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We study changes to teacher working conditions from 2016-17 to 2022-23, covering school years before, during, and after the COVID pandemic. We show working conditions were improving leading into the pandemic but declined when the pandemic arrived. Perhaps more surprisingly, the pandemic was not a low point: teacher working conditions have continued to decline during the post-pandemic period. Teachers report worsening working conditions along many dimensions including the level of classroom disruptions, student responsibility, and safety, among others. They also report declines in trust between themselves and principals, parents, and other teachers. Trends in working conditions since the pandemic are similar in schools serving more and less socioeconomically advantaged students. However, schools in districts where online learning was the predominant mode of instruction during the 2020-21 school year have experienced larger declines than other schools.

Most selective colleges implemented test-optional admissions during the pandemic, making college entrance exam scores optional for applicants. We draw on descriptive, two-way fixed effects, and event study methods to examine variation in test-optional implementation during the pandemic and how implementation relates to selectivity and enrollment. For "test-optional" colleges during the pandemic, we found substantial variation in policy type (e.g., test optional, test free) and whether the policy extended to all applicants and scholarship consideration. Findings suggest test-optional implementation related to increases in Black student enrollment, mostly at moderately selective colleges and when policies extended to all applicants and scholarships. At highly selective colleges, findings suggest test-optional implementation related to an increase in applications but not consistent gains in enrollment.

As states and districts seek to elevate learning and prepare more students for work in the digital future, they have not only a unique opportunity, but also an urgent imperative, to redesign high school math. This report examines an array of education policies that exert considerable influence on the math students learn and when they learn it, factors that have a profound influence on students' futures. It shares up-to-date information about how some states are navigating these policies as they develop innovative redesigns of high school mathematics and about strategies aimed at improving student outcomes, particularly for historically underrepresented students. A survey of state math supervisors highlights the priorities driving these changes. To illustrate various redesign strategies, the report features examples from five states: Georgia, Ohio, Oregon, Utah, and Washington. Drawing from those strategies, the research evidence, and the survey of state math supervisors, it provides insights and recommendations for states and districts to consider as they update their high school math policies.

OpenSciEd is a set of Creative Commons licensed, standards aligned curriculum and teacher professional learning materials that will be available for grades K-12. Using a storyline model that gives students the responsibility of "figuring out" science phenomena by engaging in science practices and classroom discussion to achieve consensus, OpenSciEd "empowers educators to go beyond traditional science teaching methods." This instructional model is ambitious for teachers, who must serve as facilitators and continually adapt instruction in response to directions students take. Because of this instructional shift, districts must also shift in how they adopt and implement science curriculum. Among the aims of the OpenSciEd Research Community, led by Digital Promise with support from the Carnegie Corporation of New York, is to develop and disseminate resources that support advancement of OpenSciEd-enabled research. Toward this goal, Digital Promise has released this report that synthesizes perspectives from OpenSciEd practitioners on their needs. The report identifies: (1) supports that teachers need to enact OpenSciEd with integrity, engage all their students, and gather evidence of students' standards-based learning outcomes; (2) ways districts can achieve deep, sustained adoption and meet their teachers' needs; and (3) potential research questions and opportunities for innovation that can improve OpenSciEd implementation in districts and classrooms. [This report was produced in collaboration with OpenSciEd.]

Letters of recommendation from school counselors are required to apply to many selective colleges and universities. Still, relatively little is known about how this non-standardized component may affect equity in admissions. We use cutting-edge natural language processing techniques to algorithmically analyze a national dataset of over 600,000 student applications and counselor recommendation letters submitted via the Common App platform. We examine how the length and topical content of letters (e.g., sentences about Personal Qualities, Athletics, Intellectual Promise, etc.) relate to student self-identified race/ethnicity, sex, and proxies for socioeconomic status. Paired with regression analyses, we explore whether demographic differences in letter characteristics persist when accounting for additional student, school, and counselor characteristics, as well as among letters written by the same counselor and among students with comparably competitive standardized test scores. We ultimately find large and noteworthy naïve differences in letter length and content across nearly all demographic groups, many in alignment with known inequities (e.g., many more sentences about Athletics among White and higher-SES students, longer letters and more sentences on Personal Qualities for private school students). However, these differences vary drastically based on the exact controls and comparison groups included -- demonstrating that the ultimate implications of these letter differences for equity hinges on exactly how and when letters are used in admissions processes (e.g., among which groups of students are they used to "break ties"?). Findings do not point to a clear recommendation whether institutions should keep or discard letter requirements, but reflect the importance of reading letters and overall applications in the context of structural opportunity. We discuss additional implications and possible recommendations for college access and admissions policy/practice.

