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California has a two-tiered credential structure for education administrators. The first tier is the Preliminary Administrative Services Credential. An individual who has completed all requirements for a preliminary credential but does not have an offer of employment in an administrative position may apply for a Certificate of Eligibility, which does not expire. Once an individual secures an administrative position, the holder of a Certificate of Eligibility may apply for the Preliminary Administrative Services Credential. The second tier is the Clear Administrative Services Credential. To receive the Clear Administrative Services Credential, individuals must successfully complete a 2-year induction program. The state's 2-year induction model focuses on job-embedded, individualized support that includes: (1) coaching with a trained coach for at least 40 hours per year, (2) personalized professional development for at least 20 hours per year, and (3) multiple assessments that capture competency toward the practices outlined in the state's professional standards for administrators. These assessments include an initial assessment to inform a participant's individual induction plan, benchmark assessments to measure progress over the course of the program, and a summative assessment to demonstrate that participants have completed program requirements and to support programs in recommending participants for the clear credential. This report explores the landscape of new administrator induction in California using statewide survey data and case studies of administrator induction programs. The California Commission on Teacher Credentialing (CTC), which accredits and oversees all administrator induction programs, conducts a program completer survey asking about administrators' experiences with their induction programs and overall preparedness when they apply for their clear credential. This report analyzes 4 years of survey data from 6,812 administrators who completed induction between September 2017 and August 2021. It also draws on qualitative data from six case studies of administrator induction programs that were highly rated on the program completer surveys to highlight exemplary structures and practices.

Teachers of color often work in schools with few colleagues from the same racial or ethnic background. This "racial isolation" may affect their work experiences and important job outcomes, including retention. Using longitudinal administrative and survey data, we investigate the degree to which Tennessee teachers who are more racially isolated are more likely to turn over. Accounting for other factors, we find that racially isolated Black teachers are more likely to leave their schools than less isolated teachers. This turnover is driven by transfers to a different district and exiting the profession altogether. Consistent with an explanation that isolated teachers' work experiences differ, they also report less collaboration with colleagues and receive lower observation scores.

Teaching performance assessments (TPAs) can be used to assess the readiness of potential teachers because they require candidates to provide evidence of their planning and teaching skills through classroom videos accompanied by commentaries Ensuring that teaching candidates are well prepared to enter the classroom is a critical mission for teacher preparation programs and state agencies that approve programs and set teacher licensure standards. Teaching performance assessments (TPAs) can be used to assess the readiness of potential teachers because they require candidates to provide evidence of their teaching knowledge and skills through classroom videos, lesson plans, student work, and analysis of teaching and learning. Multiple studies have found that TPA scores predict effectiveness once candidates enter the classroom as licensed teachers. TPAs have been adopted in at least 16 states as a requirement of either teacher preparation program completion or initial licensure. California, the focus of this study, was one of the first states to adopt a TPA as a licensure requirement for beginning teachers. The report on which this brief is based explored whether certain preparation experiences predicted success on the California Teaching Performance Assessment (CalTPA) or the educative Teaching Performance Assessment (edTPA), the two widely available TPAs used across California preparation programs. Focusing on the 2021-22 and 2022-23 school years, this analysis examined whether TPA success for more than 18,000 California teaching candidates varied by preparation pathway, program, and the nature of preparation experiences as reported by respondents on the annual survey administered to all those completing preparation and applying for their preliminary teaching credential with the California Commission on Teacher Credentialing (CTC).

Ensuring that teaching candidates are well prepared to enter the classroom is a critical mission for teacher preparation programs and state agencies that approve programs and set teacher licensure standards. Teaching performance assessments (TPAs) can be used to assess the readiness of potential teachers because they require candidates to provide evidence of their teaching knowledge and skills through classroom videos, lesson plans, student work, and analysis of teaching and learning. TPAs have been adopted in at least 16 states as a requirement of either teacher preparation program completion or initial licensure. California, the focus of this study, was one of the first states to adopt a TPA as a licensure requirement for beginning teachers. The state has since adopted three TPA models: the California Teaching Performing Assessment (CalTPA), the educative Teaching Performance Assessment (edTPA), and the Fresno Assessment of Student Teachers (FAST). Unlike multiple-choice licensure exams measuring the basic skills or content knowledge of teaching candidates, TPAs are classroom-based assessments capturing direct evaluation of teaching skills. This evaluation process creates opportunities for candidates (and their preparation programs) to identify their strengths and weaknesses. TPAs typically occur when preservice candidates are in student teaching placements (referred to here as clinical practice) or, for those participating in in-service preparation like internships, in their own classrooms. Multiple studies have found that TPA scores predict effectiveness once candidates enter the classroom as licensed teachers, and proponents argue that TPAs serve as a valuable professional standard set to ensure a teaching candidate is ready to enter the classroom. On the other hand, critics have questioned whether a TPA requirement, along with other tests, serves as an unnecessary gatekeeper to the profession and whether the fees and time investment required by a TPA can be a cumbersome barrier for potential teachers, particularly teaching candidates of color and candidates with fewer financial resources. While TPA passing rates among California teaching candidates were quite high prior to the COVID-19 pandemic, variability in performance on TPAs has increased in California as the pandemic has rocked every aspect of the education system, including the training of teachers. Focusing on the 2021-22 and 2022-23 academic years, this study explored whether certain preparation experiences predicted TPA success. Understanding these relationships can inform programmatic and policy decisions about how to support teaching candidates in entering the workforce with strong preparation and minimal barriers. Using data provided by the California Commission on Teacher Credentialing, this analysis focused on 18,455 California teaching candidates who took either the CalTPA or edTPA--the two widely available TPAs used across California teacher preparation programs--between September 1, 2021, and August 31, 2023.

