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This report presents selected findings from the 2020/22 Beginning Postsecondary Students Longitudinal Study (BPS:20/22), a study conducted by the National Center for Education Statistics (NCES) within the U.S. Department of Education. The findings in this report are based on data about a nationally representative sample of all first-time beginning students (FTBs) who entered postsecondary education for the first time during the 2019-20 academic year. BPS:20/22 is the first follow-up of a cohort of FTBs originally identified during the 2019-20 National Postsecondary Student Aid Study (NPSAS:20) and covers 3 years of students' experiences, from their first year of postsecondary education in the 2019-20 academic year through the 2021-22 academic year. The BPS:20/22 sample consisted of approximately 37,330 total students, representing the approximately 3.3 million undergraduates who were first-time postsecondary students enrolled in the 2019-20 academic year. Data from BPS can address questions about students' postsecondary enrollment patterns, continued enrollment in college, highest postsecondary credential earned, social and academic experiences and activities, and employment history. To facilitate analyses of student choices and postsecondary enrollment and credential attainment, the BPS:20/22 survey collected information on teacher effectiveness, as well as frequency and types of communication and interactions with family and friends, faculty members, and other students, which were also included in the NPSAS:20 survey. Additionally, in response to the COVID-19 pandemic, several survey questions asked respondents to report on employment and education disruptions and personal experiences "as a result of the coronavirus pandemic" in spring 2020 and in the 2020-21 academic year. Respondents indicated their perception of whether an experience was the result of the coronavirus pandemic. The report provides information on first-time postsecondary students' attainment and persistence rates at any institution 3 years after they began postsecondary education, along with their education and personal experiences during the COVID-19 pandemic. In this report, attainment represents the students' highest postsecondary credential earned, whereas persistence describes enrollment during the 2021-22 academic year for students who had not earned a postsecondary credential. As a result, students who earned a postsecondary credential and enrolled for additional postsecondary education are accounted for in attainment rates. It is important to note that BPS:20/22 data reflect first-time postsecondary students' experiences over 3 academic years, when many students are still working toward a postsecondary credential. The 2022/25 Beginning Postsecondary Students Longitudinal Study, which is designed to follow up with students 6 years after they began postsecondary education, will provide more comprehensive data about postsecondary attainment. This First Look shows some of the ways that BPS:20/22 data can be used. Selected findings are not intended to emphasize any particular issue, and the report does not discuss all the information presented in the tables. Readers should not infer causality from the data in this report. Although many of the factors discussed are related to each other, this report does not explore these relationships. The student- and institution-level factors in this report are just a few of the several hundred that will be available in the BPS:20/22 data.

This quasi-experimental study examined the effectiveness of a one-time emergency financial relief program among Pell Grant eligible undergraduate students in Spring 2015 pursuing their first bachelor's degree across academic and financial outcomes. The academic outcomes included retention to the next semester, degree completion, attempted credit hours, and grade point average. The financial outcome captured whether students received a stop registration hold due to an unpaid financial balance in the semester after receiving the emergency relief. The results reveal that financial relief applied to low-income students' accounts can improve their retention and graduation rates. The financial relief was most effective among first-generation college students, resulting in a complete elimination of the retention gap for first-generation students. The emergency relief did not improve GPA or substantially change the number of credits earned. A concerning finding was that students receiving this emergency support were more likely to receive a financial hold in a subsequent semester and that effect was stronger among students of color (Black/African American, Hispanic/Latine, Asian, Multiracial, American Indian/Alaska Native), males, and first-generation college students.

Reproducibility of computational research is critical for ensuring transparency, reliability and reusability. Challenges with computational reproducibility have been documented in several fields, but healthcare discrete-event simulation (DES) models have not been thoroughly examined in this context. This study assessed the computational reproducibility of eight published healthcare DES models (Python or R), selected to represent diverse contexts, complexities, and years of publication. Repositories and articles were also assessed against guidelines and reporting standards, offering insights into their relationship with reproducibility success. Reproducing results required up to 28 hours of troubleshooting per model, with 50% fully reproduced and 50% partially reproduced (12.5% to 94.1% of reported outcomes). Key barriers included the absence of open licences, discrepancies between reported and coded parameters, and missing code to produce model outputs, run scenarios, and generate tables and figures. Addressing these issues would often require relatively little effort from authors: adding an open licence and sharing all materials used to produce the article. Actionable recommendations are proposed to enhance reproducibility practices for simulation modellers and reviewers.

