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The Institute of Education Sciences (IES), the research arm of the U.S. Department of Education, is the nation's leading source for rigorous, independent education research, evaluation, and statistics. IES's National Center for Education Research (NCER) supports rigorous research that addresses the nation's most pressing education needs from early childhood to adult education. IES's National Center for Special Education Research (NCSER) supports a comprehensive program of education research designed to expand knowledge and understanding of infants, toddlers, and youth with and at risk for disabilities to improve their developmental, education, transition, and postsecondary outcomes. This paper makes the case that IES Development and Innovation research can support the development of usable, feasible, and affordable approaches (practices, programs, or policies) to help schools meet the mental health needs of their students and staff. The goal of this research is to ensure that school-based interventions are contextually appropriate, implemented with high fidelity, and more likely to produce equitable outcomes than current practice. [This paper was published in the "School Mental Health".]

This report is the result of the participation of hundreds of students, families, school educators, and researchers who dedicated their time and energy to investigating the critical role that families and communities play in ensuring students and schools can flourish. It is a culmination of over two years of collaborative research and hundreds of conversations on six continents. While there were unique findings in each school, district, and country, six powerful lessons stand out across geographies and contexts. This research report delves into these lessons and how to build greater family, school, and community partnerships as seen through the eyes of families, educators, and students who shared their beliefs on, experiences with, and trust in schools. After venturing into government schools across rural and urban districts in Sierra Leone to facilitate conversations with families and communities, one of the lead researchers noted that there is a crucial and symbiotic relationship between schools, families, and communities that are often overlook.

We study maximal almost rigid modules over a gentle algebra $A$. We prove that the number of indecomposable direct summands of every maximal almost rigid $A$-module is equal to the sum of the number of vertices and the number of arrows of the Gabriel quiver of $A$. Moreover, the algebra $A$, considered as an $A$-module, can be completed to a maximal almost rigid module in a unique way. Gentle algebras are precisely the tiling algebras of surfaces with marked points. We show that the (permissible) triangulations of the surface of $A$ are in bijection with the maximal almost rigid $A$-modules. Furthermore, we study the endomorphism algebra $C=\text{End}_A T$ of a maximal almost rigid module $T$. We construct a fully faithful functor $G\colon \text{mod}\,A\to \text{mod}\, \overline{A}$ into the module category of a bigger gentle algebra $\overline{A}$ and show that $G$ maps maximal almost rigid $A$-modules to tilting $\overline{A}$-modules. In particular, $C$ and $\overline{A}$ are derived equivalent and $C$ is gentle. After giving a geometric realization of the functor $G$, we obtain a tiling $G(\mathbf{T})$ of the surface of $\overline{A}$ as the image of the triangulation $\mathbf{T}$ corresponding to $T$. We then show that the tiling algebra of $G(\mathbf{T})$ is $C$. Moreover, the tiling algebra of $\mathbf{T}$ is obtained algebraically from $C$ as the tensor algebra with respect to the $C$-bimodule $\text{Ext}_C^2(DC,C)$, which also is fundamental in cluster-tilting theory., Comment: 50 pages, 35 figures. Comments are welcome!

We present here the current state of a collection of promising ultraviolet technologies in preparation for the Habitable Worlds Observatory. Working with experts representing a significant number of groups working in the ultraviolet, we summarize some of the leading science drivers, present an argument for a 100 nm blue wavelength cutoff, and gather current state of the art of UV technologies. We present the state of the art of contamination control, a crucial piece of the UV instrument plan. We explore next steps with individual technologies, as well as present paths forward with systems level testing and development.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