This Postsecondary Teaching with Technology Collaborative (the Collaborative) report examines how faculty and staff at seven community colleges and two broad-access universities reconsidered students' online learning needs in the midst of the pandemic. The report focuses on a set of interconnected student mindsets and competencies that the authors call self-directed learning (SDL) skills; in their framing, SDL skills include motivational processes (e.g., building self-efficacy and sense of belonging), metacognitive processes (e.g., identifying and adjusting learning strategies and reflecting), and applied learning processes (e.g., managing time and seeking help). The authors explore whether and how the colleges offered supports for students to strengthen their SDL skills and the extent to which SDL skill supports were offered within online courses versus outside of class. Findings from interview data and secondary sources show that most institutions made changes to improve conditions for online teaching and learning, including improved use of learning management systems, increased professional development for instructors, improved physical technology infrastructure and expanded software licenses, and the development of student-facing online orientation materials. At the same time, increased use of online courses continued to present challenges, including poorer student performance in online course sections, lack of meaningful communication and engagement, lower likelihood for online students to seek help when they need it, and increased demands for students to apply SDL skills such as time and task management. Moreover, many SDL supports occurred at the institution level and therefore were not tailored to particular content areas or course types. These supports provided students with valuable and needed resources to help them succeed in college but also had some limitations. Finally, while most faculty and staff the authors interviewed expressed that SDL skills are important and should be supported by the institution, explicit instructional support for SDL skills inside classrooms appeared to be limited and uneven; faculty may not know how to teach these skills, may not think that they have the time or resources to do so, or may not see it as their responsibility. The report concludes with three recommendations for institutions to promote SDL skill development. "Teaching and Designing Online STEM Courses to Support Self-Directed Learning Skills" which focuses on teaching and designing online STEM courses to support SDL skills is a collaborative report.

The Postsecondary Teaching with Technology Collaborative (the Collaborative) is currently researching the obstacles students face in online STEM courses and the ways that institutions and instructors can better support these students. As part of this research program, the authors conducted qualitative research in partnership with six community colleges and two broad-access universities to learn if, when, and how instructors support self-directed learning (SDL) skill development in their online courses. SDL skills can be understood as motivational, metacognitive, and applied learning processes--in other words, the ways that students maintain motivation, reflect and think about their learning, plan and set goals, and adjust their study strategies. In spring 2022, the authors surveyed 141 STEM instructors online and conducted 12 virtual "course tours" with STEM instructors who shared their screens and walked researchers through their course structure and content in the learning management system. Instructors in the sample perceived online courses as learning environments where it is more difficult to engage students and help them stay on track with their coursework. According to faculty, students in online courses are less likely than those in in-person courses to experience the excitement and motivation of being part of a community of learners because of the lack of opportunities to engage with peers. Instructors also described fewer opportunities to learn about and build connections with their students through informal conversations, and they emphasized a number of barriers to incorporating SDL skills into instruction. Despite this, the authors provide a variety of examples of strategies and activities instructors indicated they use to support the processes that are theorized--according to the Collaborative's SDL framework--to lead to SDL skill development, as well as three case examples of SDL instruction that faculty incorporated into their STEM course teaching. The brief concludes with an overview of the types of instructional strategies that support SDL skill development and how they can be used throughout a course. A Collaborative report on teaching and designing online STEM courses to support SDL skills is also available.

