferred level of detail of an explanation, but an influence of demographic aspects (like gender) on app-specific knowledge and its impact on application confidence were observed, pointing to a possible mediated relationship between knowledge and preferences for explanations. Contribution: Our results show that explanation preferences are weakly influenced by app-specific knowledge but shaped by demographic and psychological factors, supporting the development of adaptive explanation systems tailored to user expertise. These findings support requirements analysis processes by highlighting important factors that should be considered in user-centered methods such as personas., Comment: This paper has been accepted at the research track of the 31st International Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2025)

Context and Motivation: The increasing complexity of modern software systems often challenges users' abilities to interact with them. Taking established quality attributes such as usability and transparency into account can mitigate this problem, but often do not suffice to completely solve it. Recently, explainability has emerged as essential non-functional requirement to help overcome the aforementioned difficulties. Question/problem: User preferences regarding the integration of explanations in software differ. Neither too few nor too many explanations are helpful. In this paper, we investigate the influence of a user's subjective mood and objective demographic aspects on explanation needs by means of frequency and type of explanation. Principal ideas/results: Our results reveal a limited relationship between these factors and explanation needs. Two significant correlations were identified: Emotional reactivity was positively correlated with the need for UI explanations, while a negative correlation was found between age and user interface needs. Contribution: As we only find very few significant aspects that influence the need for explanations, we conclude that the need for explanations is very subjective and does only partially depend on objective factors. These findings emphasize the necessity for software companies to actively gather user-specific explainability requirements to address diverse and context-dependent user demands. Nevertheless, future research should explore additional personal traits and cross-cultural factors to inform the development of adaptive, user-centered explanation systems., Comment: This paper has been accepted at the research track of the 31st International Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2025)

We derive a family of $L^p$ estimates of the X-Ray transform of positive measures in $\mathbb R^d$, which we use to construct a $\log R$-loss counterexample to the Mizohata-Takeuchi conjecture for every $C^2$ hypersurface in $\mathbb R^d$ that does not lie in a hyperplane. In particular, multilinear restriction estimates at the endpoint cannot be sharpened directly by the Mizohata-Takeuchi conjecture.

Ensuring a continuous and growing influx of skilled chip designers and a smooth path from education to innovation are key goals for several national and international "Chips Acts". Silicon democratization can greatly benefit from end-to-end (from silicon technology to software) free and open-source (OS) platforms. We present Croc, an extensible RISC-V microcontroller platform explicitly targeted at hands-on teaching and innovation. Croc features a streamlined OS synthesis and an end-to-end OS implementation flow, ensuring full, unconstrained access to the design, the design automation tools, and the implementation technology. Croc uses the industry-proven, open-source CVE2 core, implementing the RV32I(EMC) instruction set architecture (ISA), enabling students to define and implement their own ISA extensions. MLEM, a tapeout of Croc in IHP's open 130 nm node completed in eight weeks by a team of just two students, demonstrates the platform's viability for hands-on teaching in schools, universities, or even on a self-education path. In spring 2025, ETH Zurich will utilize Croc for its curricular VLSI class, involving up to 80 students, producing up to 40 OS application-specific integrated circuit layouts, and completing up to five student-led system-on-chip tapeouts. The lecture notes and exercises are already available under a Creative Commons license., Comment: 2 pages, 1 figure, submitted to the RISC-V Summit Europe 2025 for possible publication

Modern embodied artificial agents excel in static, predefined tasks but fall short in dynamic and long-term interactions with humans. On the other hand, humans can adapt and evolve continuously, exploiting the situated knowledge embedded in their environment and other agents, thus contributing to meaningful interactions. We introduce the concept of co-existence for embodied artificial agents and argues that it is a prerequisite for meaningful, long-term interaction with humans. We take inspiration from biology and design theory to understand how human and non-human organisms foster entities that co-exist within their specific niches. Finally, we propose key research directions for the machine learning community to foster co-existing embodied agents, focusing on the principles, hardware and learning methods responsible for shaping them.