The $\sim$5 Myr PDS 70 is the only known system with protoplanets residing in the cavity of the circumstellar disk from which they formed, ideal for studying exoplanet formation and evolution within its natal environment. Here we report the first spin constraint and C/O measurement of PDS 70b from Keck/KPIC high-resolution spectroscopy. We detected CO (3.8 $\sigma$) and H$_2$O (3.5 $\sigma$) molecules in the PDS 70b atmosphere via cross-correlation, with a combined CO and H$_2$O template detection significance of 4.2 $\sigma$. Our forward model fits, using BT-Settl model grids, provide an upper limit for the spin-rate of PDS 70b ($<$29 km s$^{-1}$). The atmospheric retrievals constrain the PDS 70b C/O ratio to ${0.28}^{+0.20}_{-0.12}$ ($<$0.63 under 95$\%$ confidence level) and a metallicity [C/H] of ${-0.2}^{+0.8}_{-0.5}$ dex, consistent with that of its host star. The following scenarios can explain our measured C/O of PDS 70b in contrast with that of the gas-rich outer disk (for which C/O $\gtrsim$ 1). First, the bulk composition of PDS 70b might be dominated by dust+ice aggregates rather than disk gas. Another possible explanation is that the disk became carbon-enriched $\textit{after}$ PDS 70b was formed, as predicted in models of disk chemical evolution and as observed in both very low mass star and older disk systems with $\textit{JWST}$/MIRI. Because PDS 70b continues to accrete and its chemical evolution is not yet complete, more sophisticated modeling of the planet and the disk, and higher quality observations of PDS 70b (and possibly PDS 70c), are necessary to validate these scenarios., Comment: Accepted to ApJ Letters; 15 pages, 3 figures

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data., Comment: 21 pages, 11 figures, 2 tables

We analyze 99 photometrically selected Little Red Dots (LRDs) at z ~ 4-8 in the GOODS fields, leveraging ultra-deep JADES NIRCam short-wavelength (SW) data. We examine the morphology of 30 LRDs; the remaining 69 are predominantly compact, i.e. are strongly dominated by sources < 400 pc in diameter and lack extended components even in stacked SW band images. Among the LRDs selected for morphological analysis, 50% show at least two distinct, associated sources or galaxy components, while the others appear as single sources with highly asymmetric structures. We find median stellar masses of log10(M*/Msun) = 9.07(-0.08)(+0.11) for pure stellar models with Av ~ 1.16(+0.11)(-0.21) mag, and log10(M*/Msun) = 9.67(+0.17)(-0.27) for models including AGNs, where Av ~ 2.74(+0.55)(-0.71) mag, consistent with recent results showing LRDs tend to have high stellar masses and dust content when fitted with AGN models. NIRSpec spectra are available for 15 sources, 6 of which fall within the morphological analysis sample and show multiple components. Among these 15, broad H-alpha emission is detected in 40%, with full-width half-maximum (FWHM) ranging from 1200 to 2900 km/s. One source exhibits broad H-beta emission with FWHM = 2000 +/- 500 km/s. Analysis of line ratios probing the interstellar medium (ISM) reveals a composite nature, indicating AGN activity combined with stellar processes. These findings suggest LRDs have a mixed nature, with AGN signatures in some cases linked to disturbed morphologies observed at rest-frame UV wavelengths., Comment: 28 pages, 9 figures, 3 tables. Submitted to ApJ. Comments are welcome!

Pulsar timing array observations have found evidence for an isotropic gravitational wave background with the Hellings-Downs angular correlations, expected from general relativity. This interpretation hinges on the measured shape of the angular correlations, which is predominately quadrupolar under general relativity. Here we explore a more flexible parameterization: we expand the angular correlations into a sum of Legendre polynomials and use a Bayesian analysis to constrain their coefficients with the 15-year pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). When including Legendre polynomials with multipoles $\ell \geq 2$, we only find a significant signal in the quadrupole with an amplitude consistent with general relativity and non-zero at the $\sim 95\%$ confidence level and a Bayes factor of 200. When we include multipoles $\ell \leq 1$, the Bayes factor evidence for quadrupole correlations decreases by more than an order of magnitude due to evidence for a monopolar signal at approximately 4 nHz which has also been noted in previous analyses of the NANOGrav 15-year data. Further work needs to be done in order to better characterize the properties of this monopolar signal and its effect on the evidence for quadrupolar angular correlations., Comment: 15 pages, 6 figures

