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The neighborhood, whether reestablished in the city or in a new town, at its best avoids not only racial and income segregation, but also age segregation. Since the three-generation family within a single dwelling no longer is likely, the neighborhood should be restored to accommodate all generations. This study explores how the concept of neighborhood can be reestablished and how it can become a three-generation neighborhood. (Author/MLF)

We discuss our transient search for directed energy systems in local galaxies, with calculations indicating the ability of modest searches to detect optical Search for Extraterrestrial Intelligence (SETI) sources in the closest galaxies. Our analysis follows Lubin (2016) where a messenger civilization follows a beacon strategy we call "intelligent targeting." We plot the required laser time to achieve an SNR of 10 and find the time for a blind transmission to target all stars in the Milky Way to be achievable for local galactic civilizations. As high cadence and sky coverage is the pathway to enable such a detection, we operate the Local Galactic Transient Survey (LGTS) targeting M31 (the Andromeda Galaxy), the Large Magellanic Cloud (LMC), and the Small Magellanic Cloud (SMC) via Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. We explore the ability of modest searches like the LGTS to detect directed pulses in optical and near-infrared wavelengths from Extraterrestrial Intelligence (ETI) at these distances and conclude a civilization utilizing less powerful laser technology than we can construct in this century is readily detectable with the LGTS's observational capabilities. Data processing of 30,000 LGTS images spanning 5 years is in progress with the TRansient Image Processing Pipeline (TRIPP; Thomas et al. (2025))., Comment: 8 pages, 7 figures, 1 table

We present the TRansient Image Processing Pipeline (TRIPP), a transient and variable source detection pipeline that employs both difference imaging and light curve analysis techniques for astronomical data. Additionally, we demonstrate TRIPP's rapid analysis capability by detecting transient candidates in near-real time. TRIPP was tested using image data of the supernova SN2023ixf and from the Local Galactic Transient Survey (LGTS) collected by the Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. To verify the methods employed by TRIPP, we compare our results to published findings on the photometry of SN2023ixf. Additionally, we report the ability of TRIPP to detect transient signals from optical Search for Extra Terrestrial Intelligence (SETI) sources., Comment: 11 pages, 10 figures, 2 tables

We analyze the asymptotic number and typical structure of claw-free graphs at constant edge densities. The first of our main results is a formula for the asymptotics of the logarithm of the number of claw-free graphs of edge density $\gamma \in (0,1)$. We show that the problem exhibits a second-order phase transition at edge density $\gamma^\ast=\frac{5-\sqrt{5}}{4}$. The asymptotic formula arises by solving a variational problem over graphons. For $\gamma\geq\gamma^\ast$ there is a unique optimal graphon, while for $\gamma<\gamma^\ast$ there is an infinite set of optimal graphons. By analyzing more detailed structure, we prove that for $\gamma<\gamma^\ast$, there is in fact a unique graphon $W$ such that almost all claw-free graphs at edge density $\gamma$ are close in cut metric to $W$. We also analyze the probability of claw-freeness in the Erd\H{o}s-R\'enyi random graph $G(n,p)$ for constant $p$, obtaining a formula for the large-deviation rate function for claw-freeness. In this case, the problem exhibits a first-order phase transition at $p^\ast=\frac{3-\sqrt{5}}{2}$, separating distinct structural regimes. At the critical point $p^\ast$, the corresponding graphon variational problem has infinitely many solutions, and we again pinpoint a unique optimal graphon that describes the typical structure of $G(n,p^\ast)$ conditioned on being claw-free.

A next-generation medium-energy gamma-ray telescope targeting the MeV range would address open questions in astrophysics regarding how extreme conditions accelerate cosmic-ray particles, produce relativistic jet outflows, and more. One concept, AMEGO-X, relies upon the mission-enabling CMOS Monolithic Active Pixel Sensor silicon chip AstroPix. AstroPix is designed for space-based use, featuring low noise, low power consumption, and high scalability. Desired performance of the device include an energy resolution of 5 keV (or 10% FWHM) at 122 keV and a dynamic range per-pixel of 25-700 keV, enabled by the addition of a high-voltage bias to each pixel which supports a depletion depth of 500 um. This work reports on the status of the AstroPix development process with emphasis on the current version under test, version three (v3), and highlights of version two (v2). Version 3 achieves energy resolution of 10.4 +\- 3.2 % at 59.5 keV and 94 +\- 6 um depletion in a low-resistivity test silicon substrate., Comment: 20 pages, 13 figures

Medium-timescale (minutes to hours) radio transients are a relatively unexplored population. The wide field-of-view and high instantaneous sensitivity of instruments such as MeerKAT provides an opportunity to probe this class of sources, using image-plane detection techniques. The previous letter in this series describes our project and associated TRON pipeline designed to mine archival MeerKAT data for transient and variable sources. In this letter, we report on a new transient, a radio flare, associated with Gaia DR3 6865945581361480448, a G type star, whose parallax places it at a distance of 1334 pc. Its duration and high degree of circular polarization suggests electron cyclotron maser instability as the mechanism, consistent with an RS CVn variable., Comment: 4 pages, 4 figures, submitted to MNRAS Letters