We construct time-evolving gravitational potential models for a Milky Way-mass galaxy from the FIRE-2 suite of cosmological-baryonic simulations using basis function expansions. These models capture the angular variation with spherical harmonics for the halo and azimuthal harmonics for the disk, and the radial or meridional plane variation with splines. We fit low-order expansions (4 angular/harmonic terms) to the galaxy's potential for each snapshot, spaced roughly 25 Myr apart, over the last 4 Gyr of its evolution, then extract the forces at discrete times and interpolate them between adjacent snapshots for forward orbit integration. Our method reconstructs the forces felt by simulation particles with high fidelity, with 95% of both stars and dark matter, outside of self-gravitating subhalos, exhibiting errors $\leq4\%$ in both the disk and the halo. Imposing symmetry on the model systematically increases these errors, particularly for disk particles, which show greater sensitivity to imposed symmetries. The majority of orbits recovered using the models exhibit positional errors $\leq10\%$ for 2-3 orbital periods, with higher errors for orbits that spend more time near the galactic center. Approximate integrals of motion are retrieved with high accuracy even with a larger potential sampling interval of 200 Myr. After 4 Gyr of integration, 43% and 70% of orbits have total energy and angular momentum errors within 10%, respectively. Consequently, there is higher reliability in orbital shape parameters such as pericenters and apocenters, with errors $\sim10\%$ even after multiple orbital periods. These techniques have diverse applications, including studying satellite disruption in cosmological contexts., Comment: 28 pages, 13 figures, and 4 tables. Submitted to APJ

We present an atmospheric retrieval analysis on a set of young, cloudy, red L-dwarfs -- CWISER J124332.12+600126.2 and WISEP J004701.06+680352.1 -- using the \textit{Brewster} retrieval framework. We also present the first elemental abundance measurements of the young K-dwarf (K0) host star, BD+60 1417 using high resolution~(R = 50,000) spectra taken with PEPSI/LBT. In the complex cloudy L-dwarf regime the emergence of condensate cloud species complicates retrieval analysis when only near-infrared data is available. We find that for both L dwarfs in this work, despite testing three different thermal profile parameterizations we are unable to constrain reliable abundance measurements and thus the C/O ratio. While we can not conclude what the abundances are, we can conclude that the data strongly favor a cloud model over a cloudless model. We note that the difficulty in retrieval constraints persists regardless of the signal to noise of the data examined (S/N $\sim$ 10 for CWISER J124332.12+600126.2 and~40 for WISEP J004701.06+680352.1). The results presented in this work provide valuable lessons about retrieving young, low-surface gravity, cloudy L-dwarfs. This work provides continued evidence of missing information in models and the crucial need for JWST to guide and inform retrieval analysis in this regime., Comment: Accepted to ApJ

High-quality, stable child care and early education (CCEE) can have lasting, positive impacts on children. However, the challenges of recruiting, strengthening, and retaining the CCEE workforce are well documented. CCEE educators typically have low levels of formal education and compensation; limited opportunities for education, training, and professional development; inconsistent working conditions; and high levels of stress and burnout. Additionally, the CCEE sector is well known for high turnover rates, which can strain remaining educators and decrease the quality of care they offer. Turnover can also lead to diminishing returns on an organization's professional development investments. The COVID-19 pandemic has exacerbated these issues. The Building and Sustaining the Child Care and Early Education Workforce (BASE) project conducted an environmental scan and a literature review to identify and document existing knowledge about the CCEE workforce and strategies to strengthen it. One of the main themes identified from this effort was a need for more and better data on the workforce dynamics of CCEE educators. In particular, data are needed on who enters, advances in, stays in, and exits different roles, settings, and types of CCEE care or leaves the field altogether--as well as when, how, and why they do. This information is important for understanding workforce dynamics and informing the development, evaluation, and improvement of strategies that effectively build and sustain a qualified and stable CCEE workforce. For this reason, the BASE project team conducted a data scan to summarize the landscape of existing data sources that may address these gaps and identify areas where future data collection may be most useful. This brief summarizes the findings from the data scan.

Although more Latinos are entering the STEM workforce, they continue to be underrepresented in high-salary STEM occupations. To ensure America's future, institutions and STEM employers must both play an active role in preparing, selecting, and sourcing Latino talent for a global economy. Excelencia's research demonstrates that select institutions are investing in the recruitment, development, and completion of Latino talent and STEM employers have opportunities to strengthen their workforce. To bridge this gap, Excelencia has analyzed national datasets to connect STEM employers with institutions that are graduating and preparing Latinos for the workforce. This brief identifies the top institutions graduating Latinos (2019-20) in STEM from certificates to the doctorate level. These top 25 institutions are meeting the nation's economic needs by intentionally serving Latino students and producing Latino talent. This brief also highlights institutions and their intentional efforts to support post-completion success by providing educational credentials needed to support the current and future STEM workforce.