Efforts to strengthen the pipeline to college degree completion have focused on improving college access and providing academic, social, and financial supports to students post-enrollment. This paper explores one facet of postsecondary education that has served as a barrier to both college access and success--developmental education--which has proven to exacerbate racial inequities in academic progress in higher education and has effectively decreased college access for low-income students and students of color. After more than a decade of trying to tackle the developmental education problem indirectly through basic skills-related initiatives, task forces, and success initiatives, the California state legislature passed Assembly Bill 705 (AB705) in 2017, which directed colleges to replace standardized placement tests with multiple measures of high school performance to determine college readiness. In this paper, the authors describe the research that prompted developmental education reform approaches nationally and in California, describe the efforts in California that led to the passing of AB705, and summarize research on its implementation and outcomes. Building on analysis of the research, the paper concludes with five key practice and policy recommendations for California community college leaders as they move toward realizing a civil rights agenda for college access and success in the next 25 years: (1) Address faculty and practitioners' beliefs; (2) Move from structural to instructional reform; (3) Improve data accessibility, reporting, and accountability; (4) Expand equitable college access opportunities for students in high school; and (5) Address barriers facing English learners.

This interim report shares emerging findings and recommendations from a collaborative project. The project examines post-16 Education and Training (E&T) in the UK. It is focused on analysing the divergent approaches to E&T policy across the four devolved nations to understand more deeply the key policy issues and challenges facing E&T and the kinds of structures that support young people's transitions from education to employment and help them live fulfilling lives. The authors, therefore, focus on the interplay between policies, participation, and outcomes across the four nations, particularly examining issues of inequality for young people. Through the combination of detailed comparative policy analysis and comparative quantitative analysis of a range of nation-specific datasets, the authors aim to address the following key questions: (1) Mapping the UK E&T policy contexts: In what key areas is post-16 E&T policy in the four nations converging and diverging?; (2) Comparing routes and choices: How are differences in post-16 institutions and policies reflected in participation and pathways for learners?; and (3) Outcomes and inequalities: How are differences in post-16 institutions and policies reflected in learner outcomes?

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field., Comment: 16 pages, 2 figures, 1 table, 1 supplemental table

Being able to accurately monitor the screen exposure of young children is important for research on phenomena linked to screen use such as childhood obesity, physical activity, and social interaction. Most existing studies rely upon self-report or manual measures from bulky wearable sensors, thus lacking efficiency and accuracy in capturing quantitative screen exposure data. In this work, we developed a novel sensor informatics framework that utilizes egocentric images from a wearable sensor, termed the screen time tracker (STT), and a vision language model (VLM). In particular, we devised a multi-view VLM that takes multiple views from egocentric image sequences and interprets screen exposure dynamically. We validated our approach by using a dataset of children's free-living activities, demonstrating significant improvement over existing methods in plain vision language models and object detection models. Results supported the promise of this monitoring approach, which could optimize behavioral research on screen exposure in children's naturalistic settings., Comment: Prepare for submission

We present the largest Galactic neutral hydrogen HI absorption survey to date, utilizing the Australian SKA Pathfinder Telescope at an unprecedented spatial resolution of 30''. This survey, GASKAP-HI, unbiasedly targets 2,714 continuum background sources over 250 square degrees in the direction of the Magellanic Clouds, a significant increase compared to a total of 373 sources observed by previous Galactic absorption surveys across the entire Milky Way. We aim to investigate the physical properties of cold (CNM) and warm (WNM) neutral atomic gas in the Milky Way foreground, characterized by two prominent filaments at high Galactic latitudes (between $-45^{\circ}$ and $-25^{\circ}$). We detected strong HI absorption along 462 lines of sight above the 3$\sigma$ threshold, achieving an absorption detection rate of 17%. GASKAP-HI's unprecedented angular resolution allows for simultaneous absorption and emission measurements to sample almost the same gas clouds along a line of sight. A joint Gaussian decomposition is then applied to absorption-emission spectra to provide direct estimates of HI optical depths, temperatures, and column densities for the CNM and WNM components. The thermal properties of CNM components are consistent with those previously observed along a wide range of Solar neighborhood environments, indicating that cold HI properties are widely prevalent throughout the local interstellar medium. Across our region of interest, CNM accounts for ~30% of the total HI gas, with the CNM fraction increasing with column density toward the two filaments. Our analysis reveals an anti-correlation between CNM temperature and its optical depth, which implies that CNM with lower optical depth leads to a higher temperature., Comment: Largest Galactic HI Absorption Survey To Date (GASKAP-HI): Cold Atomic Gas in the Magellanic Cloud foreground using Australian SKA Pathfinder. This paper has 19 pages, 17 figures. This paper has been accepted for publication in Monthly Notices of the Royal Astronomical Society Main Journal