Medium-timescale (minutes to hours) radio transients are a relatively unexplored population. The wide field-of-view and high instantaneous sensitivity of instruments such as MeerKAT provides an opportunity to probe this class of sources, using image-plane detection techniques. We aim to systematically mine archival synthesis imaging data in order to search for medium-timescale transients and variables that are not detected by conventional long-track image synthesis techniques. We deploy a prototype blind transient and variable search pipeline named TRON. This processes calibrated visibility data, constructs high-time cadence images, performs a search for variability on multiple timescales, and extracts lightcurves for detected sources. As proof of concept, we apply it to three MeerKAT observations of globular clusters, known to host transient or variable sources. We detect a previously known eclipsing MSP suspected to be a `black widow' system, in the globular cluster Omega Centauri, with a light curve confirming the eclipsing nature of the emission. We detect a previously known `red back' eclipsing MSP in the globular cluster Terzan 5. Using observations of the globular cluster 47 Tucanae, we detect two known millisecond pulsars (MSPs), and one previously reported MSP candidate, with hints of eclipsing behaviour., Comment: 6 pages, 8 figures, submitted to MNRAS Letters

Recent advances in Generative AI (GenAI) are transforming multiple aspects of society, including education and foreign language learning. In the context of English as a Foreign Language (EFL), significant research has been conducted to investigate the applicability of GenAI as a learning aid and the potential negative impacts of new technologies. Critical questions remain about the future of AI, including whether improvements will continue at such a pace or stall and whether there is a true benefit to implementing GenAI in education, given the myriad costs and potential for negative impacts. Apart from the ethical conundrums that GenAI presents in EFL education, there is growing consensus that learners and teachers must develop AI literacy skills to enable them to use and critically evaluate the purposes and outputs of these technologies. However, there are few formalised frameworks available to support the integration and development of AI literacy skills for EFL learners. In this article, we demonstrate how the use of a general, all-purposes framework (the AI Assessment Scale) can be tailored to the EFL writing and translation context, drawing on existing empirical research validating the scale and adaptations to other contexts, such as English for Academic Purposes. We begin by engaging with the literature regarding GenAI and EFL writing and translation, prior to explicating the use of three levels of the updated AIAS for structuring EFL writing instruction which promotes academic literacy and transparency and provides a clear framework for students and teachers.

New radio interferometers such as MeerKAT, SKA, ngVLA, and DSA-2000 drive advancements in software for two key reasons. First, handling the vast data from these instruments requires subdivision and multi-node processing. Second, their improved sensitivity, achieved through better engineering and larger data volumes, demands new techniques to fully exploit it. This creates a critical challenge in radio astronomy software: pipelines must be optimized to process data efficiently, but unforeseen artefacts from increased sensitivity require ongoing development of new techniques. This leads to a trade-off among (1) performance, (2) flexibility, and (3) ease-of-development. Rigid designs often miss the full scope of the problem, while temporary research code is unsuitable for production. This work introduces a framework for developing radio astronomy techniques while balancing the above trade-offs. It prioritizes flexibility and ease-of-development alongside acceptable performance by leveraging Open Source data formats and software. To manage growing data volumes, data is distributed across multiple processors and nodes for parallel processing, utilizing HPC and cloud infrastructure. We present two Python libraries, Dask-MS and Codex Africanus, which enable distributed, high-performance radio astronomy software with Dask. Dask is a lightweight parallelization and distribution framework that integrates with the PyData ecosystem, addressing the "Big Data" challenges of radio astronomy., Comment: 20 pages, submitted to Astronomy & Computing

Background: The rapid development and use of generative AI (GenAI) tools in academia presents complex and multifaceted ethical challenges for its users. Earlier research primarily focused on academic integrity concerns related to students' use of AI tools. However, limited information is available on the impact of GenAI on academic research. This study aims to examine the ethical concerns arising from the use of GenAI across different phases of research and explores potential strategies to encourage its ethical use for research purposes. Methods: We selected one or more GenAI platforms applicable to various research phases (e.g. developing research questions, conducting literature reviews, processing data, and academic writing) and analysed them to identify potential ethical concerns relevant for that stage. Results: The analysis revealed several ethical concerns, including a lack of transparency, bias, censorship, fabrication (e.g. hallucinations and false data generation), copyright violations, and privacy issues. These findings underscore the need for cautious and mindful use of GenAI. Conclusions: The advancement and use of GenAI are continuously evolving, necessitating an ongoing in-depth evaluation. We propose a set of practical recommendations to support researchers in effectively integrating these tools while adhering to the fundamental principles of ethical research practices.

