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The Career and Occupational Development (COD) assessment of the National Assessment of Educational Progress (NAEP) was made up of about 70 percent free response exercises requiring hand scoring. This paper describes the techniques used in developing the "scoring guides" for these exercises and summarizes the results of two empirical studies of the application of these scoring guides. The guides used in the hand scoring were sets of nominal (descriptive) category systems. No attempt was made to arrange the categories along any ordinal continuum according to either quality or content. However, categories were considered to be either acceptable or unacceptable. The readers were given a scoring guide in which each category is given a descriptive title and illustrated by a number of sample responses. (RC) Primary type of information provided by report: Procedures (Scoring).

In August 2017, Hurricane Harvey ravaged the Houston area, causing the homelessness of nearly 24,000 students in the Houston Independent School District (Houston ISD) alone. Additionally, nearly all Houston ISD schools sustained damage of some kind, resulting in school closures, campus relocations, and even the delaying of the start of classes for some students. In the first brief of this two-part series, the authors examine the characteristics of students who became homeless due to Harvey. They found that students who became temporarily homeless for a year or less due to Harvey tended to fare as well as or better on educational outcomes than even their never-homeless peers.

In August 2017, the Houston area was ravaged by one of the costliest natural disasters in history--Hurricane Harvey. In this brief, the first in a two-part series, the authors examine the effects of Harvey on student homelessness in the Houston Independent School District (Houston ISD). The authors find that student homelessness in Houston ISD quadrupled due to Harvey, and most students experiencing homelessness lived, at least temporarily, in unsheltered contexts, such as sleeping in a car or on the street. Unlike other high-profile storms such as Hurricane Katrina, students who became homeless due to Harvey tended to be broadly representative of the district in terms of their demographic characteristics. However, they differed systematically from students who experienced homelessness for conventional, economic reasons such as job loss and medical debt, who were particularly likely to be Black. The authors conclude with implications of these findings for educational stakeholders in preparation for both generational and "everyday" homelessness crises.

Specialized high schools are an increasingly popular way to prepare young adults for postsecondary experiences and expand school choice. While much literature ex- amines charter school spillover effects and the effects of specialized schools on the students who attend them, little is known about the spillover effects of specialized high schools on traditional public schools (TPS). Using an event study design, we show that one type of specialized high school, North Carolina's Cooperative Innovative High Schools, initially attracted students who were higher achieving and more likely to be white than TPS students, but these specialized schools became more representative of the district population over time. On average, the opening of specialized schools had a mix of null and positive spillover effects on TPS student achievement. While there is some evidence of negative spillovers from the first schools that opened, the effects become more positive over time.

This First Look report introduces new data for national and state-level public elementary and secondary revenues and expenditures for fiscal year (FY) 2022. Specifically, this report includes the following school finance data: (1) revenue and expenditure totals; (2) revenues by source; (3) expenditures by function, subfunction, and object; (4) current expenditures; (5) revenues and current expenditures per pupil; (6) expenditures from Title I funds; and (7) revenues and expenditures from COVID-19 Federal Assistance Funds. The expenditure functions include instruction, support services, food services, and enterprise operations. The support services function is further broken down into seven subfunctions: instructional staff support services, pupil support services, general administration, school administration, operations and maintenance, student transportation, other support services (such as business services). Objects reported within a function or subfunction include salaries and wages, employee benefits, purchased services, supplies, and equipment. The purpose of a First Look report is to introduce new data through the presentation of tables containing descriptive information. The selected findings chosen for this report demonstrate the range of information available when using NPEFS. They do not represent all of the data and are not meant to emphasize any particular issue. While the tables in this report include data for all NPEFS respondents, the selected findings are limited to the 50 states and the District of Columbia.