The nation is facing a shortage of educators prepared to meet the needs of an increasingly diverse student population. Latinos are the nation's fastest growing population but are underrepresented in higher-skilled occupations in the classroom and overrepresented in supportive roles with lower wages. This shows the need for employers and institutions to create pathways into the field of education as Latinos see the value to educate others and the community. To ensure America's future, institutions and employers in education must both play an active role in preparing, selecting, and sourcing Latino talent for a global economy. To bridge this gap, "Excelencia" has analyzed national datasets to connect employers in education with institutions that are graduating and preparing Latinos for the workforce. This brief identifies the top institutions graduating Latinos (2019-20) from certificates to the doctoral level in education. These top 25 institutions are meeting the nation's economic needs by intentionally serving Latino students and producing Latino talent. This brief also highlights institutions and their intentional efforts to support post-completion success by providing education credentials needed to support the current and future education workforce.

It is now well established that the COVID-19 pandemic had a devastating and unequal impact on student achievement. Test score declines were disproportionately large for historically marginalized students, exacerbating preexisting achievement gaps and threatening educational and economic inequality. In this paper, we use longitudinal student-level NWEA MAP Growth test data to estimate differences in test score declines for students at different points on the prepandemic test distribution. We also test the extent to which students' schools and districts accounted for these differences in declines. We find significant differences in learning loss by baseline achievement, with lower-achieving student's scores dropping 0.100 SD more in math and 0.113 SD more in reading than higher-achieving students' scores. We additionally show that the school a student attended accounts for about three-quarters of this widening gap in math achievement and about one-third in reading. The findings suggest school and district-level policies may have mattered more for learning loss than individual students' experiences within schools and districts. Such nuanced information regarding the variation in the pandemic's impacts on students is critical for policymakers and practitioners designing targeted academic interventions and for tracking disparities in academic recovery. [Additional funding for this report was provided by Kenneth C. Griffin.]

Multilingual learners (MLs) are students whose primary language is not English and are eligible for English language development services through the Transitional Bilingual Instruction Program (TBIP). Eligible MLs receive TBIP services until they become proficient in English. During the 2022-23 school year, 149,357 students were identified as multilingual learners.

The EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) is a balloon-borne telescope designed to survey star formation over cosmological time scales using intensity mapping in the 420 - 540 GHz frequency range. EXCLAIM uses a fully cryogenic telescope coupled to six on-chip spectrometers featuring kinetic inductance detectors (KIDs) to achieve high sensitivity, allowing for fast integration in dark atmospheric windows. The telescope receiver is cooled to $\approx$ 1.7 K by immersion in a superfluid helium bath and enclosed in a superfluid-tight shell with a meta-material anti-reflection coated silicon window. In addition to the optics and the spectrometer package, the receiver contains the magnetic shielding, the cryogenic segment of the spectrometer readout, and the sub-Kelvin cooling system. A three-stage continuous adiabatic demagnetization refrigerator (CADR) keeps the detectors at 100 mK while a $^4$He sorption cooler provides a 900 mK thermal intercept for mechanical suspensions and coaxial cables. We present the design of the EXCLAIM receiver and report on the flight-like testing of major receiver components, including the superfluid-tight receiver window and the sub-Kelvin coolers., Comment: 13 pages, 7 figures, 1 table