Stimela2 is a new-generation framework for developing data reduction workflows. It is designed for radio astronomy data but can be adapted for other data processing applications. Stimela2 aims at the middle ground between ease of development, human readability, and enabling robust, scalable and reproducible workflows. It represents workflows by linear, concise and intuitive YAML-format "recipes". Atomic data reduction tasks (binary executables, Python functions and code, and CASA tasks) are described by YAML-format "cab definitions" detailing each task's "schema" (inputs and outputs). Stimela2 provides a rich syntax for chaining tasks together, and encourages a high degree of modularity: recipes may be nested into other recipes, and configuration is cleanly separated from recipe logic. Tasks can be executed natively or in isolated environments using containerization technologies such as Apptainer. The container images are open-source and maintained through a companion package called cult-cargo. This enables the development of system-agnostic and fully reproducible workflows. Stimela2 facilitates the deployment of scalable, distributed workflows by interfacing with the Slurm scheduler and the Kubernetes API. The latter allows workflows to be readily deployed in the cloud. Previous papers in this series used Stimela2 as the underlying technology to run workflows on the AWS cloud. This paper presents an overview of Stimela2's design, architecture and use in the radio astronomy context., Comment: 26 pages, submitted to Astronomy & Computing

Calibration of radio interferometer data ought to be a solved problem; it has been an integral part of data reduction for some time. However, as larger, more sensitive radio interferometers are conceived and built, the calibration problem grows in both size and difficulty. The increasing size can be attributed to the fact that the data volume scales quadratically with the number of antennas in an array. Additionally, new instruments may have up to two orders of magnitude more channels than their predecessors. Simultaneously, increasing sensitivity is making calibration more challenging: low-level RFI and calibration artefacts (in the resulting images) which would previously have been subsumed by the noise may now limit dynamic range and, ultimately, the derived science. It is against this backdrop that we introduce QuartiCal: a new Python package implementing radio interferometric calibration routines. QuartiCal improves upon its predecessor, CubiCal, in terms of both flexibility and performance. Whilst the same mathematical framework - complex optimization using Wirtinger derivatives - is in use, the approach has been refined to support arbitrary length chains of parameterized gain terms. QuartiCal utilizes Dask, a library for parallel computing in Python, to express calibration as an embarrassingly parallel task graph. These task graphs can (with some constraints) be mapped onto a number of different hardware configurations, allowing QuartiCal to scale from running locally on consumer hardware to a distributed, cloud-based cluster. QuartiCal's qualitative behaviour is demonstrated using MeerKAT observations of PSR J2009-2026. These qualitative results are followed by an analysis of QuartiCal's performance in terms of wall time and memory footprint for a number of calibration scenarios and hardware configurations., Comment: 22 pages, submitted to Astronomy & Computing

The popularity of the CLEAN algorithm in radio interferometric imaging stems from its maturity, speed, and robustness. While many alternatives have been proposed in the literature, none have achieved mainstream adoption by astronomers working with data from interferometric arrays operating in the big data regime. This lack of adoption is largely due to increased computational complexity, absence of mature implementations, and the need for astronomers to tune obscure algorithmic parameters. This work introduces pfb-imaging: a flexible library that implements the scaffolding required to develop and accelerate general radio interferometric imaging algorithms. We demonstrate how the framework can be used to implement a sparsity-based image reconstruction technique known as (unconstrained) SARA in a way that scales with image size rather than data volume and features interpretable algorithmic parameters. The implementation is validated on terabyte-sized data from the MeerKAT telescope, using both a single compute node and Amazon Web Services computing instances., Comment: 29 pages, submitted to Astronomy & Computing

We explore the potential experimental realization of the mixed-spin Kitaev model in materials such as Zr$_{0.5}$Ru$_{0.5}$Cl$_3$, where spin-1/2 and spin-3/2 ions occupy distinct sublattices of a honeycomb lattice. By developing a superexchange theory specifically for this mixed-spin system, we identify the conditions under which dominant Kitaev-like interactions emerge. Focusing on the limiting case of pure Kitaev coupling with single-ion anisotropy, we employ a combination of superexchange theory, parton mean-field theory, and density matrix renormalization group (DMRG) simulations. We establish a comprehensive ground-state phase diagram identifying four distinct quantum spin liquid phases. Our findings highlight the importance of spin-orbital couplings and quadrupolar order parameters in stabilizing exotic phases, providing a foundation for exploring mixed-spin Kitaev magnets., Comment: 13 pages, 6 figures