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

This paper describes Meta's ACH system for mutation-guided LLM-based test generation. ACH generates relatively few mutants (aka simulated faults), compared to traditional mutation testing. Instead, it focuses on generating currently undetected faults that are specific to an issue of concern. From these currently uncaught faults, ACH generates tests that can catch them, thereby `killing' the mutants and consequently hardening the platform against regressions. We use privacy concerns to illustrate our approach, but ACH can harden code against {\em any} type of regression. In total, ACH was applied to 10,795 Android Kotlin classes in 7 software platforms deployed by Meta, from which it generated 9,095 mutants and 571 privacy-hardening test cases. ACH also deploys an LLM-based equivalent mutant detection agent that achieves a precision of 0.79 and a recall of 0.47 (rising to 0.95 and 0.96 with simple pre-processing). ACH was used by Messenger and WhatsApp test-a-thons where engineers accepted 73% of its tests, judging 36% to privacy relevant. We conclude that ACH hardens code against specific concerns and that, even when its tests do not directly tackle the specific concern, engineers find them useful for their other benefits., Comment: Submitted to FSE 2025 Industry Track

This note discusses our formalisation in Lean of the classification of the groups of order $p q$ for (not necessarily distinct) prime numbers $p$ and $q$, together with various intermediate results such as the characterisation of internal direct and semidirect products.

We present a new photometric pipeline for the detection of pre-supernova (pre-SN) emission in the Young Supernova Experiment (YSE) sky survey. The method described is applied to SN 2020tlf, a type II SN (SN II) with precursor emission in the last ~100 days before first light. We re-analyze the YSE griz-band light curves of SN 2020tlf and provide revised pre-explosion photometry that includes a robust list of confident detection and limiting magnitudes. Compared to the results of Jacobson-Galan et al. 2022a, this new analysis yields fewer total r/i/z-band pre-SN detections at phases > -100 days. Furthermore, we discourage the use of the blackbody modeling of the pre-explosion spectral energy distribution, the pre-SN bolometric light curve and the blackbody model parameters presented in Jacobson-Galan et al. 2022a. Nevertheless, binned photometry of SN 2020tlf confirms a consistent progenitor luminosity of ~10$^{40}$ erg s$^{-1}$ before explosion., Comment: 5 pages, 1 figure. Published in RNAAS

We present an AI pipeline that involves using smart drones equipped with computer vision to obtain a more accurate fruit count and yield estimation of the number of blueberries in a field. The core components are two object-detection models based on the YOLO deep learning architecture: a Bush Model that is able to detect blueberry bushes from images captured at low altitudes and at different angles, and a Berry Model that can detect individual berries that are visible on a bush. Together, both models allow for more accurate crop yield estimation by allowing intelligent control of the drone's position and camera to safely capture side-view images of bushes up close. In addition to providing experimental results for our models, which show good accuracy in terms of precision and recall when captured images are cropped around the foreground center bush, we also describe how to deploy our models to map out blueberry fields using different sampling strategies, and discuss the challenges of annotating very small objects (blueberries) and difficulties in evaluating the effectiveness of our models., Comment: 30 pages

Background: The MAJORANA DEMONSTRATOR , a modular array of isotopically enriched high-purity germanium (HPGe) detectors, was constructed to demonstrate backgrounds low enough to justify building a tonne-scale experiment to search for the neutrinoless double-beta decay ($\beta\beta(0\nu)$) of $^{76}\mathrm{Ge}$. Purpose: This paper presents a description of the instrument, its commissioning, and operations. It covers the electroforming, underground infrastructure, enrichment, detector fabrication, low-background and construction techniques, electronics, data acquisition, databases, and data processing of the MAJORANA DEMONSTRATOR. Method: The MAJORANA DEMONSTRATOR operated inside an ultra-low radioactivity passive shield at the 4850-foot~level of the Sanford Underground Research Facility (SURF) from 2015-2021. Results and Conclusions: The MAJORANA DEMONSTRATOR achieved the best energy resolution and second-best background level of any $\beta\beta(0\nu)$ search. This enabled it to achieve an ultimate half-life limit on $\beta\beta(0\nu)$ in $^{76}\mathrm{Ge}$ of $8.3\times 10^{25}$~yr (90\% C.L.) and perform a rich set of searches for other physics beyond the Standard Model., Comment: 72 pages

We prove two estimates for the Barban--Davenport--Halberstam type variance of a general complex sequence in arithmetic progressions. The proofs are elementary, and our estimates are capable of yielding an asymptotic for the variance when the sequence is sufficiently nice, and is either somewhat sparse or is sufficiently like the integers in its divisibility by small moduli. As a concrete application, we deduce a Barban--Davenport--Halberstam type variance asymptotic for the $y$-smooth numbers less than $x$, on a wide range of the parameters. This addresses a question considered by Granville and Vaughan., Comment: 23 pages