This First Look report introduces new data for national and state-level public elementary and secondary revenues and expenditures for fiscal year (FY) 2022. Specifically, this report includes the following school finance data: (1) revenue and expenditure totals; (2) revenues by source; (3) expenditures by function, subfunction, and object; (4) current expenditures; (5) revenues and current expenditures per pupil; (6) expenditures from Title I funds; and (7) revenues and expenditures from COVID-19 Federal Assistance Funds. The expenditure functions include instruction, support services, food services, and enterprise operations. The support services function is further broken down into seven subfunctions: instructional staff support services, pupil support services, general administration, school administration, operations and maintenance, student transportation, other support services (such as business services).1 Objects reported within a function or subfunction include salaries and wages, employee benefits, purchased services, supplies, and equipment. The finance data used in this report are from the National Public Education Financial Survey (NPEFS), a component of the Common Core of Data (CCD). The CCD is one of NCES's primary survey programs on public elementary and secondary education in the United States. State education agencies (SEAs) in each of the 50 states, the District of Columbia, and the five other jurisdictions of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands report these data annually to NCES. The NPEFS instructions ask SEAs to report revenues and expenditures covering prekindergarten through high school public education in regular, special, and vocational schools; charter schools; and state-run education programs (such as special education schools or education programs for incarcerated youth).

High-quality machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing models for scientific applications such as weather and climate analysis. Unfortunately, despite the growing development of new deep learning models for weather and climate, there is a scarcity of curated, pre-processed machine learning (ML)-ready datasets. Curating such high-quality datasets for developing new models is challenging particularly because the modality of the input data varies significantly for different downstream tasks addressing different atmospheric scales (spatial and temporal). Here we introduce WxC-Bench (Weather and Climate Bench), a multi-modal dataset designed to support the development of generalizable AI models for downstream use-cases in weather and climate research. WxC-Bench is designed as a dataset of datasets for developing ML-models for a complex weather and climate system, addressing selected downstream tasks as machine learning phenomenon. WxC-Bench encompasses several atmospheric processes from meso-$\beta$ (20 - 200 km) scale to synoptic scales (2500 km), such as aviation turbulence, hurricane intensity and track monitoring, weather analog search, gravity wave parameterization, and natural language report generation. We provide a comprehensive description of the dataset and also present a technical validation for baseline analysis. The dataset and code to prepare the ML-ready data have been made publicly available on Hugging Face -- https://huggingface.co/datasets/nasa-impact/WxC-Bench

Legacy software systems, written in outdated languages like MUMPS and mainframe assembly, pose challenges in efficiency, maintenance, staffing, and security. While LLMs offer promise for modernizing these systems, their ability to understand legacy languages is largely unknown. This paper investigates the utilization of LLMs to generate documentation for legacy code using two datasets: an electronic health records (EHR) system in MUMPS and open-source applications in IBM mainframe Assembly Language Code (ALC). We propose a prompting strategy for generating line-wise code comments and a rubric to evaluate their completeness, readability, usefulness, and hallucination. Our study assesses the correlation between human evaluations and automated metrics, such as code complexity and reference-based metrics. We find that LLM-generated comments for MUMPS and ALC are generally hallucination-free, complete, readable, and useful compared to ground-truth comments, though ALC poses challenges. However, no automated metrics strongly correlate with comment quality to predict or measure LLM performance. Our findings highlight the limitations of current automated measures and the need for better evaluation metrics for LLM-generated documentation in legacy systems., Comment: Abbreviated version submitted to LLM4Code 2025 (a workshop co-located with ICSE 2025), 13 pages, 3 figures

We provide a simple framework for the study of parametric (multiplicative) noise, making use of scale parameters. We show that for a large class of stochastic differential equations increasing the multiplicative noise intensity surprisingly causes the mass of the stationary probability distribution to become increasingly concentrated around the minima of the multiplicative noise term, whilst under quite general conditions exhibiting a kind of intermittent burst like jumps between these minima. If the multiplicative noise term has one zero this causes on-off intermittency. Our framework relies on first term expansions, which become more accurate for larger noise intensities. In this work we show that the full width half maximum in addition to the maximum is appropriate for quantifying the stationary probability distribution (instead of the mean and variance, which are often undefined). We define a corresponding new kind of weak sense stationarity. We consider a double well potential as an example of application, demonstrating relevance to tipping points in noisy systems., Comment: 14 pages, 8 figures