Recent developments in Generative Artificial Intelligence (GenAI) have created significant uncertainty in education, particularly in terms of assessment practices. Against this backdrop, we present an updated version of the AI Assessment Scale (AIAS), a framework with two fundamental purposes: to facilitate open dialogue between educators and students about appropriate GenAI use and to support educators in redesigning assessments in an era of expanding AI capabilities. Grounded in social constructivist principles and designed with assessment validity in mind, the AIAS provides a structured yet flexible approach that can be adapted across different educational contexts. Building on implementation feedback from global adoption across both the K-12 and higher education contexts, this revision represents a significant change from the original AIAS. Among these changes is a new visual guide that moves beyond the original traffic light system and utilises a neutral colour palette that avoids implied hierarchies between the levels. The scale maintains five distinct levels of GenAI integration in assessment, from "No AI" to "AI Exploration", but has been refined to better reflect rapidly advancing technological capabilities and emerging pedagogical needs. This paper presents the theoretical foundations of the revised framework, provides detailed implementation guidance through practical vignettes, and discusses its limitations and future directions. As GenAI capabilities continue to expand, particularly in multimodal content generation, the AIAS offers a starting point for reimagining assessment design in an era of disruptive technologies.

While autoregressive machine-learning-based emulators have been trained to produce stable and accurate rollouts in the climate of the present-day and recent past, none so far have been trained to emulate the sensitivity of climate to substantial changes in CO$_2$ or other greenhouse gases. As an initial step we couple the Ai2 Climate Emulator version 2 to a slab ocean model (hereafter ACE2-SOM) and train it on output from a collection of equilibrium-climate physics-based reference simulations with varying levels of CO$_2$. We test it in equilibrium and non-equilibrium climate scenarios with CO$_2$ concentrations seen and unseen in training. ACE2-SOM performs well in equilibrium-climate inference with both in-sample and out-of-sample CO$_2$ concentrations, accurately reproducing the emergent time-mean spatial patterns of surface temperature and precipitation change with CO$_2$ doubling, tripling, or quadrupling. In addition, the vertical profile of atmospheric warming and change in extreme precipitation rates up to the 99.9999th percentile closely agree with the reference model. Non-equilibrium-climate inference is more challenging. With CO$_2$ increasing gradually at a rate of 2% year$^{-1}$, ACE2-SOM can accurately emulate the global annual mean trends of surface and lower-to-middle atmosphere fields but produces unphysical jumps in stratospheric fields. With an abrupt quadrupling of CO$_2$, ML-controlled fields transition unrealistically quickly to the 4xCO$_2$ regime. In doing so they violate global energy conservation and exhibit unphysical sensitivities of and surface and top of atmosphere radiative fluxes to instantaneous changes in CO$_2$. Future emulator development needed to address these issues should improve its generalizability to diverse climate change scenarios., Comment: 31 pages, 13 figures

Many questions posed in the Astro2020 Decadal survey in both the New Messengers and New Physics and the Cosmic Ecosystems science themes require a gamma-ray mission with capabilities exceeding those of existing (e.g. Fermi, Swift) and planned (e.g. COSI) observatories. ComPair, the Compton Pair telescope, is a prototype of such a next-generation gamma-ray mission. It had its inaugural balloon flight from Ft. Sumner, New Mexico in August 2023. To continue the goals of the ComPair project to develop technologies that will enable a future gamma-ray mission, the next generation of ComPair (ComPair-2) will be upgraded to increase the sensitivity and low-energy transient capabilities of the instrument. These advancements are enabled by AstroPix, a silicon monolithic active pixel sensor, in the tracker and custom dual-gain silicon photomultipliers and front-end electronics in the calorimeter. This effort builds on design work for the All-sky Medium Energy Gamma-ray Observatory eXplorer (AMEGO-X) concept that was submitted the 2021 MIDEX Announcement of Opportunity. Here we describe the ComPair-2 prototype design and integration and testing plans to advance the readiness level of these novel technologies., Comment: 11 pages, 7 figures, SPIE Astronomical Telescopes and Instrumentation 2004 conference proceedings

Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the potential of Large Language Models (LLMs), specifically GPT4, in generating patient-friendly educational content from clinical trial informed consent forms. Using data from ClinicalTrials.gov, we employed zero-shot learning for creating trial summaries and one-shot learning for developing multiple-choice questions, evaluating their effectiveness through patient surveys and crowdsourced annotation. Results showed that GPT4-generated summaries were both readable and comprehensive, and may improve patients' understanding and interest in clinical trials. The multiple-choice questions demonstrated high accuracy and agreement with crowdsourced annotators. For both resource types, hallucinations were identified that require ongoing human oversight. The findings demonstrate the potential of LLMs "out-of-the-box" to support the generation of clinical trial education materials with minimal trial-specific engineering, but implementation with a human-in-the-loop is still needed to avoid misinformation risks.