In 1960, J.F. Adams introduced secondary cohomology operations that are defined on cohomology elements on which sufficiently many Steenrod algebra elements vanish. This led to his theorem on singly generated cohomology rings, which in turn led to his celebrated resolution of the Hopf invariant one problem. Here we advertise a conjecture that would extend Adams' result and prove it in a special case., Comment: 14 pages

We present a new, cosmologically model-independent, statistical analysis of the Pantheon+ type Ia supernovae spectroscopic dataset, improving a standard methodology adopted by Lane et al. We use the Tripp equation for supernova standardisation alone, thereby avoiding any potential correlation in the stretch and colour distributions. We compare the standard homogeneous cosmological model, i.e., $\Lambda$CDM, and the timescape cosmology which invokes backreaction of inhomogeneities. Timescape, while statistically homogeneous and isotropic, departs from average Friedmann-Lema\^{\i}tre-Robertson-Walker evolution, and replaces dark energy by kinetic gravitational energy and its gradients, in explaining independent cosmological observations. When considering the entire Pantheon+ sample, we find very strong evidence ($\ln B> 5$) in favour of timescape over $\Lambda$CDM. Furthermore, even restricting the sample to redshifts beyond any conventional scale of statistical homogeneity, $z > 0.075$, timescape is preferred over $\Lambda$CDM with $\ln B> 1$. These results provide evidence for a need to revisit the foundations of theoretical and observational cosmology., Comment: 6 pages, 3 figures, 1 table; associated RAS press release RAS PR 24/33

Silicon G centers show significant promise as single photon sources in a scalable silicon platform. But these color centers have large non-radiative decay and a low Debye-Waller factor, limiting their usability in quantum applications. In this work, we demonstrate bright Purcell-enhanced emission from a silicon G center by coupling it to a nanophotonic cavity. The nanobeam cavity enhances the spontaneous emission rate of a single G center by a factor of 6, corresponding to a Purcell factor greater than 31 when accounting for decay into the phonon sideband. We obtain a spontaneous emission rate of 0.97 ns, which is the fastest single photon emission rate reported in silicon. With this radiative enhancement, we achieve an order of magnitude improvement in emitter brightness compared to previously reported values. These results pave the way for scalable quantum light sources on a silicon photonic chip.

TSF is growing in various domains including manufacturing. Although numerous TSF algorithms have been developed recently, the validation and evaluation of algorithms hold substantial value for researchers and practitioners and are missing. This study aims to fill this gap by evaluating the SoTA TSF algorithms on thirteen manufacturing datasets, focusing on their applicability in manufacturing. Each algorithm was selected based on its TSF category to ensure a representative set of algorithms. The evaluation includes different scenarios to evaluate the models using two problem categories and two forecasting horizons. To evaluate the performance, the WAPE was calculated, and additional post hoc analyses were conducted to assess the significance of observed differences. Only algorithms with codes from open-source libraries were utilized, and no hyperparameter tuning was done. This allowed us to evaluate the algorithms as "out-of-the-box" solutions that can be easily implemented, ensuring their usability within the manufacturing by practitioners with limited technical knowledge. This aligns to facilitate the adoption of these techniques in smart manufacturing systems. Based on the results, transformer and MLP-based architectures demonstrated the best performance with MLP-based architecture winning the most scenarios. For univariate TSF, PatchTST emerged as the most robust, particularly for long-term horizons, while for multivariate problems, MLP-based architectures like N-HITS and TiDE showed superior results. The study revealed that simpler algorithms like XGBoost could outperform complex algorithms in certain tasks. These findings challenge the assumption that more sophisticated models produce better results. Additionally, the research highlighted the importance of computational resource considerations, showing variations in runtime and memory usage across different algorithms.

We present starkiller, an open-source Python package for forward-modeling flux retrieval from integral field unit spectrograph (IFU) datacubes. Starkiller simultaneously provides stellar spectral classification, relative velocity, and line-of-sight extinction for all sources in a catalog, alongside a source-subtracted datacube. It performs synthetic difference imaging by simulating all catalog sources in the field of view, using the catalog for positions and fluxes to scale stellar models, independent of the datacube. This differencing method is particularly powerful for subtracting both point-sources and trailed or even streaked sources from extended astronomical objects. We demonstrate starkiller's effectiveness in improving observations of extended sources in dense stellar fields for VLT/MUSE observations of comets, asteroids and nebulae. We also show that starkiller can treat satellite-impacted VLT/MUSE observations. The package could be applied to tasks as varied as dust extinction in clusters and stellar variability; the stellar modeling using Gaia fluxes is provided as a standalone function. The techniques can be expanded to imagers and to other IFUs., Comment: 21 pages, 17 figures, submitted to AAS journals, open source software