In recent years, several computational tools have been developed to detect and identify sexism, misogyny, and gender-based hate speech, especially on online platforms. Though these tools intend to draw on knowledge from both social science and computer science, little is known about the current state of research in quantifying online sexism or misogyny. Given the growing concern over the discrimination of women in online spaces and the rise in interdisciplinary research on capturing the online manifestation of sexism and misogyny, a systematic literature review on the research practices and their measures is the need of the hour. We make three main contributions: (i) we present a semi-automated way to narrow down the search results in the different phases of selection stage in the PRISMA flowchart; (ii) we perform a systematic literature review of research papers that focus on the quantification and measurement of online gender-based hate speech, examining literature from computer science and the social sciences from 2012 to 2022; and (iii) we identify the opportunities and challenges for measuring gender-based online hate speech. Our findings from topic analysis suggest a disciplinary divide between the themes of research on sexism/misogyny. With evidence-based review, we summarise the different approaches used by the studies who have explored interdisciplinary approaches to bridge the knowledge gap. Coupled with both the existing literature on social science theories and computational modeling, we provide an analysis of the benefits and shortcomings of the methodologies used. Lastly, we discuss the challenges and opportunities for future research dedicated to measuring online sexism and misogyny.

Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $\varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort (n = 1,592) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.

We present recent laboratory results demonstrating high-contrast coronagraphy for the future space-based large IR/Optical/Ultraviolet telescope recommended by the Decadal Survey. The High-contrast Imager for Complex Aperture Telescopes (HiCAT) testbed aims to implement a system-level hardware demonstration for segmented aperture coronagraphs with wavefront control. The telescope hardware simulator employs a segmented deformable mirror with 37 hexagonal segments that can be controlled in piston, tip, and tilt. In addition, two continuous deformable mirrors are used for high-order wavefront sensing and control. The low-order sensing subsystem includes a dedicated tip-tilt stage, a coronagraphic target acquisition camera, and a Zernike wavefront sensor that is used to measure and correct low-order aberration drifts. We explore the performance of a segmented aperture coronagraph both in static operations (limited by natural drifts and instabilities) and in dynamic operations (in the presence of artificial wavefront drifts added to the deformable mirrors), and discuss the estimation and control strategies used to reach and maintain the dark-zone contrast using our low-order wavefront sensing and control. We summarize experimental results that quantify the performance of the testbed in terms of contrast, inner/outer working angle and bandpass, and analyze limiting factors., Comment: 17 pages, 14 figures, SPIE Astronomical Telescopes + Instrumentation, 2022, Montr\'eal, Qu\'ebec, Canada

We present the final results of the Apodized Pupil Lyot Coronagraph (APLC) on the High-contrast imager for Complex Aperture Telescopes (HiCAT) testbed, under NASA's Strategic Astrophysics Technology program. The HiCAT testbed was developed over the past decade to enable a system-level demonstration of coronagraphy for exoplanet direct imaging with the future Habitable Wolds Observatory. HiCAT includes an active, segmented telescope simulator, a coronagraph, and metrology systems (Low-order and Mid-Order Zernike Wavefront Sensors, and Phase Retrieval camera). These results correspond to an off-axis (un-obscured) configuration, as was envisioned in the 2020 Decadal Survey Recommendations. Narrowband and broadband dark holes are generated using two continuous deformable mirrors (DM) to control high order wavefront aberrations, and low-order drifts can be further stabilized using the LOWFS loop. The APLC apodizers, manufactured using carbon nanotubes, were optimized for broadband performance and include the calibrated geometric aperture. HiCAT is, to this date, the only testbed facility able to demonstrate high-contrast coronagraphy with a truly segmented aperture, as is required for the Habitable World Observatory, albeit limited to ambient conditions. Results presented here include $6\times 10^{-8}$ (90% CI) contrast in 9% bandpass in a 360 deg dark hole with inner and outer working angles of $4.4 \lambda/D_{pupil}$ and $11 \lambda/D_{pupil}$ . Narrowband contrast (3% bandpass) reaches $2.4\times 10^{-8}$ (90% confidence interval)., Comment: 17 pages, 16 figures, SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

Societal risk emanating from how recommender algorithms disseminate content online is now well documented. Emergent regulation aims to mitigate this risk through ethical audits and enabling new research on the social impact of algorithms. However, there is currently a need for tools and methods that enable such evaluation. This paper presents ARTAI, an evaluation environment that enables large-scale assessments of recommender algorithms to identify harmful patterns in how content is distributed online and enables the implementation of new regulatory requirements for increased transparency in recommender systems., Comment: 3 pages, 1 figure, accepted at FAccTRec 2024 Workshop, RecSys 2024

This paper presents a diffusion-based recommender system that incorporates classifier-free guidance. Most current recommender systems provide recommendations using conventional methods such as collaborative or content-based filtering. Diffusion is a new approach to generative AI that improves on previous generative AI approaches such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). We incorporate diffusion in a recommender system that mirrors the sequence users take when browsing and rating items. Although a few current recommender systems incorporate diffusion, they do not incorporate classifier-free guidance, a new innovation in diffusion models as a whole. In this paper, we present a diffusion recommender system that augments the underlying recommender system model for improved performance and also incorporates classifier-free guidance. Our findings show improvements over state-of-the-art recommender systems for most metrics for several recommendation tasks on a variety of datasets. In particular, our approach demonstrates the potential to provide better recommendations when data is sparse., Comment: 8 pages