We study the probability that the random graph $G(n,p)$ is triangle-free. When $p =o(n^{-1/2})$ or $p = \omega(n^{-1/2})$ the asymptotics of the logarithm of this probability are known via Janson's inequality in the former case and via regularity or hypergraph container methods in the latter case. We prove for the first time an asymptotic formula for the logarithm of this probability when $p = c n^{-1/2}$ for $c$ a sufficiently small constant. More generally, we study lower-tail large deviations for triangles in random graphs: the probability that $G(n,p)$ has at most $\eta$ times its expected number of triangles, when $p = c n^{-1/2}$ for $c$ and $\eta \in [0,1)$ constant. Our results apply for all $c$ if $\eta \ge .4993$ and for $c$ small enough otherwise. For $\eta$ small (including the case of triangle-freeness), we prove that a phase transition occurs as $c$ varies, in the sense of a non-analyticity of the rate function, while for $\eta \ge .4993$ we prove that no phase transition occurs. On the other hand for the random graph $G(n,m)$, with $m = b n^{3/2}$, we show that a phase transition occurs in the lower-tail problem for triangles as $b$ varies for \emph{every} $\eta \in [0,1)$. Our method involves ingredients from algorithms and statistical physics including the cluster expansion and concentration inequalities for contractive Markov chains.

Measures of textual similarity and divergence are increasingly used to study cultural change. But which measures align, in practice, with social evidence about change? We apply three different representations of text (topic models, document embeddings, and word-level perplexity) to three different corpora (literary studies, economics, and fiction). In every case, works by highly-cited authors and younger authors are textually ahead of the curve. We don't find clear evidence that one representation of text is to be preferred over the others. But alignment with social evidence is strongest when texts are represented through the top quartile of passages, suggesting that a text's impact may depend more on its most forward-looking moments than on sustaining a high level of innovation throughout., Comment: Accepted CHR 2024

As educational institutions grapple with teaching students about increasingly complex Artificial Intelligence (AI) systems, finding effective methods for explaining these technologies and their societal implications remains a major challenge. This study proposes a methodological approach combining Conceptual Metaphor Theory (CMT) with UNESCO's AI competency framework to develop Critical AI Literacy (CAIL). Through a systematic analysis of metaphors commonly used to describe AI systems, we develop criteria for selecting pedagogically appropriate metaphors and demonstrate their alignment with established AI literacy competencies, as well as UNESCO's AI competency framework. Our method identifies and suggests four key metaphors for teaching CAIL. This includes GenAI as an echo chamber, GenAI as a funhouse mirror, GenAI as a black box magician, and GenAI as a map. Each of these seeks to address specific aspects of understanding characteristics of AI, from filter bubbles to algorithmic opacity. We present these metaphors alongside interactive activities designed to engage students in experiential learning of AI concepts. In doing so, we offer educators a structured approach to teaching CAIL that bridges technical understanding with societal implications. This work contributes to the growing field of AI education by demonstrating how carefully selected metaphors can make complex technological concepts more accessible while promoting critical engagement with AI systems.

Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator version 2) and its application to reproducing atmospheric variability over the past 80 years on timescales from days to decades. ACE2 is a 450M-parameter autoregressive machine learning emulator, operating with 6-hour temporal resolution, 1{\deg} horizontal resolution and eight vertical layers. It exactly conserves global dry air mass and moisture and can be stepped forward stably for arbitrarily many steps with a throughput of about 1500 simulated years per wall clock day. ACE2 generates emergent phenomena such as tropical cyclones, the Madden Julian Oscillation, and sudden stratospheric warmings. Furthermore, it accurately reproduces the atmospheric response to El Ni\~no variability and global trends of temperature over the past 80 years. However, its sensitivities to separately changing sea surface temperature and carbon dioxide are not entirely realistic., Comment: 31 pages, 23 figures

Pirogov--Sinai theory is a well-developed method for understanding the low-temperature phase diagram of statistical mechanics models on lattices. Motivated by physical and algorithmic questions beyond the setting of lattices, we develop a combinatorially flexible version of Pirogov--Sinai theory for the hard-core model of independent sets. Our results illustrate that the main conclusions of Pirogov--Sinai theory can be obtained in significantly greater generality than that of $\mathbb Z^{d}$. The main ingredients in our generalization are combinatorial and involve developing appropriate definitions of contours based on the notion of cycle basis connectivity. This is inspired by works of Tim\'{a}r and Georgakopoulos--Panagiotis.

This scoping review examines the current body of knowledge at the intersection of Generative Artificial Intelligence (GenAI) and Self-Directed Learning (SDL). By synthesising the findings from 18 studies published from 2020 to 2024 and following the PRISMA-SCR guidelines for scoping reviews, we developed four key themes. This includes GenAI as a Potential Enhancement for SDL, The Educator as a GenAI Guide, Personalisation of Learning, and Approaching with Caution. Our findings suggest that GenAI tools, including ChatGPT and other Large Language Models (LLMs) show promise in potentially supporting SDL through on-demand, personalised assistance. At the same time, the literature emphasises that educators are as important and central to the learning process as ever before, although their role may continue to shift as technologies develop. Our review reveals that there are still significant gaps in understanding the long-term impacts of GenAI on SDL outcomes, and there is a further need for longitudinal empirical studies that explore not only text-based chatbots but also emerging multimodal applications.