This work augments the recently introduced Stabilizer Tensor Network (STN) protocol with magic state injection, reporting a new framework with significantly enhanced ability to simulate circuits with an extensive number of non-Clifford operations. Specifically, for random $T$-doped $N$-qubit Clifford circuits the computational cost of circuits prepared with magic state injection scales as $\mathcal{O}(\text{poly}(N))$ when the circuit has $t \lesssim N$ $T$-gates compared to an exponential scaling for the STN approach, which is demonstrated in systems of up to $200$ qubits. In the case of the Hidden Bit Shift circuit, a paradigmatic benchmarking system for extended stabilizer methods with a tunable amount of magic, we report that our magic state injected STN framework can efficiently simulate $4000$ qubits and $320$ $T$-gates. These findings provide a promising outlook for the use of this protocol in the classical modelling of quantum circuits that are conventionally difficult to simulate efficiently., Comment: 5+5 pages, 3+2 figures

Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects such as keyhole, balling, and lack of fusion can significantly compromise structural integrity. This study presents SL-RF+ (Sequentially Learned Random Forest with Enhanced Sampling), a novel Sequential Learning (SL) framework for melt pool defect classification designed to maximize data efficiency and model accuracy in data-scarce environments. SL-RF+ utilizes RF classifier combined with Least Confidence Sampling (LCS) and Sobol sequence-based synthetic sampling to iteratively select the most informative samples to learn from, thereby refining the model's decision boundaries with minimal labeled data. Results show that SL-RF+ outperformed traditional machine learning models across key performance metrics, including accuracy, precision, recall, and F1 score, demonstrating significant robustness in identifying melt pool defects with limited data. This framework efficiently captures complex defect patterns by focusing on high-uncertainty regions in the process parameter space, ultimately achieving superior classification performance without the need for extensive labeled datasets. While this study utilizes pre-existing experimental data, SL-RF+ shows strong potential for real-world applications in pure sequential learning settings, where data is acquired and labeled incrementally, mitigating the high costs and time constraints of sample acquisition.

This study examines the pitch content in traditional Ghanaian seperewa (Akan harp-lute) songs, utilizing a unique dataset from field recordings of the mid-twentieth century. We selected 71 songs and used Demucs to isolate vocals from instrumental tracks. We then retrieved the F0 content from these isolated tacks and applied Gaussian Mixture Models (GMM) to approximate musical scales. Comparative F0 analysis between vocals and seperewa revealed higher microtonal deviations from equal temperament in vocal tracks. We also note challenges in using MIR tools for musical scale approximation in non-Western music. Our research contributes to the quantitative study of pitch in traditional music of Sub-Saharan Africa.

In this paper, we analyze the question of replica symmetry in the bulk for multi-partite entanglement measures in the vacuum state of two dimensional holographic CFTs. We first define a class of multi-partite local unitary invariants, multi-invariants, with a given replica symmetry that acts freely and transitively on the replicas. We look for a subclass of measures such that the dual bulk geometry also preserves replica symmetry. We obtain the most general solution to this problem if we require the bulk to preserve replica symmetry for general configurations of the regions. Orbifolding the bulk solution with the replica symmetry gives us a bulk geometry with a network of conical singularities. Our approach makes it clear that there are infinitely many infinitely large families of multi-invariants such that each family evaluates identically on the holographic state. Geometrically, these are equalities involving volumes of handlebodies, possibly of different genus, at particular points in the moduli space. In certain cases, we check our bulk computation with an explicit calculation in CFT. Finally we comment on the generalization to higher dimension., Comment: 64 pages + appendices, 31 figures

The Center for the Study of Social Policy (CSSP) developed the "Strengthening Families Approach and Protective Factors Framework" (SF) and the "Youth Thrive Protective and Promotive Factors Framework" (YT) in response to an urgent need in the fields of child maltreatment prevention and youth services to shift from a primary goal of identifying risk factors and "fixing" problems to elevating the goal of building attributes, relationships, knowledge, skills, and resources that maximize the potential of children, youth, and families. These research-informed frameworks address the two most significant developmental periods in the lifespan: early childhood and adolescence. The purpose of this report is to expand the perspectives and research evidence that inform the SF and YT frameworks by addressing key issues not highlighted in the initial SF and YT research foundation reports. It is intended to broaden understandings about the challenges children, youth, and parents face and ways to promote a trajectory of healthy development and well-being. The report includes research and perspectives by racially and ethnically diverse scholars to ensure that the explanations of key constructs and pathways to healthy development and well-being are not limited to a single ethnocentric perspective. Also, it is informed by perspectives of SF and YT thought partners, practitioners, and young adults which were shared during several listening sessions undertaken in 2023.