Let $R$ be a finite non-commutative ring with $1\ne 0$. By a polynomial function on $R$, we mean a function $F\colon R\longrightarrow R$ induced by a polynomial $f=\sum\limits_{i=0}^{n}a_ix^i\in R[x]$ via right substitution of the variable $x$, i.e. $F(a)=f(a)= \sum\limits_{i=0}^{n}a_ia^i$ for every $a\in R$. In this paper, we study the polynomial functions of the free $R$-algebra with a central basis $\{1,\beta_1,\ldots,\beta_k\}$ ($k\ge 1$) such that $\beta_i\beta_j=0$ for every $1\le i,j\le k$, $R[\beta_1,\ldots,\beta_k]$. %, the ring of dual numbers over $R$ in $k$ variables. Our investigation revolves around assigning a polynomial $\lambda_f(y,z)$ over $R$ in non-commutative variables $y$ and $z$ to each polynomial $f$ in $R[x]$; and describing the polynomial functions on $R[\beta_1,\ldots,\beta_k]$ through the polynomial functions induced on $R$ by polynomials in $R[x]$ and by their assigned polynomials in the in non-commutative variables $y$ and $z$. %and analyzing the resulting polynomial functions on $R[\beta_1,\ldots,\beta_k]$. By extending results from the commutative case to the non-commutative scenario, we demonstrate that several properties and theorems in the commutative case can be generalized to the non-commutative setting with appropriate adjustments.

One thread of empirical work in social science focuses on decomposing group differences in outcomes into unexplained components and components explained by observable factors. In this paper, we study gender wage decompositions, which require estimating the portion of the gender wage gap explained by career histories of workers. Classical methods for decomposing the wage gap employ simple predictive models of wages which condition on a small set of simple summaries of labor history. The problem is that these predictive models cannot take advantage of the full complexity of a worker's history, and the resulting decompositions thus suffer from omitted variable bias (OVB), where covariates that are correlated with both gender and wages are not included in the model. Here we explore an alternative methodology for wage gap decomposition that employs powerful foundation models, such as large language models, as the predictive engine. Foundation models excel at making accurate predictions from complex, high-dimensional inputs. We use a custom-built foundation model, designed to predict wages from full labor histories, to decompose the gender wage gap. We prove that the way such models are usually trained might still lead to OVB, but develop fine-tuning algorithms that empirically mitigate this issue. Our model captures a richer representation of career history than simple models and predicts wages more accurately. In detail, we first provide a novel set of conditions under which an estimator of the wage gap based on a fine-tuned foundation model is $\sqrt{n}$-consistent. Building on the theory, we then propose methods for fine-tuning foundation models that minimize OVB. Using data from the Panel Study of Income Dynamics, we find that history explains more of the gender wage gap than standard econometric models can measure, and we identify elements of history that are important for reducing OVB.

Understanding the role that magnetic fields play on the stage of galaxy formation requires accurate methods for inferring the properties of extragalactic magnetic fields. Radio synchrotron emission has been the most promising avenue to infer magnetic field strengths across galaxies, with the application of a central assumption: that galactic cosmic rays are in energy equipartition with the magnetic field. In this work, we leverage flexible synthetic observations of a high-resolution magnetohydrodynamic simulation of a Milky Way-like galaxy to review whether true equipartition is capable of reproducing radio observations of galaxies, and investigate its impact on the inference of magnetic field strengths when varying the properties and density distribution of the cosmic rays. We find that imposing equipartition (regardless of scale length) results in cosmic ray electron densities that are unable to generate either the amplitude or the shape of the radio intensity profiles typically observed in spiral galaxies. Instead, observationally motivated smooth distributions of cosmic ray electrons across the galaxy provide a remarkable match to observations. We further demonstrate that assuming equipartition with those mock observations can lead to significant overestimation of the magnetic field strength ($\sim10-50\times$). This overestimation varies with cosmic ray electron densities, cosmic ray spectrum power-law index, and galactic environment, aggravated in inter-arm regions and attenuated in star-forming regions. Our results promote caution when assuming equipartition in observations, and suggest that additional theoretical and numerical work is required to leverage the upcoming generation of radio observations poised to revolutionize our understanding of astrophysical magnetic fields., Comment: Submitted to ApJ, 26 pages, 12 figures