A next-generation medium-energy (100 keV to 100 MeV) gamma-ray observatory will greatly enhance the identification and characterization of multimessenger sources in the coming decade. Coupling gamma-ray spectroscopy, imaging, and polarization to neutrino and gravitational wave detections will develop our understanding of various astrophysical phenomena including compact object mergers, supernovae remnants, active galactic nuclei and gamma-ray bursts. An observatory operating in the MeV energy regime requires technologies that are capable of measuring Compton scattered photons and photons interacting via pair production. AstroPix is a monolithic high voltage CMOS active pixel sensor which enables future gamma-ray telescopes in this energy range. AstroPix's design is iterating towards low-power (~1.5 mW/cm$^{2}$), high spatial (500 microns pixel pitch) and spectral (<5 keV at 122 keV) tracking of photon and charged particle interactions. Stacking planar arrays of AstroPix sensors in three dimensions creates an instrument capable of reconstructing the trajectories and energies of incident gamma rays over large fields of view. A prototype multi-layered AstroPix instrument, called the AstroPix Sounding rocket Technology dEmonstration Payload (A-STEP), will test three layers of AstroPix quad chips in a suborbital rocket flight. These quad chips (2x2 joined AstroPix sensors) form the 4x4 cm$^{2}$ building block of future large area AstroPix instruments, such as ComPair-2 and AMEGO-X. This payload will be the first demonstration of AstroPix detectors operated in a space environment and will demonstrate the technology's readiness for future astrophysical and nuclear physics applications. In this work, we overview the design and state of development of the ASTEP payload., Comment: 11 pages, 10 figures, SPIE Astronomical Telescopes and Instrumentation 2004 conference proceedings

This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available through the lens of Critical Digital Pedagogy. Following PRISMA-ScR guidelines, we collected 10 studies from academic databases focusing on both learner and teacher agency in GenAI-enabled environments. We conducted an AI-supported hybrid thematic analysis that revealed three key themes: Control in Digital Spaces, Variable Engagement and Access, and Changing Notions of Agency. The findings suggest that while GenAI may enhance learner agency through personalization and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts. This review highlights gaps in the current research on GenAI's impact on agency. These findings have implications for educational policy and practice, suggesting the need for frameworks that promote equitable access while preserving learner agency in GenAI-enhanced educational environments.

Observations of polarization position angle ($\theta$) standards made from 2014 to 2023 with the High Precision Polarimetric Instrument (HIPPI) and other HIPPI-class polarimeters in both hemispheres are used to investigate their variability. Multi-band data were first used to thoroughly recalibrate the instrument performance by bench-marking against carefully selected literature data. A novel Co-ordinate Difference Matrix (CDM) approach - which combines pairs of points - was then used to amalgamate monochromatic ($g^\prime$ band) observations from many observing runs and re-determine $\theta$ for 17 standard stars. The CDM algorithm was then integrated into a fitting routine and used to establish the impact of stellar variability on the measured position angle scatter. The approach yields variability detections for stars on long time scales that appear stable over short runs. The best position angle standards are $\ell$ Car, $o$ Sco, HD 154445, HD 161056 and $\iota^1$ Sco which are stable to $\leq$ 0.123$^\circ$. Position angle variability of 0.27-0.82$^\circ$, significant at the 3-$\sigma$ level, is found for 5 standards, including the Luminous Blue Variable HD 160529 and all but one of the other B/A-type supergiants (HD 80558, HD 111613, HD 183143 and 55 Cyg), most of which also appear likely to be variable in polarization magnitude ($p$) - there is no preferred orientation for the polarization in these objects, which are all classified as $\alpha$ Cygni variables. Despite this we make six key recommendations for observers - relating to data acquisition, processing and reporting - that will allow them to use these standards to achieve $<$ 0.1$^\circ$ precision in the telescope position angle with similar instrumentation, and allow data sets to be combined more accurately., Comment: 30 pages, 8 figures, 17 tables, 4 appendices; accepted to MNRAS. Companion data at: http://www.mira.org/research/polarimetry/PA