Synthesizing high-quality tabular data is an important topic in many data science tasks, ranging from dataset augmentation to privacy protection. However, developing expressive generative models for tabular data is challenging due to its inherent heterogeneous data types, complex inter-correlations, and intricate column-wise distributions. In this paper, we introduce TabDiff, a joint diffusion framework that models all multi-modal distributions of tabular data in one model. Our key innovation is the development of a joint continuous-time diffusion process for numerical and categorical data, where we propose feature-wise learnable diffusion processes to counter the high disparity of different feature distributions. TabDiff is parameterized by a transformer handling different input types, and the entire framework can be efficiently optimized in an end-to-end fashion. We further introduce a multi-modal stochastic sampler to automatically correct the accumulated decoding error during sampling, and propose classifier-free guidance for conditional missing column value imputation. Comprehensive experiments on seven datasets demonstrate that TabDiff achieves superior average performance over existing competitive baselines across all eight metrics, with up to $22.5\%$ improvement over the state-of-the-art model on pair-wise column correlation estimations. Code is available at https://github.com/MinkaiXu/TabDiff.

Intels efforts to build a practical quantum computer are focused on developing a scalable spin-qubit platform leveraging industrial high-volume semiconductor manufacturing expertise and 300 mm fabrication infrastructure. Here, we provide an overview of the design, fabrication, and demonstration of a new customized quantum test chip, which contains 12-quantum-dot spin-qubit linear arrays, code named Tunnel Falls. These devices are fabricated using immersion and extreme ultraviolet lithography (EUV), along with other standard high-volume manufacturing (HVM) processes, as well as production-level process control. We present key device features and fabrication details, as well as qubit characterization results confirming device functionality. These results corroborate our fabrication methods and are a crucial step towards scaling of extensible 2D qubit array schemes.

As our understanding of autism and ableism continues to increase, so does our understanding of ableist language towards autistic people. Such language poses a significant challenge in NLP research due to its subtle and context-dependent nature. Yet, detecting anti-autistic ableist language remains underexplored, with existing NLP tools often failing to capture its nuanced expressions. We present AUTALIC, the first benchmark dataset dedicated to the detection of anti-autistic ableist language in context, addressing a significant gap in the field. The dataset comprises 2,400 autism-related sentences collected from Reddit, accompanied by surrounding context, and is annotated by trained experts with backgrounds in neurodiversity. Our comprehensive evaluation reveals that current language models, including state-of-the-art LLMs, struggle to reliably identify anti-autistic ableism and align with human judgments, underscoring their limitations in this domain. We publicly release AUTALIC along with the individual annotations which serve as a valuable resource to researchers working on ableism, neurodiversity, and also studying disagreements in annotation tasks. This dataset serves as a crucial step towards developing more inclusive and context-aware NLP systems that better reflect diverse perspectives., Comment: 9 pages, 5 figures, 7 tables

We give a short review of recent progress on determining the order of magnitude of moments $\mathbb{E}|\sum_{n \leq x} f(n)|^{2q}$ of random multiplicative functions, and of closely related issues. We hope this can serve as a concise introduction to some of the ideas involved, for those who may not have too much background in the area., Comment: 26 pages. A version of this paper will appear in the Proceedings of the ICBS 2024

Recent advances in machine learning have led to a surge in adoption of neural networks for various tasks, but lack of interpretability remains an issue for many others in which an understanding of the features influencing the prediction is necessary to ensure fairness, safety, and legal compliance. In this paper we consider one class of such tasks, tabular dataset classification, and propose a novel neuro-symbolic architecture, Neural Reasoning Networks (NRN), that is scalable and generates logically sound textual explanations for its predictions. NRNs are connected layers of logical neurons which implement a form of real valued logic. A training algorithm (R-NRN) learns the weights of the network as usual using gradient descent optimization with backprop, but also learns the network structure itself using a bandit-based optimization. Both are implemented in an extension to PyTorch (https://github.com/IBM/torchlogic) that takes full advantage of GPU scaling and batched training. Evaluation on a diverse set of 22 open-source datasets for tabular classification demonstrates performance (measured by ROC AUC) which improves over multi-layer perceptron (MLP) and is statistically similar to other state-of-the-art approaches such as Random Forest, XGBoost and Gradient Boosted Trees, while offering 43% faster training and a more than 2 orders of magnitude reduction in the number of parameters required, on average. Furthermore, R-NRN explanations are shorter than the compared approaches while producing more accurate feature importance scores.