The goal of this work is to study occurrences of non-unique solutions in dual-energy CT (DECT) for objects containing water and a contrast agent. Previous studies of the Jacobian of nonlinear systems identified that a vanishing Jacobian determinant indicates the existence of multiple solutions to the system. Vanishing Jacobian determinants are identified for DECT setups by simulating intensity data for practical thickness ranges of water and contrast agent. Once existence is identified, non-unique solutions are found by simulating scan data and finding intensity contours with that intersect multiple times. With this process non-unique solutions are found for DECT setups scanning iodine and gadolinium, including setups using tube potentials in practical ranges. Non-unique solutions demonstrate a large range of differences and can result in significant discrepancies between recovered and true material mapping., Comment: Proceedings paper for accepted contribution to the 8th International Conference on Image Formation in X-Ray Computed Tomography

The strange metal is a peculiar phase of matter in which the electron scattering rate, $\tau^{-1} \sim k_B T/\hbar$, which determines the electrical resistance, is universal across a wide family of materials and determined only by fundamental constants. In 1989, theorists hypothesized that this universality would manifest as scale-invariant behavior in the dynamic charge susceptibility, $\chi''(q,\omega)$. Here, we present momentum-resolved inelastic electron scattering measurements of the strange metal Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ showing that the susceptibility has the scale-invariant form $\chi''(q,\omega) = T^{-\nu} f(\omega/T)$, with exponent $\nu = 0.93$. We find the response is consistent with conformal invariance, meaning the dynamics may be thought of as occurring on a circle of radius $1/T$ in imaginary time, characterized by conformal dimension $\Delta = 0.05$. Our study indicates that the strange metal is a universal phenomenon whose properties are not determined by microscopic properties of a particular material., Comment: 14 pages, 4 figures + supplementary data

In this second paper on the DECam deep drilling field (DDF) program we release 2,020 optical gri-band light curves for transients and variables in the extragalactic COSMOS and ELAIS fields based on time series observations with a 3-day cadence from semester 2021A through 2023A. In order to demonstrate the wide variety of time domain events detected by the program and encourage others to use the data set, we characterize the sample by presenting a brief analysis of the light curve parameters such as time span, amplitude, and peak brightness. We also present preliminary light curve categorizations, and identify potential stellar variables, active galactic nuclei, tidal disruption events, supernovae (such as Type Ia, Type IIP, superluminous, and gravitationally lensed supernovae), and fast transients. Where relevant, the number of identified transients is compared to the predictions of the original proposal. We also discuss the challenges of analyzing DDF data in the context of the upcoming Vera C. Rubin Observatory and its Legacy Survey of Space and Time, which will include DDFs. Images from the DECam DDF program are available without proprietary period and the light curves presented in this work are publicly available for analysis., Comment: 17 pages, 18 figures, 1 table; accepted for publication in MNRAS

Dual-comb ranging has emerged as an effective technology for long-distance metrology, providing absolute distance measurements with high speed, precision, and accuracy. Here, we demonstrate a dual-comb ranging method that utilizes a free-space transceiver unit, enabling dead-zone-free measurements and simultaneous ranging with interchanged comb roles to allow for long-distance measurements even when the target is moving. It includes a GPU-accelerated algorithm for real-time signal processing and a free-running single-cavity solid-state dual-comb laser with a carrier wavelength $\lambda_c \approx$ 1055 nm, a pulse repetition rate of 1 GHz and a repetition rate difference of 5.06 kHz. This combination offers a fast update rate and sufficient signal strength to reach a single-shot time-of-flight precision of around 0.1 $\mu$m (i.e. $< \lambda_c/4$) on a cooperative target placed at a distance of more than 40 m. The free-running laser is sufficiently stable to use the phase information for interferometric distance measurements, which improves the single-shot precision to $<$20 nm. To assess the ranging accuracy, we track the motion of the cooperative target when moved over 40 m and compare it to a reference interferometer. The residuals between the two measurements are below 3 $\mu$m. These results highlight the potential of this approach for accurate and dead-zone-free long-distance ranging, supporting real-time tracking with nm-level precision., Comment: Supplementary Information included