We study the following combinatorial counting and sampling problems: can we efficiently sample from the Erd\H{o}s-R\'{e}nyi random graph $G(n,p)$ conditioned on triangle-freeness? Can we efficiently approximate the probability that $G(n,p)$ is triangle-free? These are prototypical instances of forbidden substructure problems ubiquitous in combinatorics. The algorithmic questions are instances of approximate counting and sampling for a hypergraph hard-core model. Estimating the probability that $G(n,p)$ has no triangles is a fundamental question in probabilistic combinatorics and one that has led to the development of many important tools in the field. Through the work of several authors, the asymptotics of the logarithm of this probability are known if $p =o( n^{-1/2})$ or if $p =\omega( n^{-1/2})$. The regime $p = \Theta(n^{-1/2})$ is more mysterious, as this range witnesses a dramatic change in the the typical structural properties of $G(n,p)$ conditioned on triangle-freeness. As we show, this change in structure has a profound impact on the performance of sampling algorithms. We give two different efficient sampling algorithms for triangle-free graphs (and complementary algorithms to approximate the triangle-freeness large deviation probability), one that is efficient when $p < c/\sqrt{n}$ and one that is efficient when $p > C/\sqrt{n}$ for constants $c, C>0$. The latter algorithm involves a new approach for dealing with large defects in the setting of sampling from low-temperature spin models.

There are expected to be millions of isolated black holes in the Galaxy resulting from the death of massive stars. Measuring the abundance and properties of this remnant population would shed light on the end stages of stellar evolution and the evolution paths of black hole systems. Detecting isolated black holes is currently only possible via gravitational microlensing which has so far yielded one definitive detection. The difficulty in finding microlensing black holes lies in having to choose a small subset of events based on characteristics of their lightcurves to allocate expensive and scarce follow-up resources to confirm the identity of the lens. Current methods either rely on simple cuts in parameter space without using the full distribution information or are only effective on a small subsets of events. In this paper we present a new lens classification method. The classifier takes in posterior constraints on lightcurve parameters and combines them with a Galactic simulation to estimate the lens class probability. This method is flexible and can be used with any set of microlensing lightcurve parameters making it applicable to large samples of events. We make this classification framework available via the popclass python package. We apply the classifier to $\sim10,000$ microlensing events from the OGLE survey and find $23$ high-probability black hole candidates. Our classifier also suggests that the only known isolated black hole is an observational outlier according to current Galactic models and allocation of astrometric follow-up on this event was a high-risk strategy., Comment: 19 pages, 10 figures. Submitted to AAS Journals

popclass is a python package that provides a flexible, probabilistic framework for classifying the lens of a gravitational microlensing event. popclass allows a user to match characteristics of a microlensing signal to a simulation of the Galaxy to calculate lens type probabilities for an event. Constraints on any microlensing signal characteristics and any Galactic model can be used. popclass comes with an interface to common inference libraries for microlensing signal constraints, pre-loaded Galactic models, plotting functionality, and classification uncertainty quantification methods., Comment: 6 pages, 1 figure. Submitted to JOSS. Code available at https://github.com/LLNL/popclass

This paper studies the application of the simulated annealing metaheuristic on the identical parallel machine scheduling problem, a variant of the broader optimal job scheduling problem. In the identical parallel machine scheduling problem, $n$ jobs are to be assigned among $m$ machines. Furthermore, each job takes a certain amount of time that remains constant across machines. The goal of the paper is to schedule $n$ jobs on $m$ machines and minimize the maximum runtime of all machines. Both exact and heuristic methods have been applied to the problem, and the proposed algorithm falls in the heuristic category, making use of the simulated annealing metaheuristic. Compared to exact algorithms, simulated annealing was found to yield near-optimal solutions in comparable or less time for all problem cases., Comment: 5 pages, 3 figures

The ComPair balloon instrument is a prototype gamma-ray telescope that aims to further develop technology for observing the gamma-ray sky in the MeV regime. ComPair combines four detector subsystems to enable parallel Compton scattering and pair-production detection, critical for observing in this energy range. This includes a 10 layer double-sided silicon strip detector tracker, a virtual Frisch grid low energy CZT calorimeter, a high energy CsI calorimeter, and a plastic scintillator anti-coincidence detector. The inaugural balloon flight successfully launched from the Columbia Scientific Balloon Facility site in Fort Sumner, New Mexico, in late August 2023, lasting approximately 6.5 hours in duration. In this proceeding, we discuss the development of the ComPair Since balloon payload, the performance during flight, and early results., Comment: 13 pages, 12 figures

The seven-gluon two-loop full-color Yang-Mills amplitude is presented in a compact analytic form where we use the methods of four-dimensional unitarity cuts to obtain the polylogarithmic pieces and augmented recursion to obtain the rational pieces. Furthermore, an $n$-point form for the full-color polylogarithmic piece is presented. We use these results to probe for potential relations amongst the two-loop partial amplitudes., Comment: two attached Mathematica files. v2 small correction to Table 1 and additional citations added