Superconducting, curved magnets can reduce accelerator footprints by producing strong fields (>3T) and reducing the total number of magnets through their capability for combined-function multipolar fields, making them an attractive choice for applications such as heavy ion therapy. There exists the problem that the effect of strongly curved harmonics and fringe fields on compact accelerator beam dynamics is not well represented: existing approaches use integrated cylindrical multipoles to describe and model the fields for beam dynamics studies, which are invalid in curved coordinate systems and assume individual errors cancel out over the full machine. In the modelling of these machines, the effect of strongly curved harmonics and fringe fields on compact accelerator beam dynamics needs to properly included. An alternative approach must be introduced for capturing off-axis fields in a strongly curved magnet, which may affect long-term beam stability in a compact accelerator. In this article, we investigate the impacts of deploying a curved canted-cosine-theta (CCT) superconducting magnet in a compact medical synchrotron for the first time. We develop a method to analyse and characterise the 3D curved fields of an electromagnetic model of a CCT developed for the main bending magnets of a 27m circumference carbon ion therapy synchrotron, designed within the Heavy Ion Therapy Research Integration Plus European project, and the CERN Next Ion Medical Machine Study (NIMMS). The fields are modelled in the compact synchrotron in MAD-X/PTC to study their effects on beam dynamics and long-term beam stability. The insights gained through the methods presented allow for the optimisation of both magnet and synchrotron designs, with the potential to impact the operational performance of future ion therapy facilities., Comment: 14 Pages, 31 figures

The estimation of uncertainties associated with predictions from quantitative structure-activity relationship (QSAR) models can accelerate the drug discovery process by identifying promising experiments and allowing an efficient allocation of resources. Several computational tools exist that estimate the predictive uncertainty in machine learning models. However, deviations from the i.i.d. setting have been shown to impair the performance of these uncertainty quantification methods. We use a real-world pharmaceutical dataset to address the pressing need for a comprehensive, large-scale evaluation of uncertainty estimation methods in the context of realistic distribution shifts over time. We investigate the performance of several uncertainty estimation methods, including ensemble-based and Bayesian approaches. Furthermore, we use this real-world setting to systematically assess the distribution shifts in label and descriptor space and their impact on the capability of the uncertainty estimation methods. Our study reveals significant shifts over time in both label and descriptor space and a clear connection between the magnitude of the shift and the nature of the assay. Moreover, we show that pronounced distribution shifts impair the performance of popular uncertainty estimation methods used in QSAR models. This work highlights the challenges of identifying uncertainty quantification methods that remain reliable under distribution shifts introduced by real-world data.

We consider a population with two types of individuals, distinguished by the resources required for reproduction: type-$0$ (small) individuals need a fractional resource unit of size $\vartheta \in (0,1)$, while type-$1$ (large) individuals require $1$ unit. The total available resource per generation is $R$. To form a new generation, individuals are sampled one by one, and if enough resources remain, they reproduce, adding their offspring to the next generation. The probability of sampling an individual whose offspring is small is $\rho_{R}(x)$, where $x$ is the proportion of small individuals in the current generation. This discrete-time stochastic model is called the two-size Wright-Fisher model, where the function $\rho_{R}$ can represent mutation and/or frequency-dependent selection. We show that on the evolutionary time scale, i.e. accelerating time by a factor $R$, the frequency process of type-$0$ individuals converges to the solution of a Wright-Fisher-type SDE. The drift term of that SDE accounts for the bias introduced by the function $\rho_R$ and the consumption strategy, the latter also inducing an additional multiplicative factor in the diffusion term. To prove this, the dynamics within each generation are viewed as a renewal process, with the population size corresponding to the first passage time $\tau(R)$ above level $R$. The proof relies on methods from renewal theory, in particular a uniform version of Blackwell's renewal theorem for binary, non-arithmetic random variables, established via $\varepsilon$-coupling., Comment: 24 pages, 7 figures

We prove that the quadratically enriched count of rational curves in a smooth toric del Pezzo surface passing through $k$-rational points and pairs of conjugate points in quadratic field extensions $k\subset k(\sqrt{d_i})$ can be determined by counting certain tropical stable maps through vertically stretched point conditions with a suitable multiplicity. Building on the floor diagram technique in tropical geometry, we provide an algorithm to compute these numbers. Our tropical algorithm computes not only these new quadratically enriched enumerative invariants, but simultaneously also the complex Gromov-Witten invariant, the real Welschinger invariant counting curves satisfying real point conditions only, the real Welschinger invariant of curves satisfying pairs of complex conjugate and real point conditions, and the quadratically enriched count of curves satisfying $k$-rational point conditions., Comment: 75 pages (fullpage format), 10 figures, 5 tables. V2 minor revision and corrected typos

Humans strive to design safe AI systems that align with our goals and remain under our control. However, as AI capabilities advance, we face a new challenge: the emergence of deeper, more persistent relationships between humans and AI systems. We explore how increasingly capable AI agents may generate the perception of deeper relationships with users, especially as AI becomes more personalised and agentic. This shift, from transactional interaction to ongoing sustained social engagement with AI, necessitates a new focus on socioaffective alignment-how an AI system behaves within the social and psychological ecosystem co-created with its user, where preferences and perceptions evolve through mutual influence. Addressing these dynamics involves resolving key intrapersonal dilemmas, including balancing immediate versus long-term well-being, protecting autonomy, and managing AI companionship alongside the desire to preserve human social bonds. By framing these challenges through a notion of basic psychological needs, we seek AI systems that support, rather than exploit, our fundamental nature as social and emotional beings.