Modern computing systems are capable of exascale calculations, which are revolutionizing the development and application of high-fidelity numerical models in computational science and engineering. While these systems continue to grow in processing power, the available system memory has not increased commensurately, and electrical power consumption continues to grow. A predominant approach to limit the memory usage in large-scale applications is to exploit the abundant processing power and continually recompute many low-level simulation quantities, rather than storing them. However, this approach can adversely impact the throughput of the simulation and diminish the benefits of modern computing architectures. We present two novel contributions to reduce the memory burden while maintaining performance in simulations based on finite element discretizations. The first contribution develops dictionary-based data compression schemes that detect and exploit the structure of the discretization, due to redundancies across the finite element mesh. These schemes are shown to reduce memory requirements by more than 99 percent on meshes with large numbers of nearly identical mesh cells. For applications where this structure does not exist, our second contribution leverages a recently developed augmented Lagrangian sequential quadratic programming algorithm to enable r-adaptive mesh optimization, with the goal of enhancing redundancies in the mesh. Numerical results demonstrate the effectiveness of the proposed methods to detect, exploit and enhance mesh structure on examples inspired by large-scale applications., Comment: 22 pages

For a group $G$, a subgroup $U \leq G$ and a group $\mathrm{Inn}(G) \leq A \leq \mathrm{Aut}(G)$, we say that $U$ is an $A$-covering group of $G$ if $G = \bigcup_{a\in A}U^a$. A theorem of Jordan (1872) implies that if $G$ is a finite group, $A = \mathrm{Inn}(G)$ and $U$ is an $A$-covering group of $G$, then $U = G$. Motivated by a question concerning Kronecker classes of field extensions, Neumann and Praeger (1988) conjectured that, more generally, there is an integer function $f$ such that if $G$ is a finite group and $U$ is an $A$-covering subgroup of $G$, then $|G:U| \leq f(|A:\mathrm{Inn}(G)|)$. A key piece of evidence for this conjecture is a theorem of Praeger (1994), which asserts that there is a two-variable integer function $g$ such that if $G$ is a finite group and $U$ is an $A$-covering subgroup of $G$, then $|G:U|\leq g(|A:\mathrm{Inn}(G)|,c)$ where $c$ is the number of $A$-chief factors of~$G$. Unfortunately, the proof of this result contains an error. In this paper, using a different argument, we give a correct proof of this theorem., Comment: 6 pages

This paper presents the development of a new entropy-based feature selection method for identifying and quantifying impacts. Here, impacts are defined as statistically significant differences in spatio-temporal fields when comparing datasets with and without an external forcing in Earth system models. Temporal feature selection is performed by first computing the cross-fuzzy entropy to quantify similarity of patterns between two datasets and then applying changepoint detection to identify regions of statistically constant entropy. The method is used to capture temperate north surface cooling from a 9-member simulation ensemble of the Mt. Pinatubo volcanic eruption, which injected 10 Tg of SO2 into the stratosphere. The results estimate a mean difference decrease in near surface air temperature of -0.560 K with a 99% confidence interval between -0.864 K and -0.257 K between April and November of 1992, one year following the eruption. A sensitivity analysis with decreasing SO2 injection revealed that the impact is statistically significant at 5 Tg but not at 3 Tg. Using identified features, a dependency graph model composed of 68 nodes and 229 edges directly connecting initial aerosol optical depth changes in the tropics to solar flux and temperature changes before the temperate north surface cooling is presented.

One great challenge of modeling granular systems lies in capturing the rheologic dependencies on scale. For example, there are marked differences between quasi-static, intermediate, and rapid flow regimes. In this study, we demonstrate that assumptions for infinite stiffness of rigid particles, an assumption upon which the state-of the-art ($\mu(I)$-rheology) modeling approaches are constructed, must be relaxed in order to recover the physical mechanisms behind many scale-dependent and non-local rheological effects. Any relaxation of the infinite stiffness assumption allows for particles to compress in series, whereby the number of simultaneously compressed particles controls the extent to which end-member particles experience a modified coefficient of effective friction, analogous to reduced stiffness for springs in series. To demonstrate the importance of such a mechanism in setting the dynamics for dense rigid granular systems, we show that modifying simple models to include the kinematics introduced by compression in series captures the emergence of thin layer stability, a widely observed yet incompletely explained non-local granular phenomenon. We also discuss, in general, how knowledge of the contact network and softness provides a potential physical basis for the diffusion of granular temperature.