The Laser Interferometer Space Antenna (LISA) mission is being developed by ESA with NASA participation. As it has recently passed the Mission Adoption milestone, models of the instruments and noise performance are becoming more detailed, and likewise prototype data analyses must as well. Assumptions such as Gaussianity, Stationarity, and continuous data continuity are unrealistic, and must be replaced with physically motivated data simulations, and data analysis methods adapted to accommodate such likely imperfections. To this end, the LISA Data Challenges have produced datasets featuring time-varying and unequal constellation armlength, and measurement artifacts including data interruptions and instrumental transients. In this work, we assess the impact of these data artifacts on the inference of Galactic Binary and Massive Black Hole properties. Our analysis shows that the treatment of noise transients and gaps is necessary for effective parameter estimation. We find that straightforward mitigation techniques can significantly suppress artifacts, albeit leaving a non-negligible impact on aspects of the science., Comment: 28 pages, 14 figures

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surroundings. SCOUT contains 89,056 utterances and 310,095 words from 278 dialogues averaging 320 utterances per dialogue. The dialogues are aligned with the multi-modal data streams available during the experiments: 5,785 images and 30 maps. The corpus has been annotated with Abstract Meaning Representation and Dialogue-AMR to identify the speaker's intent and meaning within an utterance, and with Transactional Units and Relations to track relationships between utterances to reveal patterns of the Dialogue Structure. We describe how the corpus and its annotations have been used to develop autonomous human-robot systems and enable research in open questions of how humans speak to robots. We release this corpus to accelerate progress in autonomous, situated, human-robot dialogue, especially in the context of navigation tasks where details about the environment need to be discovered., Comment: 14 pages, 7 figures

In this paper, we describe the development of symbolic representations annotated on human-robot dialogue data to make dimensions of meaning accessible to autonomous systems participating in collaborative, natural language dialogue, and to enable common ground with human partners. A particular challenge for establishing common ground arises in remote dialogue (occurring in disaster relief or search-and-rescue tasks), where a human and robot are engaged in a joint navigation and exploration task of an unfamiliar environment, but where the robot cannot immediately share high quality visual information due to limited communication constraints. Engaging in a dialogue provides an effective way to communicate, while on-demand or lower-quality visual information can be supplemented for establishing common ground. Within this paradigm, we capture propositional semantics and the illocutionary force of a single utterance within the dialogue through our Dialogue-AMR annotation, an augmentation of Abstract Meaning Representation. We then capture patterns in how different utterances within and across speaker floors relate to one another in our development of a multi-floor Dialogue Structure annotation schema. Finally, we begin to annotate and analyze the ways in which the visual modalities provide contextual information to the dialogue for overcoming disparities in the collaborators' understanding of the environment. We conclude by discussing the use-cases, architectures, and systems we have implemented from our annotations that enable physical robots to autonomously engage with humans in bi-directional dialogue and navigation., Comment: 52 pages, 14 figures

Random walks and related spatial stochastic models have been used in a range of application areas including animal and plant ecology, infectious disease epidemiology, developmental biology, wound healing, and oncology. Classical random walk models assume that all individuals in a population behave independently, ignoring local physical and biological interactions. This assumption simplifies the mathematical description of the population considerably, enabling continuum-limit descriptions to be derived and used in model analysis and fitting. However, interactions between individuals can have a crucial impact on population-level behaviour. In recent decades, research has increasingly been directed towards models that include interactions, including physical crowding effects and local biological processes such as adhesion, competition, dispersal, predation and adaptive directional bias. In this article, we review the progress that has been made with models of interacting individuals. We aim to provide an overview that is accessible to researchers in application areas, as well as to specialist modellers. We focus particularly on derivation of asymptotically exact or approximate continuum-limit descriptions and simplified deterministic models of mean-field behaviour and resulting spatial patterns. We provide worked examples and illustrative results of selected models. We conclude with a discussion of current areas of focus and future challenges.

Parameter inference is essential when interpreting observational data using mathematical models. Standard inference methods for differential equation models typically rely on obtaining repeated numerical solutions of the differential equation(s). Recent results have explored how numerical truncation error can have major, detrimental, and sometimes hidden impacts on likelihood-based inference by introducing false local maxima into the log-likelihood function. We present a straightforward approach for inference that eliminates the need for solving the underlying differential equations, thereby completely avoiding the impact of truncation error. Open-access Jupyter notebooks, available on GitHub, allow others to implement this method for a broad class of widely-used models to interpret biological data., Comment: 11 pages, 2 figures