Due to the unprecedented signal strengths offered by the newest high-resolution spectrographs on 10-m class telescopes, exploring the 3D nature of exoplanets is possible with an unprecedented level of precision. In this paper, we present a new technique to probe the vertical structure of exoplanetary winds and dynamics using ensembles of planet absorption lines of varying opacity, and apply it to the well-studied ultra-hot Jupiter WASP-76b. We then compare these results to state-of-the-art global circulation models (GCMs) with varying magnetic drag prescriptions. We find that the known asymmetric velocity shift in Fe I absorption during transit persists at all altitudes, and observe tentative trends for stronger blueshifts and more narrow line profiles deeper in the atmosphere. By comparing three different model prescriptions (a hydrodynamical model with no drag, a magnetic drag model, and a uniform drag model) we are able to rule out the uniform drag model due to inconsistencies with observed trends in the data. We find that the magnetic model is slightly favored over the the hydrodynamic model, and note that this 3-Gauss kinematic magnetohydrodynamical GCM is also favored when compared to low-resolution data. Future generation high-resolution spectrographs on Extremely large telescopes (ELTs) will greatly increase signals and make methods like these possible with higher precision and for a wider range of objects., Comment: 20 pages, 10 figures, accepted to ApJ

The online ordered Ramsey game is played between two players, Builder and Painter, on an infinite sequence of vertices with ordered graphs $(G_1,G_2)$, which have linear orderings on their vertices. On each turn, Builder first selects an edge before Painter colors it red or blue. Builder's objective is to construct either an ordered red copy of $G_1$ or an ordered blue copy of $G_2$, while Painter's objective is to delay this for as many turns as possible. The online ordered Ramsey number $r_o(G_1,G_2)$ is the number of turns Builder takes to win in the case that both players play optimally. Few lower bounds are known for this quantity. In this paper, we introduce a succinct proof of a new lower bound based on the maximum left- and right-degrees in the ordered graphs. We also upper bound $r_o(G_1,G_2)$ in two cases: when $G_1$ is a cycle and $G_2$ a complete bipartite graph, and when $G_1$ is a tree and $G_2$ a clique., Comment: 8 pages

Red Giant stars host solar-like oscillations which have mixed character, being sensitive to conditions both in the outer convection zone and deep within the interior. The properties of these modes are sensitive to both core rotation and magnetic fields. While asteroseismic studies of the former have been done on a large scale, studies of the latter are currently limited to tens of stars. We aim to produce the first large catalogue of both magnetic and rotational perturbations. We jointly constrain these parameters by devising an automated method for fitting the power spectra directly. We successfully apply the method to 302 low-luminosity red giants. We find a clear bimodality in core rotation rate. The primary peak is at $\delta \nu_{\mathrm{rot}}$ = 0.32 $\mu$Hz, and the secondary at $\delta \nu_{\mathrm{rot}}$ = 0.47 $\mu$Hz. Combining our results with literature values, we find that the percentage of stars rotating much more rapidly than the population average increases with evolutionary state. We measure magnetic splittings of 2$\sigma$ significance in 23 stars. While the most extreme magnetic splitting values appear in stars with masses > 1.1M$_{\odot}$, implying they formerly hosted a convective core, a small but statistically significant magnetic splitting is measured at lower masses. Asymmetry between the frequencies of a rotationally split multiplet has previously been used to diagnose the presence of a magnetic perturbation. We find that of the stars with a significant detection of magnetic perturbation, 43\% do not show strong asymmetry. We find no strong evidence of correlation between the rotation and magnetic parameters., Comment: 18 pages, 23 figures, accepted for publication in MNRAS

Diphthong vowels exhibit a degree of inherent dynamic change, the extent of which can vary synchronically and diachronically, such that diphthong vowels can become monophthongs and vice versa. Modelling this type of change requires defining diphthongs in opposition to monophthongs. However, formulating an explicit definition has proven elusive in acoustics and articulation, as diphthongisation is often gradient in these domains. In this study, we consider whether diphthong vowels form a coherent phonetic category from the articulatory point of view. We present articulometry and acoustic data from six speakers of Northern Anglo-English producing a full set of phonologically long vowels. We analyse several measures of diphthongisation, all of which suggest that diphthongs are not categorically distinct from long monophthongs. We account for this observation with an Articulatory Phonology/Task Dynamic model in which diphthongs and long monophthongs have a common gestural representation, comprising two articulatory targets in each case, but they differ according to gestural constriction and location of the component gestures. We argue that a two-target representation for all long vowels is independently supported by phonological weight, as well as by the nature of historical diphthongisation and present-day dynamic vowel variation in British English.