Due to the importance of a reference atmospheric radiative transfer model for both planning and calibrating ground-based observations at millimetre and submillimetre wavelengths, we have undertaken a validation campaign consisting of acquiring atmospheric spectra under different weather conditions, in different diurnal moments and seasons, with the Atacama Pathfinder EXperiment (APEX), due to the excellent stability of its receivers and the very high frequency resolution of its backends. As a result, a data set consisting of 56 spectra within the 157.3-742.1 GHz frequency range, at kHz resolution (smoothed to $\sim$2-10 MHz for analysis), and spanning one order of magnitude ($\sim$ 0.35-3.5 mm) in precipitable water vapour columns, has been gathered from October 2020 to September 2022. These data are unique for their quality and completeness and, due to the proximity of APEX to the Atacama Large Millimetre/Submillimetre Array (ALMA), they provide an excellent opportunity to validate the atmospheric radiative transfer model currently installed in the ALMA software. The main issues addressed in the study are possible missing lines in the model, line shapes, vertical profiles of atmospheric physical parameters and molecular abundances, seasonal and diurnal variations and collision induced absorption (CIA), to which this paper is devoted, in its N$_2$-N$_2$ + N$_2$-O$_2$ + O$_2$-O$_2$ (dry), and N$_2$-H$_2$O + O$_2$-H$_2$O (``foreign'' wet) mechanisms. All these CIA terms should remain unchanged in the above mentioned ALMA atmospheric model as a result of this work., Comment: 10 pages, 5 figures. Accepted for publication in A&A

Autonomous agents require the capability to identify dynamic objects in their environment for safe planning and navigation. Incomplete and erroneous dynamic detections jeopardize the agent's ability to accomplish its task. Dynamic detection is a challenging problem due to the numerous sources of uncertainty inherent in the problem's inputs and the wide variety of applications, which often lead to use-case-tailored solutions. We propose a robust learning-free approach to segment moving objects in point cloud data. The foundation of the approach lies in modelling each voxel using a hidden Markov model (HMM), and probabilistically integrating beliefs into a map using an HMM filter. The proposed approach is tested on benchmark datasets and consistently performs better than or as well as state-of-the-art methods with strong generalized performance across sensor characteristics and environments. The approach is open-sourced at https://github.com/vb44/HMM-MOS., Comment: Accepted to the IEEE IROS 2024 workshop on Long-Term Perception for Autonomy in Dynamic Human-shared Environments: What Do Robots Need?

In cinema, visual motifs are recurrent iconographic compositions that carry artistic or aesthetic significance. Their use throughout the history of visual arts and media is interesting to researchers and filmmakers alike. Our goal in this work is to recognise and classify these motifs by proposing a new machine learning model that uses a custom dataset to that end. We show how features extracted from a CLIP model can be leveraged by using a shallow network and an appropriate loss to classify images into 20 different motifs, with surprisingly good results: an $F_1$-score of 0.91 on our test set. We also present several ablation studies justifying the input features, architecture and hyperparameters used., Comment: 17 pages, 11 figures, one table, to be published in the conference proceedings of ECCV 2024

Object tracking, especially animal tracking, is one of the key topics that attract a lot of attention due to its benefits of animal behavior understanding and monitoring. Recent state-of-the-art tracking methods are founded on deep learning architectures for object detection, appearance feature extraction and track association. Despite the good tracking performance, these methods are trained and evaluated on common objects such as human and cars. To perform on the animal, there is a need to create large datasets of different types in multiple conditions. The dataset construction comprises of data collection and data annotation. In this work, we put more focus on the latter task. Particularly, we renovate the well-known tool, LabelMe, so as to assist common user with or without in-depth knowledge about computer science to annotate the data with less effort. The new tool named as TrackMe inherits the simplicity, high compatibility with varied systems, minimal hardware requirement and convenient feature utilization from the predecessor. TrackMe is an upgraded version with essential features for multiple object tracking annotation.