Spectroscopy of Earth-like exoplanets and ultra-faint galaxies are priority science cases for the coming decades. Here, broadband source flux rates are measured in photons per square meter per hour, imposing extreme demands on detector performance, including dark currents lower than \mbox{1 e-/pixel/kilosecond}, read noise less than \mbox{1 e-/pixel/frame}, and large formats. There are currently no infrared detectors that meet these requirements. The University of Hawai'i and industrial partners are developing one promising technology, linear mode avalanche photodiodes (LmAPDs), which is on track to meet the above-mentioned requirements. We present progress towards developing a science-grade, megapixel format linear-mode avalanche photodiode array for low background shortwave (1 - 2.4 um) infrared astronomy. Our latest results show outstanding performance, with dark current \textless 1e-4 electrons/pixel/second and read noise reducing by 30\% per volt of bias, reaching less than 1e-/pixel/frame in correlated double-sampling, and able to average down to $\sim$0.3 e-/pixel/frame when using multiple non-destructive reads. We present some on-sky data as well as comment on prospects for photon number resolving capability., Comment: SPIE Astronomical Telescopes + Instrumentation 2024, PROC#13103-25. arXiv admin note: text overlap with arXiv:2208.11834

A wealth of studies report evidence that occipitotemporal cortex tessellates into "category-selective" brain regions that are apparently specialized for representing ecologically important visual stimuli like faces, bodies, scenes, and tools. Here, we argue that while valuable insights have been gained through the lens of category-selectivity, a more complete view of visual function in occipitotemporal cortex requires centering the behavioral relevance of visual properties in real-world environments rather than stimulus category. Focusing on behavioral relevance challenges a simple mapping between stimulus and visual function in occipitotemporal cortex because the environmental properties relevant to a behavior are visually diverse and how a given property is represented is modulated by our goals. Grounding our thinking in behavioral relevance rather than category-selectivity raises a host of theoretical and empirical issues that we discuss while providing proposals for how existing tools can be harnessed in this light to better understand visual function in occipitotemporal cortex.

To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge this gap, we develop ChemAgent, an enhanced chemistry agent over ChemCrow, and conduct a comprehensive evaluation of its performance on both specialized chemistry tasks and general chemistry questions. Surprisingly, ChemAgent does not consistently outperform its base LLMs without tools. Our error analysis with a chemistry expert suggests that: For specialized chemistry tasks, such as synthesis prediction, we should augment agents with specialized tools; however, for general chemistry questions like those in exams, agents' ability to reason correctly with chemistry knowledge matters more, and tool augmentation does not always help.

The detection of a stochastic gravitational wave background by pulsar timing arrays suggests the presence of a supermassive black hole binary population. Although the observed spectrum generally aligns with predictions from orbital evolution driven by gravitational wave emission in circular orbits, there is a discernible preference for a turnover at the lowest observed frequencies. This turnover could indicate a significant hardening phase, transitioning from early environmental influences to later stages predominantly influenced by gravitational wave emission. In the vicinity of these binaries, the ejection of stars or dark matter particles through gravitational three-body slingshots efficiently extracts orbital energy, leading to a low-frequency turnover in the spectrum. By analyzing the NANOGrav 15-year data, we assess how the gravitational wave spectrum depends on the initial inner galactic profile prior to disruption by binary ejections, accounting for a range of initial binary eccentricities. Our findings suggest a parsec-scale galactic center density around $10^6\,M_\odot/\textrm{pc}^3$ across most of the parameter space, offering insights into the environmental effects on black hole evolution and combined matter density near galaxy centers., Comment: 15 pages, 5 figures

The advent of high-intensity, high-polarization electron beams led to significantly improved measurements of the ratio of the proton's charge to electric form factors, GEp/GMp. However, high-$Q^2$ measurements yielded significant disagreement with extractions based on unpolarized scattering, raising questions about the reliability of the measurements and consistency of the techniques. Jefferson Lab experiment E01-001 was designed to provide a high-precision extraction of GEp/GMp from unpolarized cross section measurements using a modified version of the Rosenbluth technique to allow for a more precise comparison with polarization data. Conventional Rosenbluth separations detect the scattered electron which requires comparisons of measurements with very different detected electron energy and rate for electrons at different angles. Our Super-Rosenbluth measurement detected the struck proton, rather than the scattered electron, to extract the cross section. This yielded a fixed momentum for the detected particle and dramatically reduced cross section variation, reducing rate- and momentum-dependent corrections and uncertainties. We measure the cross section vs angle with high relative precision, allowing for extremely precise extractions of GEp/GMp at $Q^2$ = 2.64, 3.20, and 4.10 GeV$^2$. Our results are consistent with traditional extractions but with much smaller corrections and systematic uncertainties, comparable to the uncertainties from polarization measurements. Our data confirm the discrepancy between Rosenbluth and polarization extractions of the proton form factor ratio using an improved Rosenbluth extraction that yields smaller and less-correlated uncertainties than typical of previous Rosenbluth extractions. We compare our results to calculations of two-photon exchange effects and find that the observed discrepancy can be relatively well explained by such effects., Comment: 32 pages, 21 figures. arXiv admin note: text overlap with arXiv:nucl-ex/0610006