We propose a comprehensive survey of giant planets ranging from close-in highly irradiated hot Jupiters to young, wide-orbit directly imaged planets. The combination of two established techniques for probing planetary atmospheric compositions (time-series transit observations and high-contrast spectroscopy) will provide an unprecedented window into gaseous planet compositions across a range of equilibrium temperatures (100-2000 K), orbital separations (0.1-100 au), and system ages (10 Myr-1 Gyr). To-date, compositional measurements of both transiting and directly imaged planets suggest two distinct formation pathways: star-like formation for directly imaged planets and planet-like formation for transiting planets. By leveraging the combined technical and theoretical expertise of the transiting and direct imaging communities, we can obtain a holistic view of giant planet formation, migration, accretion, and evolution. From the results of this comprehensive program, we will begin to answer one of the fundamental outstanding questions in our understanding of giant planets: where and how in the protoplanetary disk do giant planets form?, Comment: 4 pages, 2 figures. This white paper was submitted to the "Working Group on Strategic Exoplanet Initiatives with HST and JWST" call (https://sites.google.com/view/exoplanet-strategy-wg) and referenced in the final working group report (10.48550/arXiv.2404.02932)

Primordial black holes (PBHs) are theorized objects that may make up some - or all - of the dark matter in the universe. At the lowest allowed masses, Hawking radiation (in the form of photons or electrons and positrons) is the primary tool to search for PBHs. This paper is part of an ongoing series in which we aim to calculate the $O(\alpha)$ corrections to Hawking radiation from asteroid-mass primordial black holes, based on a perturbative quantum electrodymanics (QED) calculation on Schwarzschild background. Silva et. al. (2023) divided the corrections into dissipative and conservative parts; this work focuses on the numerical computation of the dissipative $O(\alpha)$ corrections to the photon spectrum. We generate spectra for primordial black holes of mass $M=1$-$8 \times 10^{21} m_{\rm planck}$. This calculation confirms the expectation that at low energies, the inner bremsstrahlung radiation is the dominant contribution to the Hawking radiation spectrum. At high energies, the main $O(\alpha)$ effect is a suppression of the photon spectrum due to pair production (emitted $\gamma\rightarrow e^+e^-$), but this is small compared to the overall spectrum. We compare the low-energy tail in our curved spacetime QED calculation to several approximation schemes in the literature, and find deviations that could have important implications for constraints from Hawking radiation on primordial black holes as dark matter., Comment: 28 pages, 15 figures, to be submitted to Physical Review D, spectra to be released upon acceptance

Training emotion recognition models has relied heavily on human annotated data, which present diversity, quality, and cost challenges. In this paper, we explore the potential of Large Language Models (LLMs), specifically GPT4, in automating or assisting emotion annotation. We compare GPT4 with supervised models and or humans in three aspects: agreement with human annotations, alignment with human perception, and impact on model training. We find that common metrics that use aggregated human annotations as ground truth can underestimate the performance, of GPT-4 and our human evaluation experiment reveals a consistent preference for GPT-4 annotations over humans across multiple datasets and evaluators. Further, we investigate the impact of using GPT-4 as an annotation filtering process to improve model training. Together, our findings highlight the great potential of LLMs in emotion annotation tasks and underscore the need for refined evaluation methodologies., Comment: to be published in Interspeech 2024

Joint injuries, and their long-term consequences, present a substantial global health burden. Wearable prophylactic braces are an attractive potential solution to reduce the incidence of joint injuries by limiting joint movements that are related to injury risk. Given human motion and ground reaction forces, we present a computational framework that enables the design of personalized braces by optimizing the distribution of microstructures and elasticity. As varied brace designs yield different reaction forces that influence kinematics and kinetics analysis outcomes, the optimization process is formulated as a differentiable end-to-end pipeline in which the design domain of microstructure distribution is parameterized onto a neural network. The optimized distribution of microstructures is obtained via a self-learning process to determine the network coefficients according to a carefully designed set of losses and the integrated biomechanical and physical analyses. Since knees and ankles are the most commonly injured joints, we demonstrate the effectiveness of our pipeline by designing, fabricating, and testing prophylactic braces for the knee and ankle to prevent potentially harmful joint movements.