We present a comprehensive analysis of galaxy close-pair fractions and major merger rates to evaluate the importance of mergers in the hierarchical growth of galaxies over cosmic time. This study focuses on the previously poorly understood redshift range of $z \approx 3-9$ using JADES observations. Our mass-complete sample includes primary galaxies with stellar masses of ${\rm log}(M_\star/{\rm M_\odot}) = [8, 10]$, having major companions (mass ratio $\geq 1/4$) selected by $5-30$ pkpc projected separation and redshift proximity criteria. Pair fractions are measured using a statistically robust method incorporating photometric redshift posteriors and available spectroscopic data. The pair fraction evolves steeply with redshift, peaking at $z \sim 5-6$, followed by a turnover, and shows dependence on the stellar mass: the pair fraction peaks at later cosmic times (lower redshifts) for more massive galaxies. Similarly, the derived galaxy major merger rate increases and flattens beyond $z \sim 6$ to $2-10~{\rm Gyr^{-1}}$ per galaxy, showing a weak scaling with stellar mass, driven by the evolution of the galaxy stellar mass function. A comparison between the cumulative mass accretion from major mergers and the mass assembled through star formation indicates that major mergers contribute approximately $5-14\%$ to the total mass growth over the studied redshift range, which is in agreement with the ex-situ mass fraction estimated from our simple numerical model. These results highlight that major mergers contribute little to the direct stellar mass growth compared to in-situ star formation but could still play an indirect role by driving star formation itself., Comment: 32 pages, 24 figures, 4 tables. Submitted to MNRAS. Comments are welcome

Constraint-based metabolic models can be used to investigate the intracellular physiology of microorganisms. These models couple genes to reactions, and typically seek to predict metabolite fluxes that optimize some biologically important metric. Classical techniques, like Flux Balance Analysis (FBA), formulate the metabolism of a microbe as an optimization problem where growth rate is maximized. While FBA has found widespread use, it often leads to thermodynamically infeasible solutions that contain internal cycles (loops). To address this shortcoming, Loopless-Flux Balance Analysis (ll-FBA) seeks to predict flux distributions that do not contain these loops. ll-FBA is a disjunctive program, usually reformulated as a mixed-integer program, and is challenging to solve for biological models that often contain thousands of reactions and metabolites. In this paper, we compare various reformulations of ll-FBA and different solution approaches. %We discuss the use of intersection cuts and compare the performance of blocking cycles to decomposing the problem and to solving the convex hull formulation. Overall, the combinatorial Benders' decomposition is the most promising of the tested approaches with which we could solve most instances. However, the model size and numerical instability pose a challenge to the combinatorial Benders' method.

Quality-Diversity is a family of evolutionary algorithms that generate diverse, high-performing solutions through local competition principles inspired by natural evolution. While research has focused on improving specific aspects of Quality-Diversity algorithms, surprisingly little attention has been paid to investigating alternative formulations of local competition itself -- the core mechanism distinguishing Quality-Diversity from traditional evolutionary algorithms. Most approaches implement local competition through explicit collection mechanisms like fixed grids or unstructured archives, imposing artificial constraints that require predefined bounds or hard-to-tune parameters. We show that Quality-Diversity methods can be reformulated as Genetic Algorithms where local competition occurs through fitness transformations rather than explicit collection mechanisms. Building on this insight, we introduce Dominated Novelty Search, a Quality-Diversity algorithm that implements local competition through dynamic fitness transformations, eliminating the need for predefined bounds or parameters. Our experiments show that Dominated Novelty Search significantly outperforms existing approaches across standard Quality-Diversity benchmarks, while maintaining its advantage in challenging scenarios like high-dimensional and unsupervised spaces.