Super-tree random measure's (STRM's) were introduced by Allouba, Durrett, Hawkes and Perkins as a simple stochastic model which emulates a superprocess at a fixed time. A STRM $\nu$ arises as the a.s. limit of a sequence of empirical measures for a discrete time particle system which undergoes independent supercritical branching and independent random displacement (spatial motion) of children from their parents. We study the connectedness properties of the closed support of a STRM ($\mathrm{supp}(\nu)$) for a particular choice of random displacement. Our main results are distinct sufficient conditions for the a.s. total disconnectedness (TD) of $\mathrm{supp}(\nu)$, and for percolation on $\mathrm{supp}(\nu)$ which will imply a.s. existence of a non-trivial connected component in $\mathrm{supp}(\nu)$. We illustrate a close connection between a subclass of these STRM's and super-Brownian motion (SBM). For these particular STRM's the above results give a.s. TD of the support in three and higher dimensions and the existence of a non-trivial connected component in two dimensions, with the three-dimensional case being critical. The latter two-dimensional result assumes that $p_c(\mathbb{Z}^2)$, the critical probability for site percolation on $\mathbb{Z}^2$, is less than $1-e^{-1}$. (There is strong numerical evidence supporting this condition although the known rigorous bounds fall just short.) This gives evidence that the same connectedness properties should hold for SBM. The latter remains an interesting open problem in dimensions $2$ and $3$ ever since it was first posed by Don Dawson over $30$ years ago., Comment: 64 pages, 5 figures. Comments welcome!

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI., Comment: 21 pages

We prove a hardness of sampling result for the anti-ferromagnetic Ising model on random graphs of average degree $d$ for large constant $d$, proving that when the normalized inverse temperature satisfies $\beta>1$ (asymptotically corresponding to the condensation threshold), then w.h.p. over the random graph there is no stable sampling algorithm that can output a sample close in $W_2$ distance to the Gibbs measure. The results also apply to a fixed-magnetization version of the model, showing that there are no stable sampling algorithms for low but positive temperature max and min bisection distributions. These results show a gap in the tractability of search and sampling problems: while there are efficient algorithms to find near optimizers, stable sampling algorithms cannot access the Gibbs distribution concentrated on such solutions. Our techniques involve extensions of the interpolation technique relating behavior of the mean field Sherrington-Kirkpatrick model to behavior of Ising models on random graphs of average degree $d$ for large $d$. While previous interpolation arguments compared the free energies of the two models, our argument compares the average energies and average overlaps in the two models., Comment: 28 pages

Stabilizing $Z_2$ fluxes in Kitaev spin liquids (KSLs) is crucial for both characterizing candidate materials and identifying Ising anyons. In this study, we investigate the effects of spin-$S$ magnetic impurities embedded in the spin-1/2 KSL. Utilizing exact diagonalization and density matrix renormalization group methods, we examine the impurity magnetization and ground-state flux sector with varying impurity coupling and spin size. Our findings reveal that impurity magnetization exhibits an integer/half-integer spin dependence, which aligns with analytical predictions, and a flux-sector transition from bound-flux to zero-flux occurs at low coupling strengths, independent of the impurity spin. Notably, for spin-3/2 impurities, we observe a reentrant bound-flux sector, which remains stable under magnetic fields. By introducing a minimal model based on Majorana fermions, we provide phenomenological explanations for the transitions. Our results suggest a novel way of binding a flux in KSLs, beyond the proposals of vacancies or Kondo impurities., Comment: 11 pages, 4 figures, and Supplementary information

We present a comprehensive analysis of the photometric and spectroscopic evolution of SN~2021foa, unique among the class of transitional supernovae for repeatedly changing its spectroscopic appearance from hydrogen-to-helium-to-hydrogen-dominated (IIn-to-Ibn-to-IIn) within 50 days past peak brightness. The spectra exhibit multiple narrow ($\approx$ 300--600~km~s$^{-1}$) absorption lines of hydrogen, helium, calcium and iron together with broad helium emission lines with a full-width-at-half-maximum (FWHM) of $\sim 6000$~km~s$^{-1}$. For a steady, wind-mass loss regime, light curve modeling results in an ejecta mass of $\sim 8$ M$_{\odot}$ and CSM mass below 1 M$_{\odot}$, and an ejecta velocity consistent with the FWHM of the broad helium lines. We obtain a mass-loss rate of $\approx 2$ M$_{\odot} {\rm yr}^{-1}$. This mass-loss rate is three orders of magnitude larger than derived for normal Type II SNe. We estimate that the bulk of the CSM of SN~2021foa must have been expelled within half a year, about 15 years ago. Our analysis suggests that SN~2021foa had a helium rich ejecta which swept up a dense shell of hydrogen rich CSM shortly after explosion. At about 60 days past peak brightness, the photosphere recedes through the dense ejecta-CSM region, occulting much of the red-shifted emission of the hydrogen and helium lines, which results in observed blue-shift ($\sim -3000$~km~s$^{-1}$). Strong mass loss activity prior to explosion, such as those seen in SN~2009ip-like objects and SN~2021foa as precursor emission, are the likely origin of a complex, multiple-shell CSM close to the progenitor star., Comment: Revised. Accepted in ApJ