This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale processes, which can introduce substantial uncertainties. RL offers capabilities to enhance these parameterisation schemes, including direct interaction, handling sparse or delayed feedback, continuous online learning, and long-term optimisation. We evaluate the performance of eight RL algorithms on two idealised environments: one for temperature bias correction, another for radiative-convective equilibrium (RCE) imitating real-world computational constraints. Results show different RL approaches excel in different climate scenarios with exploration algorithms performing better in bias correction, while exploitation algorithms proving more effective for RCE. These findings support the potential of RL-based parameterisation schemes to be integrated into global climate models, improving accuracy and efficiency in capturing complex climate dynamics. Overall, this work represents an important first step towards leveraging RL to enhance climate model accuracy, critical for improving climate understanding and predictions. Code accessible at https://github.com/p3jitnath/climate-rl., Comment: 24 pages, 9 figures

Designing and fabricating structures with specific mechanical properties requires understanding the intricate relationship between design parameters and performance. Understanding the design-performance relationship becomes increasingly complicated for nonlinear deformations. Though successful at modeling elastic deformations, simulation-based techniques struggle to model large elastoplastic deformations exhibiting plasticity and densification. We propose a neural network trained on experimental data to learn the design-performance relationship between 3D-printable shells and their compressive force-displacement behavior. Trained on thousands of physical experiments, our network aids in both forward and inverse design to generate shells exhibiting desired elastoplastic and hyperelastic deformations. We validate a subset of generated designs through fabrication and testing. Furthermore, we demonstrate the network's inverse design efficacy in generating custom shells for several applications., Comment: Submitted to 3D Printing and Additive Manufacturing

Large wildlife image collections from camera traps are crucial for biodiversity monitoring, offering insights into species richness, occupancy, and activity patterns. However, manual processing of these data is time-consuming, hindering analytical processes. To address this, deep neural networks have been widely adopted to automate image analysis. Despite their growing use, the impact of model training decisions on downstream ecological metrics remains unclear. Here, we analyse camera trap data from an African savannah and an Asian sub-tropical dry forest to compare key ecological metrics derived from expert-generated species identifications with those generated from deep neural networks. We assess the impact of model architecture, training data noise, and dataset size on ecological metrics, including species richness, occupancy, and activity patterns. Our results show that while model architecture has minimal impact, large amounts of noise and reduced dataset size significantly affect these metrics. Nonetheless, estimated ecological metrics are resilient to considerable noise, tolerating up to 10% error in species labels and a 50% reduction in training set size without changing significantly. We also highlight that conventional metrics like classification error may not always be representative of a model's ability to accurately measure ecological metrics. We conclude that ecological metrics derived from deep neural network predictions closely match those calculated from expert labels and remain robust to variations in the factors explored. However, training decisions for deep neural networks can impact downstream ecological analysis. Therefore, practitioners should prioritize creating large, clean training sets and evaluate deep neural network solutions based on their ability to measure the ecological metrics of interest.

This study presents a cross-verification of fast ion effects on turbulence through a systematic comparison of two leading gyrokinetic codes, GENE [T.Gorler et al., J. Comput. Phys. 230 7053-7071 (2011)] and CGYRO [J.Candy et al, J. Comput. Phys. 324 73-93 (2016)], using L-mode plasma profiles from KSTAR for local linear and nonlinear electromagnetic simulations. The focus is on the impact of fast ions and rotation effects on energy flux, aiming to identify the similarities and differences between these codes in the context of turbulence transport research. The analysis shows consistency in linear stability results, fractional changes in energy flux, and zonal shearing between the codes. However, discrepancies arise in absolute thermal energy levels, phase angle distribution, and rotation effects on energy transport, especially in the presence of fast ions. The study underscores the critical importance of phase angle analysis in gyrokinetic code verification, particularly when assessing fast ion effects on turbulence. Additionally, it highlights the need to examine quantities at lower levels of the primacy hierarchy, as discrepancies at higher levels can lead to divergent results at lower levels. These findings indicate the necessity for further investigation into these discrepancies and the novel phase angle structures observed, contributing to the advancement of accurate transport predictions in fusion plasmas.