We conduct a systematic search for astrophysical transients using data from the Atacama Cosmology Telescope (ACT). The data were taken from 2017 to 2022 in three frequency bands spanning 77 GHz to 277 GHz. In this paper we present a pipeline for transient detection using single observation maps where each pixel of a map contains one observation with an integration time of approximately four minutes. We find 34 transient events at 27 unique locations. All but two of the transients are associated with Galactic stars and exhibit a wide range of properties. We also detect an event coincident with the classical nova, YZ Ret and one event consistent with a flaring active galactic nucleus. We notably do not detect any reverse shock emission from gamma ray bursts, a non-detection which is in tension with current models., Comment: 23 pages, 10 figures, 10 tables. First and second author share equal contributions. Article and accompanying data submitted to ApJ. Data tables will be made available upon publication

Negation is a fundamental linguistic concept used by humans to convey information that they do not desire. Despite this, there has been minimal research specifically focused on negation within vision-language tasks. This lack of research means that vision-language models (VLMs) may struggle to understand negation, implying that they struggle to provide accurate results. One barrier to achieving human-level intelligence is the lack of a standard collection by which research into negation can be evaluated. This paper presents the first large-scale dataset, Negative Instruction (NeIn), for studying negation within the vision-language domain. Our dataset comprises 530,694 quadruples, i.e., source image, original caption, negative sentence, and target image in total, including 495,694 queries for training and 35,000 queries for benchmarking across multiple vision-language tasks. Specifically, we automatically generate NeIn based on a large, existing vision-language dataset, MS-COCO, via two steps: generation and filtering. During the generation phase, we leverage two VLMs, BLIP and MagicBrush, to generate the target image and a negative clause that expresses the content of the source image. In the subsequent filtering phase, we apply BLIP to remove erroneous samples. Additionally, we introduce an evaluation protocol for negation understanding of image editing models. Extensive experiments using our dataset across multiple VLMs for instruction-based image editing tasks demonstrate that even recent state-of-the-art VLMs struggle to understand negative queries. The project page is: https://tanbuinhat.github.io/NeIn/

Rare-earth oxides (REOs) are an important class of materials owing to their unique properties, including high ionic conductivities, large dielectric constants, and elevated melting temperatures, making them relevant to several technological applications such as catalysis, ionic conduction, and sensing. The ability to predict these properties at moderate computational cost is essential to guiding materials discovery and optimizing materials performance. Although density-functional theory (DFT) is the favored approach for predicting electronic and atomic structures, its accuracy is limited in describing strong electron correlation and localization inherent to REOs. The newly developed strongly constrained and appropriately normed (SCAN) meta-generalized-gradient approximations (meta-GGAs) promise improved accuracy in modeling these strongly correlated systems. We assess the performance of these meta-GGAs on binary REOs by comparing the numerical accuracy of thirteen exchange-correlation approximations in predicting structural, magnetic, and electronic properties. Hubbard U corrections for self-interaction errors and spin-orbit coupling are systematically considered. Our comprehensive assessment offers insights into the physical properties and functional performance of REOs predicted by first-principles and provides valuable guidance for selecting optimal DFT functionals for exploring these materials.

Stellar age estimates are often calculated by interpolating a star's properties in a grid of models. However, different model grids will give different ages for the same star. We used the open cluster M67 to compare four different model grids: DSEP, GARSTEC, MIST, and YREC. Across all model grids, age estimates for main sequence stars were consistently higher than the accepted age of M67, while age estimates for red giant stars were lower. We compared model-generated age and mass values to external constraints as an additional test of the reliability of each model grid. For stars near solar age and metallicity, we recommend using the DSEP model grid to estimate the ages of main sequence stars and the GARSTEC model grid for red giant stars.

Since Parker Solar Probe's (Parker's) first perihelion pass at the Sun, large amplitude Alfv\'en waves grouped in patches have been observed near the Sun throughout the mission. Several formation processes for these magnetic switchback patches have been suggested with no definitive consensus. To provide insight to their formation, we examine the heavy ion properties of several adjacent magnetic switchback patches around Parker's 11th perihelion pass capitalizing on a spacecraft lineup with Solar Orbiter where each samples the same solar wind streams over a large range of longitudes. Heavy ion properties (Fe/O, C$^{6+}$/C$^{5+}$, O$^{7+}$/O$^{6+}$) related to the wind's coronal origin, measured with Solar Orbiter can be linked to switchback patch structures identified near the Sun with Parker. We find that switchback patches do not contain distinctive ion and elemental compositional signatures different than the surrounding non-switchback solar wind. Both the patches and ambient wind exhibit a range of fast and slow wind qualities, indicating coronal sources with open and closed field lines in close proximity. These observations and modeling indicate switchback patches form in coronal hole boundary wind and with a range of source region magnetic and thermal properties. Furthermore, the heavy ion signatures suggest interchange reconnection and/or shear driven processes may play a role in their creation., Comment: Accepted for publication in ApJ on September 4th, 2024

Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forgetfulness and disengagement. To address this, we developed Oralytics, a mHealth intervention system designed to complement clinician-delivered preventative care for marginalized individuals at risk for dental disease. Oralytics incorporates an online reinforcement learning algorithm to determine optimal times to deliver intervention prompts that encourage oral self-care behaviors. We have deployed Oralytics in a registered clinical trial. The deployment required careful design to manage challenges specific to the clinical trials setting in the U.S. In this paper, we (1) highlight key design decisions of the RL algorithm that address these challenges and (2) conduct a re-sampling analysis to evaluate algorithm design decisions. A second phase (randomized control trial) of Oralytics is planned to start in spring 2025.

This paper introduces a novel fingerprint classification technique based on a multi-layered fuzzy logic classifier. We target the cause of missed detection by identifying the fingerprints at an early stage among dry, standard, and wet. Scanned images are classified based on clarity correlated with the proposed feature points. We also propose a novel adaptive algorithm based on eigenvector space for generating new samples to overcome the multiclass imbalance. Proposed methods improve the performance of ensemble learners. It was also found that the new approach performs better than the neural-network based classification methods. Early-stage improvements give a suitable dataset for fingerprint detection models. Leveraging the novel classifier, the best set of `standard' labelled fingerprints is used to generate a unique hybrid fingerprint orientation map (HFOM). We introduce a novel min-rotate max-flow optimization method inspired by the min-cut max-flow algorithm. The unique properties of HFOM generation introduce a new use case for biometric data protection by using HFOM as a virtual proxy of fingerprints., Comment: 10 pages, 18 figures, 4 Tables The work mainly focuses on fingerprint classification and hybrid fingerprint orientation map (HFOM) generation. It highlights the security use cases of HFOM, eg. data encryption

Online AI decision-making algorithms are increasingly used by digital interventions to dynamically personalize treatment to individuals. These algorithms determine, in real-time, the delivery of treatment based on accruing data. The objective of this paper is to provide guidelines for enabling effective monitoring of online decision-making algorithms with the goal of (1) safeguarding individuals and (2) ensuring data quality. We elucidate guidelines and discuss our experience in monitoring online decision-making algorithms in two digital intervention clinical trials (Oralytics and MiWaves). Our guidelines include (1) developing fallback methods, pre-specified procedures executed when an issue occurs, and (2) identifying potential issues categorizing them by severity (red, yellow, and green). Across both trials, the monitoring systems detected real-time issues such as out-of-memory issues, database timeout, and failed communication with an external source. Fallback methods prevented participants from not receiving any treatment during the trial and also prevented the use of incorrect data in statistical analyses. These trials provide case studies for how health scientists can build monitoring systems for their digital intervention. Without these algorithm monitoring systems, critical issues would have gone undetected and unresolved. Instead, these monitoring systems safeguarded participants and ensured the quality of the resulting data for updating the intervention and facilitating scientific discovery. These monitoring guidelines and findings give digital intervention teams the confidence to include online decision-making algorithms in digital interventions.

After leaving the Sun's corona, the solar wind continues to accelerate and cools, but more slowly than expected for a freely expanding adiabatic gas. We use in situ measurements from the Parker Solar Probe and Solar Orbiter spacecrafts to investigate a stream of solar wind as it traverses the inner heliosphere. The observations show heating and acceleration of the the plasma between the outer edge of the corona and near the orbit of Venus, in connection to the presence of large amplitude Alfv\'en waves. Alfv\'en waves are perturbations in the interplanetary magnetic field that transport energy. Our calculations show the damping and mechanical work performed by the Alfv\'en waves is sufficient to power the heating and acceleration of the fast solar wind in the inner heliosphere., Comment: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on August 30 2024, DOI: 10.1126/science.adk6953

The escalating prevalence of cannabis use poses a significant public health challenge globally. In the U.S., cannabis use is more prevalent among emerging adults (EAs) (ages 18-25) than any other age group, with legalization in the multiple states contributing to a public perception that cannabis is less risky than in prior decades. To address this growing concern, we developed MiWaves, a reinforcement learning (RL) algorithm designed to optimize the delivery of personalized intervention prompts to reduce cannabis use among EAs. MiWaves leverages domain expertise and prior data to tailor the likelihood of delivery of intervention messages. This paper presents a comprehensive overview of the algorithm's design, including key decisions and experimental outcomes. The finalized MiWaves RL algorithm was deployed in a clinical trial from March to May 2024., Comment: arXiv admin note: substantial text overlap with arXiv:2402.17739

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating computation and experiment to understand and explore single-phase rock salt high-entropy oxides. By leveraging a machine-learning interatomic potential, we rapidly and accurately map high-entropy composition space using our two descriptors: bond length distribution and mixing enthalpy. The single-phase stabilities for all experimentally stabilized rock salt compositions are correctly resolved, with dozens more compositions awaiting discovery.