We construct genus one knots whose handle number is only realized by Seifert surfaces of non-minimal genus. These are counterexamples to the conjecture that the Seifert genus of a knot is its Morse-Novikov genus. As the Morse-Novikov genus may be greater than the Seifert genus, we define the genus $g$ Morse-Novikov number $MN_g(L)$ as the minimum handle number among Seifert surfaces for $L$ of genus $g$. Since, as we further show, the Morse-Novikov genus and the minimal genus Morse-Novikov number are additive under connected sum of knots, it then follows that there exists examples for which the discrepancies between Seifert genus and Morse-Novikov genus and between the Morse-Novikov number and the minimal genus Morse-Novikov number can be made arbitrarily large., Comment: 8 pages, 5 figures

Kagome superconductors AV$_{3}$Sb$_{5}$ provide a unique platform for studying the interplay between a variety of electronic orders, including superconductivity, charge density waves, nematic phases and more. Understanding the evolution of the electronic state from the charge density wave to the superconducting transition is essential for unraveling the interplay of charge, spin, and lattice degrees of freedom giving rise to the unusual magnetic properties of these nonmagnetic metals. Previous zero-field and high-field $\mu$SR studies revealed two anomalies in the muon spin relaxation rate, a first change at $T_{CDW} \sim 100$ K and a second steep increase at $T^{*}\sim 40$ K, further enhanced by an applied magnetic field, thus suggesting a contribution of magnetic origin. In this study, we use the avoided level crossing $\mu$SR technique to investigate charge order in near-zero applied field. By tracking the temperature dependence of quadrupolar level-crossing resonances, we examined the evolution of the electric field gradient at V nuclei in the kagome plane. Our results show a significant rearrangement of the charge density starting at $T^{*}$ indicating a transition in the charge distribution, likely electronic in origin, well below $T_{CDW}$. These findings, combined with previous $\mu$SR, STM, and NMR studies, emphasize the intertwined nature of proximate phases in these systems, with the charge rearrangement dominating the additional increase in $\mu$SR relaxation rate below $T^{*}$.

A characteristic signature of quark confinement is the concentration of the chromoelectric field between a static quark-antiquark pair in a flux tube. Here we report on lattice measurements of field distributions on smeared Monte Carlo ensembles in QCD with (2+1) HISQ flavors. We measure the field distributions for several distances between static quark-antiquark sources, ranging from 0.6 fm up to the distance where the color string is expected to break., Comment: 9 pages, 6 figures, talk presented at the 41st International Symposium on Lattice Field Theory (LATTICE2024), July 28th - August 3rd, 2024, The University of Liverpool

Accurate and robust recording and decoding from the central nervous system (CNS) is essential for advances in human-machine interfacing. However, technologies used to directly measure CNS activity are limited by their resolution, sensitivity to interferences, and invasiveness. Advances in muscle recordings and deep learning allow us to decode the spiking activity of spinal motor neurons (MNs) in real time and with high accuracy. MNs represent the motor output layer of the CNS, receiving and sampling signals originating in different regions in the nervous system, and generating the neural commands that control muscles. The input signals to MNs can be estimated from the MN outputs. Here we argue that peripheral neural interfaces using muscle sensors represent a promising, non-invasive approach to estimate some neural activity from the CNS that reaches the MNs but does not directly modulate force production. We also discuss the evidence supporting this concept, and the necessary advances to consolidate and test MN-based CNS interfaces in controlled and real-world settings.

Computational physics simulation can be a powerful tool to accelerate industry deployment of new scientific technologies. However, it must address the challenge of computationally tractable, moderately accurate prediction at large industry scales, and training a model without data at such large scales. A recently proposed component reduced order modeling (CROM) tackles this challenge by combining reduced order modeling (ROM) with discontinuous Galerkin domain decomposition (DG-DD). While it can build a component ROM at small scales that can be assembled into a large scale system, its application is limited to linear physics equations. In this work, we extend CROM to nonlinear steady Navier-Stokes flow equation. Nonlinear advection term is evaluated via tensorial approach or empirical quadrature procedure. Application to flow past an array of objects at moderate Reynolds number demonstrates $\sim23.7$ times faster solutions with a relative error of $\sim 2.3\%$, even at scales $256$ times larger than the original problem., Comment: 6 pages, 1 figure