We examine a century of radial velocity, visual magnitude, and astrometric observations of the nearest red supergiant, Betelgeuse, in order to reexamine the century-old assertion that Betelgeuse might be a spectroscopic binary. These data reveal Betelgeuse varying stochastically over years and decades due to its boiling, convective envelope, periodically with a $ 5.78$~yr long secondary period, and quasi-periodically from pulsations with periods of several hundred days. We show that the long secondary period is consistent between astrometric and RV datasets, and argue that it indicates a low-mass companion to Betelgeuse, less than a solar mass, orbiting in a 2,110 day period at a separation of just over twice Betelgeuse's radius. The companion star would be nearly twenty times less massive and a million times fainter than Betelgeuse, with similar effective temperature, effectively hiding it in plain sight near one of the best-studied stars in the night sky. The astrometric data favor an edge-on binary with orbital plane aligned with Betelgeuse's measured spin axis. Tidal spin-orbit interaction drains angular momentum from the orbit and spins up Betelgeuse, explaining the spin--orbit alignment and Betelgeuse's anomalously rapid spin. In the future, the orbit will decay until the companion is swallowed by Betelgeuse in the next 10,000 years., Comment: In press, AAS journals

The prediction of electronic structure for strongly correlated molecules represents a promising application for near-term quantum computers. Significant attention has been paid to ground state wavefunctions, but excited states of molecules are relatively unexplored. In this work, we consider the ADAPT-VQE algorithm, a single-reference approach for obtaining ground states, and its state-averaged generalization for computing multiple states at once. We demonstrate for both rectangular and linear H$_4$, as well as for BeH$_2$, that this approach, which we call MORE-ADAPT-VQE, can make better use of small excitation manifolds than an analagous method based on a single-reference ADAPT-VQE calculation, q-sc-EOM. In particular, MORE-ADAPT-VQE is able to accurately describe both avoided crossings and crossings between states of different symmetries. In addition to more accurate excited state energies, MORE-ADAPT-VQE can recover accurate transition dipole moments in situations where traditional ADAPT-VQE and q-sc-EOM struggle. These improvements suggest a promising direction toward the use of quantum computers for difficult excited state problems., Comment: 18 pages, 5 figures

We address three key challenges in learning continuous kernel representations: computational efficiency, parameter efficiency, and spectral bias. Continuous kernels have shown significant potential, but their practical adoption is often limited by high computational and memory demands. Additionally, these methods are prone to spectral bias, which impedes their ability to capture high-frequency details. To overcome these limitations, we propose a novel approach that leverages sparse learning in the Fourier domain. Our method enables the efficient scaling of continuous kernels, drastically reduces computational and memory requirements, and mitigates spectral bias by exploiting the Gibbs phenomenon.

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Recently, diffusion-based generative models have demonstrated remarkable performance in speech enhancement tasks. However, these methods still encounter challenges, including the lack of structural information and poor performance in low Signal-to-Noise Ratio (SNR) scenarios. To overcome these challenges, we propose the Schr\"oodinger Bridge-based Speech Enhancement (SBSE) method, which learns the diffusion processes directly between the noisy input and the clean distribution, unlike conventional diffusion-based speech enhancement systems that learn data to Gaussian distributions. To enhance performance in extremely noisy conditions, we introduce a two-stage system incorporating ratio mask information into the diffusion-based generative model. Our experimental results show that our proposed SBSE method outperforms all the baseline models and achieves state-of-the-art performance, especially in low SNR conditions. Importantly, only a few inference steps are required to achieve the best result.

Let $G$ be a nontrivial finite permutation group of degree $n$. If $G$ is transitive, then a theorem of Jordan states that $G$ has a derangement. Equivalently, a finite group is never the union of conjugates of a proper subgroup. If $G$ is intransitive, then $G$ may fail to have a derangement, and this can happen even if $G$ has only two orbits, both of which have size $(1/2+o(1))n$. However, we conjecture that if $G$ has two orbits of size exactly $n/2$ then $G$ does have a derangement, and we prove this conjecture when $G$ acts primitively on at least one of the orbits. Equivalently, we conjecture that a finite group is never the union of conjugates of two proper subgroups of the same order, and we prove this conjecture when at least one of the subgroups is maximal. The same conjecture was implicitly made by Feldman. We prove other cases of the conjecture, and we highlight connections our results have with intersecting families of permutations and roots of polynomials modulo primes. Along the way, we also prove a linear variant on Isbell's conjecture regarding derangements of prime-power order., Comment: 26 pages; references added

The rapid growth of quantum computing in the last few years has led many countries to make relevant public investments in that field. However, Colombia lacks any investment or legislation in this area. Most previous studies have focused on other countries, contributing to the region's significant lag. In this paper, we propose efforts to include quantum computing as a fundamental pillar of Colombia's development plan. In this research we involved three stakeholders: academia (Universidad Nacional de Colombia), industry (Alianza Team), and government (Vice Ministry of Digital Transformation). We anticipate that our work will provide a connection between all stakeholders involved in public investments in quantum technology in the country and will facilitate its legislation., Comment: 5 pages, 3 figures