We extend the dust-filament-based model presented in Herv\'ias-Caimapo & Huffenberger 2022 to produce parity-violating foreground spectra by manipulating the filament orientations relative to the magnetic field. We calibrate our model to observations of the misalignment angle using cross-correlations of Planck and HI 21-cm line data, producing a fiducial model that predicts a $\mathcal{D}_{\ell}^{EB}\sim$few $\mu$K$^2$ dust signal at 353 GHz and where $\sim 56$% of filaments have a positive misalignment angle. The main purpose of this model is to be used as dust with non-zero parity-violating emission in forecasting a measurement of cosmic birefringence by upcoming experiments. Here, we also use our fiducial model to assess the impact of dust in measurements of the isotropic cosmic birefringence angle $\beta$ with Planck data by measuring the misalignment angle as a function of scale, as well as directly using our model's $\mathcal{D}_{\ell}^{EB}$ prediction as a template. In both cases, we measure $\beta$ to be consistent within $0.83\sigma$ of the equivalent measurements with Planck data and its derivatives., Comment: 30 pages, 17 figures, 5 tables, 3 apendices. To be submitted to PRD

Hedges allow speakers to mark utterances as provisional, whether to signal non-prototypicality or "fuzziness", to indicate a lack of commitment to an utterance, to attribute responsibility for a statement to someone else, to invite input from a partner, or to soften critical feedback in the service of face-management needs. Here we focus on hedges in an experimentally parameterized corpus of 63 Roadrunner cartoon narratives spontaneously produced from memory by 21 speakers for co-present addressees, transcribed to text (Galati and Brennan, 2010). We created a gold standard of hedges annotated by human coders (the Roadrunner-Hedge corpus) and compared three LLM-based approaches for hedge detection: fine-tuning BERT, and zero and few-shot prompting with GPT-4o and LLaMA-3. The best-performing approach was a fine-tuned BERT model, followed by few-shot GPT-4o. After an error analysis on the top performing approaches, we used an LLM-in-the-Loop approach to improve the gold standard coding, as well as to highlight cases in which hedges are ambiguous in linguistically interesting ways that will guide future research. This is the first step in our research program to train LLMs to interpret and generate collateral signals appropriately and meaningfully in conversation., Comment: Amie Paige, Adil Soubki, and John Murzaku contributed equally to this study

SuperBIT was a 0.5-meter near-ultraviolet to near-infrared wide-field telescope that launched on a NASA superpressure balloon into the stratosphere from New Zealand for a 45-night flight. SuperBIT acquired multi-band images of galaxy clusters to study the properties of dark matter using weak gravitational lensing. We provide an overview of the instrument and its various subsystems. We then present the instrument performance from the flight, including the telescope and image stabilization system, the optical system, the power system, and the thermal system. SuperBIT successfully met the instrument's technical requirements, achieving a telescope pointing stability of 0.34 +/- 0.10 arcseconds, a focal plane image stability of 0.055 +/- 0.027 arcseconds, and a PSF FWHM of ~ 0.35 arcseconds over 5-minute exposures throughout the 45-night flight. The telescope achieved a near-diffraction limited point-spread function in all three science bands (u, b, and g). SuperBIT served as a pathfinder to the GigaBIT observatory, which will be a 1.34-meter near-ultraviolet to near-infrared balloon-borne telescope., Comment: 17 pages, 25 pages, published in the Astronomical Journal

School re-entry for pregnant girls and young mothers is increasingly recognized by African governments as a critical measure to improve the education of marginalized adolescents and mitigate school pushout. In Uganda, the high rate of teenage pregnancy, exacerbated by the COVID-19 pandemic, prompted the government to approve "Revised Guidelines for the Prevention and Management of Teenage Pregnancy in School Settings in Uganda," which outline necessary steps and measures within school environments to facilitate the safe return of girls to school post-pregnancy. However, a significant gap in implementation persists. This policy brief addresses the disparity between policy and actual practice regarding the re-entry of pregnant girls and young mothers into schools, particularly in the refugee and host communities of Uganda's West Nile region. It underscores the community practices that deny opportunities to this demographic and delineates the intersectional barriers, social, economic, and policy-based that obstruct their educational continuation. Furthermore, the brief offers policy recommendations to overcome these obstacles. Overall, the brief emphasizes a call to action for stakeholders to prioritize the educational needs of marginalized populations, specifically pregnant teenagers and young mothers in refugee and host communities, to ensure that they can lead dignified lives.