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, component reduced order modeling method is developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has been only demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier-Stokes flow, with application to general nonlinear physics in view. Large-scale, global domain is decomposed into combination of small-scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected at small scale unit component. Velocity bases are augmented with pressure supremizer, in order to satisfy inf-sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: 3rd-order tensor projection operator and empirical quadrature procedure. The proposed method is demonstrated on flow over arrays of five different unit objects, achieving $23$ times faster prediction with less than $4\%$ relative error up to $256$ times larger scale domain than unit components. Furthermore, a numerical experiment with pressure supremizer strongly indicates the need of supremizer for stable pressure prediction. A comparison between tensorial approach and empirical quadrature procedure is performed, which suggests a slight advantage for empirical quadrature procedure., Comment: 34 pages, 7 figures

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Demand response is a concept that has been around since the very first electric power systems. However, we have seen an explosion of research on demand response and demand-side technologies in the past 30 years, coinciding with the shift towards liberalized/deregulated electricity markets and efforts to decarbonize the power sector. Now we are also seeing a shift towards more distributed/decentralized electric systems; we have entered the era of "distributed energy resources," which require new grid management, operational, and control strategies. Given this paradigm shift, we argue that the concept of demand response needs to be revisited, and more carefully/consistently defined to enable us to better utilize this massive resource for economic, technical, environmental, and societal aims. In this paper, we survey existing demand response definitions, highlight their shortcomings, propose a new definition, and describe how this new definition enables us to more effectively harness the value of demand response in modern power systems. We conclude with a demand response research agenda informed by a discussion of demand response barriers and enablers., Comment: 12 pages

Recent developments in applying machine learning to address Alternating Current Optimal Power Flow (AC OPF) problems have demonstrated significant potential in providing close to optimal solutions for generator dispatch in near real-time. While these learning to optimize methods have demonstrated remarkable performance on steady-state operations, practical applications often demand compliance with dynamic constraints when used for fast-timescale optimization. This paper addresses this gap and develops a real-time stability-constrained OPF model (DynOPF-Net) that simultaneously addresses both optimality and dynamical stability within learning-assisted grid operations. The model is a unique integration of learning to optimize that learns a mapping from load conditions to OPF solutions, capturing the OPF's physical and engineering constraints, with Neural Ordinary Differential Equations, capturing generator dynamics, enabling the inclusion of a subset of stability constraints. Numerical results on the WSCC 9-bus and IEEE 57-bus benchmark systems demonstrate that DynOPF-Net can produce highly accurate AC-OPF solutions while also ensuring system stability, contrasting the unstable results obtained by state-of-the-art LtO methods.

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of $4.2\pm0.1$ tonne-years from 280 live days of LZ operation, of which $3.3\pm0.1$ tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from $^{214}$Pb $\beta$ decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of $^{124}$Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the data set to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses $\geq$9 GeV/$c^2$. The strongest SI exclusion set is $2.1\times10^{-48}$ cm$^{2}$ at the 90% confidence level at a mass of 36 GeV/$c^2$, and the best SI median sensitivity achieved is $5.0\times10^{-48}$ cm$^{2}$ for a mass of 40 GeV/$c^2$., Comment: 9 pages, 7 figures. See https://www.hepdata.net/record/155182 for a data release related to this paper

Plasma composition measurements are a vital tool for the success of current and future solar missions, but density and temperature insensitive spectroscopic diagnostic ratios are sparse, and their underlying accuracy in determining the magnitude of the First Ionization Potential (FIP) effect in the solar atmosphere remains an open question. Here we assess the Fe VIII 185.213A/Ne VIII 770.428A intensity ratio that can be observed as a multi-spacecraft combination between Solar Orbiter/SPICE and Hinode/EIS. We find that it is fairly insensitive to temperature and density in the range of log (T/K) = 5.65-6.05 and is therefore useful, in principle, for analyzing on-orbit EUV spectra. We also perform an empirical experiment, using Hinode/EIS measurements of coronal fan loop temperature distributions weighted by randomnly generated FIP bias values, to show that our diagnostic method can provide accurate results as it recovers the input FIP bias to within 10--14%. This is encouraging since it is smaller than the magnitude of variations seen throughout the solar corona. We apply the diagnostic to coordinated observations from 2023 March, and show that the combination of SPICE and EIS allows measurements of the Fe/Ne FIP bias in the regions where the footpoints of the magnetic field connected to Solar Orbiter are predicted to be located. The results show an increase in FIP bias between the main leading polarity and the trailing decayed polarity that broadly agrees with Fe/O in-situ measurements from Solar Orbiter/SWA. Multi-spacecraft coordinated observations are complex, but this diagnostic also falls within the planned wavebands for Solar-C/EUVST., Comment: To be published in The Astrophysical Journal