Social evolutionary theory seeks to explain increases in the scale and complexity of human societies, from origins to present. Over the course of the twentieth century, social evolutionary theory largely fell out of favor as a way of investigating human history, just as advances in complex systems science and computer science saw the emergence of powerful new conceptions of complex systems, and in particular new methods of measuring complexity. We propose that these advances in our understanding of complex systems and computer science should be brought to bear on our investigations into human history. To that end, we present a new framework for modeling how human societies co-evolve with their biotic environments, recognizing that both a society and its environment are computers. This leads us to model the dynamics of each of those two systems using the same, new kind of computational machine, which we define here. For simplicity, we construe a society as a set of interacting occupations and technologies. Similarly, under such a model, a biotic environment is a set of interacting distinct ecological and climatic processes. This provides novel ways to characterize social complexity, which we hope will cast new light on the archaeological and historical records. Our framework also provides a natural way to formalize both the energetic (thermodynamic) costs required by a society as it runs, and the ways it can extract thermodynamic resources from the environment in order to pay for those costs -- and perhaps to grow with any left-over resources., Comment: 61 pages, 7 figures, 5 pages of appendices, 101 references

We propose an approach for characterizing source-impact pathways, the interactions of a set of variables in space-time due to an external forcing, in climate models using in-situ analyses that circumvent computationally expensive read/write operations. This approach makes use of a lightweight open-source software library we developed known as CLDERA-Tools. We describe how CLDERA-Tools is linked with the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) in a minimally invasive way for in-situ extraction of quantities of interested and associated statistics. Subsequently, these quantities are used to represent source-impact pathways with time-dependent directed acyclic graphs (DAGs). The utility of CLDERA-Tools is demonstrated by using the data it extracts in-situ to compute a spatially resolved DAG from an idealized configuration of the atmosphere with a parameterized representation of a volcanic eruption known as HSW-V.

Participatory budgeting (PB) is a democratic approach to allocating municipal spending that has been adopted in many places in recent years, including in Chicago. Current PB voting resembles a ballot where residents are asked which municipal projects, such as school improvements and road repairs, to fund with a limited budget. In this work, we ask how interactive visualization can benefit PB by conducting a design probe-based interview study (N=13) with policy workers and academics with expertise in PB, urban planning, and civic HCI. Our probe explores how graphical elicitation of voter preferences and a dashboard of voting statistics can be incorporated into a realistic PB tool. Through qualitative analysis, we find that visualization creates opportunities for city government to set expectations about budget constraints while also granting their constituents greater freedom to articulate a wider range of preferences. However, using visualization to provide transparency about PB requires efforts to mitigate potential access barriers and mistrust. We call for more visualization professionals to help build civic capacity by working in and studying political systems.

We construct three dimensional non-semisimple topological field theories from the unrolled quantum group of the Lie superalgebra $\mathfrak{osp}(1 \vert 2)$. More precisely, the quantum group depends on a root of unity $q=e^{\frac{2 \pi \sqrt{-1}}{r}}$, where $r$ is a positive integer greater than $2$, and the construction applies when $r$ is not congruent to $4$ modulo $8$. The algebraic result which underlies the construction is the existence of a relative modular structure on the non-finite, non-semisimple category of weight modules for the quantum group. We prove a Verlinde formula which allows for the computation of dimensions and Euler characteristics of topological field theory state spaces of unmarked surfaces. When $r$ is congruent to $\pm 1$ or $\pm 2$ modulo $8$, we relate the resulting $3$-manifold invariants with physicists' $\widehat{Z}$-invariants associated to $\mathfrak{osp}(1 \vert 2)$. Finally, we establish a relation between $\widehat{Z}$-invariants associated to $\mathfrak{sl}(2)$ and $\mathfrak{osp}(1 \vert 2)$ which was conjectured in the physics literature., Comment: 38 pages

We present Polynomial Attention Drop-in Replacement (PADRe), a novel and unifying framework designed to replace the conventional self-attention mechanism in transformer models. Notably, several recent alternative attention mechanisms, including Hyena, Mamba, SimA, Conv2Former, and Castling-ViT, can be viewed as specific instances of our PADRe framework. PADRe leverages polynomial functions and draws upon established results from approximation theory, enhancing computational efficiency without compromising accuracy. PADRe's key components include multiplicative nonlinearities, which we implement using straightforward, hardware-friendly operations such as Hadamard products, incurring only linear computational and memory costs. PADRe further avoids the need for using complex functions such as Softmax, yet it maintains comparable or superior accuracy compared to traditional self-attention. We assess the effectiveness of PADRe as a drop-in replacement for self-attention across diverse computer vision tasks. These tasks include image classification, image-based 2D object detection, and 3D point cloud object detection. Empirical results demonstrate that PADRe runs significantly faster than the conventional self-attention (11x ~ 43x faster on server GPU and mobile NPU) while maintaining similar accuracy when substituting self-attention in the transformer models.