The recent detections of a large number of candidate active galactic nuclei at high redshift (i.e. $z \gtrsim 4$) has increased speculation that heavy seed massive black hole formation may be a required pathway. Here we re-implement the so-called Lyman-Werner (LW) channel model of Dijkstra et al. (2014) to calculate the expected number density of massive black holes formed through this channel. We further enhance this model by extracting information relevant to the model from the $\texttt{Renaissance}$ simulation suite. $\texttt{Renaissance}$ is a high-resolution suite of simulations ideally positioned to probe the high-$z$ universe. Finally, we compare the LW-only channel against other models in the literature. We find that the LW-only channel results in a peak number density of massive black holes of approximately $\rm{10^{-4} \ cMpc^{-3}}$ at $z \sim 10$. Given the growth requirements and the duty cycle of active galactic nuclei, this means that the LW-only is likely incompatible with recent JWST measurements and can, at most, be responsible for only a small subset of high-$z$ active galactic nuclei. Other models from the literature seem therefore better positioned, at present, to explain the high frequency of massive black holes at high $z$., Comment: 12 pages, 8 figures, comments welcome!

The measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult, leading to what has been described as "a tangle of sloppy tests [and] apples-to-oranges comparisons" (Roose, 2024). In this position paper, we argue that the ML community would benefit from learning from and drawing on the social sciences when developing and using measurement instruments for evaluating GenAI systems. Specifically, our position is that evaluating GenAI systems is a social science measurement challenge. We present a four-level framework, grounded in measurement theory from the social sciences, for measuring concepts related to the capabilities, behaviors, and impacts of GenAI. This framework has two important implications for designing and evaluating evaluations: First, it can broaden the expertise involved in evaluating GenAI systems by enabling stakeholders with different perspectives to participate in conceptual debates. Second, it brings rigor to both conceptual and operational debates by offering a set of lenses for interrogating the validity of measurement instruments and their resulting measurements.

Agentic Artificial Intelligence (AI) can autonomously pursue long-term goals, make decisions, and execute complex, multi-turn workflows. Unlike traditional generative AI, which responds reactively to prompts, agentic AI proactively orchestrates processes, such as autonomously managing complex tasks or making real-time decisions. This transition from advisory roles to proactive execution challenges established legal, economic, and creative frameworks. In this paper, we explore challenges in three interrelated domains: creativity and intellectual property, legal and ethical considerations, and competitive effects. Central to our analysis is the tension between novelty and usefulness in AI-generated creative outputs, as well as the intellectual property and authorship challenges arising from AI autonomy. We highlight gaps in responsibility attribution and liability that create a "moral crumple zone"--a condition where accountability is diffused across multiple actors, leaving end-users and developers in precarious legal and ethical positions. We examine the competitive dynamics of two-sided algorithmic markets, where both sellers and buyers deploy AI agents, potentially mitigating or amplifying tacit collusion risks. We explore the potential for emergent self-regulation within networks of agentic AI--the development of an "algorithmic society"--raising critical questions: To what extent would these norms align with societal values? What unintended consequences might arise? How can transparency and accountability be ensured? Addressing these challenges will necessitate interdisciplinary collaboration to redefine legal accountability, align AI-driven choices with stakeholder values, and maintain ethical safeguards. We advocate for frameworks that balance autonomy with accountability, ensuring all parties can harness agentic AI's potential while preserving trust, fairness, & societal welfare.

Astronomical radio interferometers achieve exquisite angular resolution by cross-correlating signal from a cosmic source simultaneously observed by distant pairs of radio telescopes to produce a Fourier-type measurement called a visibility. Million Points of Light (MPoL) is a Python library supporting feed-forward modeling of interferometric visibility datasets for synthesis imaging and parametric Bayesian inference, built using the autodifferentiable machine learning framework PyTorch. Neural network components provide a rich set of modular and composable building blocks that can be used to express the physical relationships between latent model parameters and observed data following the radio interferometric measurement equation. Industry-grade optimizers make it straightforward to simultaneously solve for the synthesized image and calibration parameters using stochastic gradient descent., Comment: Submitted to JOSS. 1559 words, 1 figure

The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports a collaborative effort performed by ten teams of students, each guided by a senior researcher as their mentor, attending ISWS 2023. Each team provided a different perspective to the topic of creative AI, substantiated by a set of research questions as the main subject of their investigation. The 2023 edition of ISWS focuses on the intersection of Semantic Web technologies and Creative AI. ISWS 2023 explored various intersections between Semantic Web technologies and creative AI. A key area of focus was the potential of LLMs as support tools for knowledge engineering. Participants also delved into the multifaceted applications of LLMs, including legal aspects of creative content production, humans in the loop, decentralised approaches to multimodal generative AI models, nanopublications and AI for personal scientific knowledge graphs, commonsense knowledge in automatic story and narrative completion, generative AI for art critique, prompt engineering, automatic music composition, commonsense prototyping and conceptual blending, and elicitation of tacit knowledge. As Large Language Models and semantic technologies continue to evolve, new exciting prospects are emerging: a future where the boundaries between creative expression and factual knowledge become increasingly permeable and porous, leading to a world of knowledge that is both informative and inspiring., Comment: Technical Report