We use NIRSpec/MSA spectroscopy and NIRCam imaging to study a sample of 18 massive ($\log\; M_{*}/M_{\odot} \gt 10\;$dex), central quiescent galaxies at $2\leq z \leq 5$ in the GOODS fields, to investigate their number density, star-formation histories, quenching timescales, and incidence of AGN. The depth of our data reaches $\log M_*/M_\odot \approx 9\;$dex, yet the least-massive central quiescent galaxy found has $\log M_*/M_\odot \gt 10\;$dex, suggesting that quenching is regulated by a physical quantity that scales with $M_*$. With spectroscopy as benchmark, we assess the completeness and purity of photometric samples, finding number densities 10 times higher than predicted by galaxy formation models, confirming earlier photometric studies. We compare our number densities to predictions from FLAMINGO, the largest-box full-hydro simulation suite to date. We rule out cosmic variance at the 3-$\sigma$ level, providing spectroscopic confirmation that galaxy formation models do not match observations at $z>3$. Using FLAMINGO, we find that the vast majority of quiescent galaxies' stars formed in situ, with these galaxies not having undergone multiple major dry mergers. This is in agreement with the compact observed size of these systems and suggests that major mergers are not a viable channel for quenching most massive galaxies. Several of our observed galaxies are particularly old, with four galaxies displaying 4000-\r{A} breaks; full-spectrum fitting infers formation and quenching redshifts of $z\geq8$ and $z\geq6$. Using all available AGN tracers, we find that 8 massive quiescent galaxies host AGN, including in old systems. This suggests a high duty cycle of AGN and a continued trickle of gas to fuel accretion., Comment: 32 pages, 20 figures, submitted to MNRAS

The pioneering work of Oono and Suzuki [ICLR, 2020] and Cai and Wang [arXiv:2006.13318] initializes the analysis of the smoothness of graph convolutional network (GCN) features. Their results reveal an intricate empirical correlation between node classification accuracy and the ratio of smooth to non-smooth feature components. However, the optimal ratio that favors node classification is unknown, and the non-smooth features of deep GCN with ReLU or leaky ReLU activation function diminish. In this paper, we propose a new strategy to let GCN learn node features with a desired smoothness -- adapting to data and tasks -- to enhance node classification. Our approach has three key steps: (1) We establish a geometric relationship between the input and output of ReLU or leaky ReLU. (2) Building on our geometric insights, we augment the message-passing process of graph convolutional layers (GCLs) with a learnable term to modulate the smoothness of node features with computational efficiency. (3) We investigate the achievable ratio between smooth and non-smooth feature components for GCNs with the augmented message-passing scheme. Our extensive numerical results show that the augmented message-passing schemes significantly improve node classification for GCN and some related models., Comment: 48 pages

The advancements of language language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked both excitement and skepticism about their true capabilities. In this work, we call for rigorous assessment of agents on individual tasks in a scientific workflow before making bold claims on end-to-end automation. To ensure the scientific authenticity and real-world relevance of our benchmark, we extract 102 tasks from 44 peer-reviewed publications in four disciplines and engage nine subject matter experts to validate them. We unify the target output for every task to a self-contained Python program file and employ an array of evaluation metrics to examine the generated programs, execution results, and costs. Each task goes through multiple rounds of manual validation by annotators and subject matter experts to ensure its annotation quality and scientific plausibility. We also propose two effective strategies to mitigate data contamination concerns. Using our benchmark, we evaluate five open-weight and proprietary LLMs, each with three frameworks: direct prompting, OpenHands CodeAct, and self-debug. Given three attempts for each task, the best-performing agent can only solve 32.4% of the tasks independently and 34.3% with expert-provided knowledge. In addition, we evaluate OpenAI o1 with direct prompting and self-debug, which demonstrates the effectiveness of increasing inference-time compute. Still, our results underscore the limitations of current language agents in generating code for data-driven discovery, let alone end-to-end automation for scientific research., Comment: 57 pages

Replacing fossil-fueled appliances and vehicles with electric alternatives can reduce greenhouse gas emissions and air pollution in many settings. However, residential electrification can raise electricity demand beyond the safe limits of electrical infrastructure, increasing the risk of blackouts or requiring grid reinforcement that can be slow and expensive. Here, we estimate the physical and economic impacts on distribution grids of electrifying all housing and personal vehicles in each county of the lower 48 United States. We find that space heating is the main driver of grid impacts, with the coldest regions seeing demand peaks up to three times higher than today's peaks. Accommodating electrification of all housing and personal vehicles could require up to 312 GW of distribution grid reinforcement nationally, at a cost of $183 to $415 billion, or $1,500 to $3,400 per household (95% confidence intervals). However, demand-side management can mitigate demand peaks, reducing grid reinforcement costs by up to 92%.