Harsh working environments and work-related stress have been known to contribute to mental health problems such as anxiety, depression, and suicidal ideation. As such, it is paramount to create solutions that can both detect employee unhappiness and find the root cause of the problem. While prior works have examined causes of mental health using machine learning, they typically focus on general mental health analysis, with few of them focusing on explainable solutions or looking at the workplace-specific setting. r/antiwork is a subreddit for the antiwork movement, which is the desire to stop working altogether. Using this subreddit as a proxy for work environment dissatisfaction, we create a new dataset for antiwork sentiment detection and subsequently train a model that highlights the words with antiwork sentiments. Following this, we performed a qualitative and quantitative analysis to uncover some of the key insights into the mindset of individuals who identify with the antiwork movement and how their working environments influenced them. We find that working environments that do not give employees authority or responsibility, frustrating recruiting experiences, and unfair compensation, are some of the leading causes of the antiwork sentiment, resulting in a lack of self-confidence and motivation among their employees., Comment: 13 pages, 8 figures

Academic literature reviews have traditionally relied on techniques such as keyword searches and accumulation of relevant back-references, using databases like Google Scholar or IEEEXplore. However, both the precision and accuracy of these search techniques is limited by the presence or absence of specific keywords, making literature review akin to searching for needles in a haystack. We present vitaLITy 2, a solution that uses a Large Language Model or LLM-based approach to identify semantically relevant literature in a textual embedding space. We include a corpus of 66,692 papers from 1970-2023 which are searchable through text embeddings created by three language models. vitaLITy 2 contributes a novel Retrieval Augmented Generation (RAG) architecture and can be interacted with through an LLM with augmented prompts, including summarization of a collection of papers. vitaLITy 2 also provides a chat interface that allow users to perform complex queries without learning any new programming language. This also enables users to take advantage of the knowledge captured in the LLM from its enormous training corpus. Finally, we demonstrate the applicability of vitaLITy 2 through two usage scenarios. vitaLITy 2 is available as open-source software at https://vitality-vis.github.io., Comment: 7 pages, 4 figures, NLVIZ Workshop 2024 (IEEE VIS 2024)

We present the results of Sun-as-a-star observations by the NEID Solar Telescope at WIYN Observatory, spanning January 1, 2021 through June 30, 2024. We identify 117,060 observations which are unlikely to be significantly affected by weather, hardware or major calibration issues. We describe several high-level data products being made available to the community to aid in the interpretation and inter comparisons of NEID solar observations. Solar observations demonstrate excellent performance of NEID, including radial velocity (RV) accuracy and long-term stability of better than $\simeq 0.37$ m s$^{-1}$ over $\simeq 3.5$ years, even though NEID was not originally designed or optimized for daytime observations of the Sun. Currently, intrinsic stellar variability is the primary barrier to detecting Earth-analog planets for most nearby, Sun-like stars. We present a comparison of the effectiveness of several methods proposed to mitigate the effects of solar variability on the Sun's estimated RV. We find that the Scalpels algorithm performs particularly well and substantially reduces the RMS RV of solar spectra from over 2 m s$^{-1}$ to 0.277 m s$^{-1}$. Even when training on a subset of days with NEID solar observations and testing on a held-out sample, the RMS of cleaned RV is 0.34-0.42 m s$^{-1}$. This is significantly better than previous attempts at removing solar variability and suggests that the current generation of EPRV instruments are technically capable of detecting Earth-mass planets orbiting a solar twin if provided with sufficient observing time allocations ($\sim 10^3$ nights of observations)., Comment: 25 pages, 14 figures. Submitted to AAS Journals. Data release archived at https://zenodo.org/doi/10.5281/zenodo.13363761