Plants respond to biotic and abiotic stresses through complex and dynamic mechanisms that integrate physical, chemical, and biological cues. Here, we present a multi-physics platform designed to systematically investigate these responses across scales. The platform combines a six-axis micromanipulator with interchangeable probes to deliver precise mechanical, electrostatic, optical, and chemical stimuli. Using this system, we explore calcium signaling in Arabidopsis thaliana, thigmonastic motion in Mimosa pudica, and chemical exchange via microinjection in Rosmarinus officinalis L. and Ocimum basilicum. Our findings highlight stimulus-specific and spatially dependent responses: mechanical and electrostatic stimuli elicit distinct calcium signaling patterns, while repeated electrostatic stimulation exhibited evidence of response fatigue. Thigmonastic responses in Mimosa pudica depend on the location of perturbation, highlighting the intricate bi-directional calcium signaling. Microinjection experiments successfully demonstrate targeted chemical perturbations in glandular trichomes, opening avenues for biochemical studies. This open-source platform provides a versatile tool for dissecting plant stress responses, bridging the gap between fundamental research and applied technologies in agriculture and bioengineering. By enabling precise, scalable, and reproducible studies of plant-environment interactions, this work offers new insights into the mechanisms underlying plant resilience and adaptability.

The realm of technology frequently confronts threats posed by adversaries exploiting loopholes in programs. Among these, the Log4Shell vulnerability in the Log4j library stands out due to its widespread impact. Log4j, a prevalent software library for log recording, is integrated into millions of devices worldwide. The Log4Shell vulnerability facilitates remote code execution with relative ease. Its combination with the extensive utilization of Log4j marks it as one of the most dangerous vulnerabilities discovered to date. The severity of this vulnerability, which quickly escalated into a media frenzy, prompted swift action within the industry, thereby mitigating potential extensive damage. This rapid response was crucial, as the consequences could have been significantly more severe if the vulnerability had been exploited by adversaries prior to its public disclosure. This paper details the discovery of the Log4Shell vulnerability and its potential for exploitation. It examines the vulnerability's impact on various stakeholders, including governments, the Apache Software Foundation (which manages the Log4j library), and companies affected by it. The paper also describes strategies for defending against Log4Shell in several scenarios. While numerous Log4j users acted promptly to safeguard their systems, the vulnerability remains a persistent threat until all vulnerable instances of the library are adequately protected.

We present measurements of the afterglow signatures in NaI(Tl) and CsI(Tl) detector modules as part of the Background and Transient Observer (BTO) mission detector trade-study. BTO is a NASA Student Collaboration Project flying on the Compton Spectrometer and Imager (COSI) Small Explorer mission in 2027. The detectors utilized in this study are cylindrical in shape with a height and diameter of 5.1 cm and were read out by silicon photomultipliers (SiPMs). We conducted a radiation campaign at the HIMAC accelerator in Japan where the scintillators were irradiated with a 230 MeV/u helium beam (He beam) and 350 MeV/u carbon beam (C beam). We find that both the CsI and NaI scintillators exhibit afterglow signatures when irradiated with the C and He beams. The CsI crystal exhibits a stronger afterglow intensity with afterglow pulses occurring for an average 2.40 ms for C and 0.9 ms for He after the initial particle pulse. The duration of afterglow pulses in CsI is 8.6x and 5.6x the afterglow signal duration in NaI for C and He (0.28 ms and 0.16 ms, respectively). Although CsI has advantages such as a higher light yield and radiation hardness, the stronger afterglows in the CsI detector increase the complexity of the electronics and lead to a ~7x larger dead time per afterglow event or a ~3x higher energy threshold value. We use the measured dead times to predict the amount of observing time lost to afterglow-inducing events for an instrument like BTO in low Earth orbit. We simulate the background rates in a BTO-like orbit and find a total value of 114 counts/s for the full two-detector system. Based on the particle energies in the HIMAC experiment, we then determine that an event with sufficient energy to produce an afterglow signal occurs once every ~70 s and ~1.4 s in NaI and CsI detectors, respectively. Thus, we conclude that NaI is the better choice for the BTO mission., Comment: 11 pages, 6 figures, 2 tables