Gravitational wave dark sirens are a powerful tool for cosmology and inference of compact object population hyperparameters. They allow for a measurement of the luminosity distance to the source, but not their redshift. Galaxy catalogues in the source localization volume can be used to infer the redshift of the source in a statistical manner. Catalogues are, however, limited by their incompleteness, which can be significant at redshifts corresponding to current GW events. In this work, we detail how to implement in practice variance completion, a novel galaxy completion method which uses knowledge of the large scale structure to optimize the potential of dark sirens analyses. We compress the prediction for the missing number of galaxies into a ratio between the predictions of variance completion and the standard homogeneous completion method. This ratio format can be easily incorporated into existing line of sight computations used in dark sirens software; we demonstrate this procedure using the GLADE+ galaxy catalogue and the gwcosmo software package. We discuss the robustness of the method, and apply it to well-localized event GW190814 as a proof of concept. Finally, we apply the method to data from the third observing run of LIGO-Virgo-KAGRA, finding that it yields results that are consistent with homogeneous completion. We also discuss the prospects for an improvement if the GW localization volume shrinks., Comment: 27 pages, 12 figures

A key initial step in mechanistic modelling of dynamical systems using first-order ordinary differential equations is to conduct a global structural identifiability analysis. This entails deducing which parameter combinations can be estimated from certain observed outputs. The standard differential algebra approach answers this question by re-writing the model as a system of ordinary differential equations solely depending on the observed outputs. Over the last decades, alternative approaches for analysing global structural identifiability based on so-called full symmetries, which are Lie symmetries acting on independent and dependent variables as well as parameters, have been proposed. However, the link between the standard differential algebra approach and that using full symmetries remains elusive. In this work, we establish this link by introducing the notion of parameter symmetries, which are a special type of full symmetry that alter parameters while preserving the observed outputs. Our main result states that a parameter combination is structurally identifiable if and only if it is a differential invariant of all parameter symmetries of a given model. We show that the standard differential algebra approach is consistent with the concept of considering structural identifiability in terms of parameter symmetries. We present an alternative symmetry-based approach, referred to as the CaLinInv-recipe, for analysing structural identifiability using parameter symmetries. Lastly, we demonstrate our approach on a glucose-insulin model and an epidemiological model of tuberculosis., Comment: 36 pages, 2 figures

Deployment of practical quantum networks, which operate at or near single photon levels, requires carefully quantifying noise processes. We investigate noise due to blackbody radiation emitted into the guided mode of silica single mode optical fibres near room temperature, which to date is under-explored in the literature. We utilise a single photon avalanche detector and lock in detection to measure $\approx$0.1 photons/s/THz ($\approx$-170dBm/THz) at 40{\deg}C near the optically thick limit of 20km in silica fibre. We also measure a coarse spectrum to validate the blackbody behaviour, and observe a prominent anomaly around the 1430nm CWDM channel, likely due to -OH impurities. Though the magnitude of this noise is small, it is additive noise which imposes a fundamental limit in raw fidelity in quantum communication, and a fundamental noise floor in classical communication over optical fibres., Comment: Submitted to Journal of Lightwave Technology in March 2024

Teaching text-to-image models to be creative involves using style ambiguity loss. In this work, we explore using the style ambiguity training objective, used to approximate creativity, on a diffusion model. We then experiment with forms of style ambiguity loss that do not require training a classifier or a labeled dataset, and find that the models trained with style ambiguity loss can generate better images than the baseline diffusion models and GANs. Code is available at https://github.com/jamesBaker361/clipcreate., Comment: arXiv admin note: substantial text overlap with arXiv:2407.12009

Differential equations (DE) constrained optimization plays a critical role in numerous scientific and engineering fields, including energy systems, aerospace engineering, ecology, and finance, where optimal configurations or control strategies must be determined for systems governed by ordinary or stochastic differential equations. Despite its significance, the computational challenges associated with these problems have limited their practical use. To address these limitations, this paper introduces a learning-based approach to DE-constrained optimization that combines techniques from proxy optimization and neural differential equations. The proposed approach uses a dual-network architecture, with one approximating the control strategies, focusing on steady-state constraints, and another solving the associated DEs. This combination enables the approximation of optimal strategies while accounting for dynamic constraints in near real-time. Experiments across problems in energy optimization and finance modeling show that this method provides full compliance with dynamic constraints and it produces results up to 25 times more precise than other methods which do not explicitly model the system's dynamic equations.