Although exoplanetary science was not initially projected to be a substantial part of the Spitzer mission, its exoplanet observations set the stage for current and future surveys with JWST and Ariel. We present a comprehensive reduction and analysis of Spitzer's 4.5 micron phase curves of 29 hot Jupiters on low-eccentricity orbits. The analysis, performed with the Spitzer Phase Curve Analysis (SPCA) pipeline, confirms that BLISS mapping is the best detrending scheme for most, but not all, observations. Visual inspection remains necessary to ensure consistency across detrending methods due to the diversity of phase curve data and systematics. Regardless of the model selection scheme - whether using the lowest-BIC or a uniform detrending approach - we observe the same trends, or lack thereof. We explore phase curve trends as a function of irradiation temperature, orbital period, planetary radius, mass, and stellar effective temperature. We discuss the trends that are robustly detected and provide potential explanations for those that are not observed. While it is almost tautological that planets receiving greater instellation are hotter, we are still far from confirming dynamical theories of heat transport in hot Jupiter atmospheres due to the sample's diversity. Even among planets with similar temperatures, other factors like rotation and metallicity vary significantly. Larger, curated sample sizes and higher-fidelity phase curve measurements from JWST and Ariel are needed to firmly establish the parameters governing day-night heat transport on synchronously rotating planets., Comment: 16 pages, 6 figures, submitted to AAS journal

We measure the ionised gas velocity dispersions of star-forming galaxies in the MAGPI survey ($z\sim0.3$) and compare them with galaxies in the SAMI ($z\sim0.05$) and KROSS ($z\sim1$) surveys to investigate how the ionised gas velocity dispersion evolves. For the first time, we use a consistent method that forward models galaxy kinematics from $z=0$ to $z=1$. This method accounts for spatial substructure in emission line flux and beam smearing. We investigate the correlation between gas velocity dispersion and galaxy properties to understand the mechanisms that drive gas turbulence. We find that in both MAGPI and SAMI galaxies, the gas velocity dispersion more strongly correlates with the star-formation rate surface density ($\Sigma_{\rm SFR}$) than with a variety of other physical properties, and the average gas velocity dispersion is similar, at the same $\Sigma_{\rm SFR}$, for SAMI, MAGPI and KROSS galaxies. The results indicate that mechanisms related to $\Sigma_{\rm SFR}$ could be the dominant driver of gas turbulence from $z\sim1$ to $z\sim0$, for example, stellar feedback and/or gravitational instability. The gas velocity dispersion of MAGPI galaxies is also correlated with the non-rotational motion of the gas, illustrating that in addition to star-formation feedback, gas transportation and accretion may also contribute to the gas velocity dispersion for galaxies at $z\sim 0.3$. KROSS galaxies only have a moderate correlation between gas velocity dispersion and $\Sigma_{\rm SFR}$ and a higher scatter of gas velocity dispersion with respect to $\Sigma_{\rm SFR}$, in agreement with the suggestion that other mechanisms, such as gas transportation and accretion, are relatively more important at higher redshift galaxies., Comment: 15 pages, 10 figures, accepted for publication in MNRAS

Large language models (LLMs) are highly capable but face latency challenges in real-time applications, such as conducting online hallucination detection. To overcome this issue, we propose a novel framework that leverages a small language model (SLM) classifier for initial detection, followed by a LLM as constrained reasoner to generate detailed explanations for detected hallucinated content. This study optimizes the real-time interpretable hallucination detection by introducing effective prompting techniques that align LLM-generated explanations with SLM decisions. Empirical experiment results demonstrate its effectiveness, thereby enhancing the overall user experience., Comment: preprint under review

Google Vizier has performed millions of optimizations and accelerated numerous research and production systems at Google, demonstrating the success of Bayesian optimization as a large-scale service. Over multiple years, its algorithm has been improved considerably, through the collective experiences of numerous research efforts and user feedback. In this technical report, we discuss the implementation details and design choices of the current default algorithm provided by Open Source Vizier. Our experiments on standardized benchmarks reveal its robustness and versatility against well-established industry baselines on multiple practical modes., Comment: Google DeepMind Technical Report. Code can be found in https://github.com/google/vizier