We study the interactive effects (IE) model as an extension of the conventional additive effects (AE) model. For the AE model, the fixed effects estimator can be obtained by applying least squares to a regression that adds a linear projection of the fixed effect on the explanatory variables (Mundlak, 1978; Chamberlain, 1984). In this paper, we develop a novel estimator -- the projection-based IE (PIE) estimator -- for the IE model that is based on a similar approach. We show that, for the IE model, fixed effects estimators that have appeared in the literature are not equivalent to our PIE estimator, though both can be expressed as a generalized within estimator. Unlike the fixed effects estimators for the IE model, the PIE estimator is consistent for a fixed number of time periods with no restrictions on serial correlation or conditional heteroskedasticity in the errors. We also derive a statistic for testing the consistency of the two-way fixed effects estimator in the possible presence of iterative effects. Moreover, although the PIE estimator is the solution to a high-dimensional nonlinear least squares problem, we show that it can be computed by iterating between two steps, both of which have simple analytical solutions. The computational simplicity is an important advantage relative to other strategies that have been proposed for estimating the IE model for short panels. Finally, we compare the finite sample performance of IE estimators through simulations.

Located 10.888 pc from Earth, COCONUTS-2b is a planetary-mass companion to a young (150-800 Myr) M3 star, with a wide orbital separation (6471 au) and a low companion-to-host mass ratio ($0.021\pm0.005$). We have studied the atmospheric properties of COCONUTS-2b using newly acquired 1.0-2.5 $\mu$m spectroscopy from Gemini/Flamingos-2. The spectral type of COCONUTS-2b is refined to T$9.5 \pm 0.5$ based on comparisons with T/Y dwarf spectral templates. We have conducted an extensive forward-modeling analysis, comparing the near-infrared spectrum and mid-infrared broadband photometry with sixteen state-of-the-art atmospheric model grids developed for brown dwarfs and self-luminous exoplanets near the T/Y transition. The PH$_{3}$-free ATMO2020++, ATMO2020++, and Exo-REM models best match the specific observations of COCONUTS-2b, regardless of variations in the input spectrophotometry. This analysis suggests the presence of disequilibrium chemistry, along with a diabatic thermal structure and/or clouds, in the atmosphere of COCONUTS-2b. All models predict fainter $Y$-band fluxes than observed, highlighting uncertainties in the alkali chemistry models and opacities. We determine a bolometric luminosity of $\log{(L_{\rm bol}/L_{\odot})}=-6.18$ dex, with a 0.5 dex-wide range of $[-6.43,-5.93]$ dex that accounts for various assumptions of models. Using thermal evolution models, we derive an effective temperature of $T_{\rm eff}=483^{+44}_{-53}$ K, a surface gravity of $\log{(g)}=4.19^{+0.18}_{-0.13}$ dex, a radius of $R=1.11^{+0.03}_{-0.04}$ R$_{\rm Jup}$, and a mass of $M=8 \pm 2$ M$_{\rm Jup}$. Various atmospheric model grids consistently indicate that COCONUTS-2b's atmosphere has sub- or near-solar metallicity and C/O. These findings provide valuable insights into COCONUTS-2b's formation history and the potential outward migration to its current wide orbit., Comment: Accepted for publication in AJ. Main text: Pages 1-25, Figures 1-11, Tables 1-4; Appendix: Pages 26-43, Figures 12-15. Mostly unchanged from the previous version, except for footnotes 6-15, which were updated based on suggestions from the AJ data editor. The Gemini/F2 spectrum of COCONUTS-2b is accessible via https://doi.org/10.5281/zenodo.13975825

We study two dimensional and three dimensional tropical subrepresentations of the regular representation $\mathbb{B}[G]$ of a finite group over the tropical booleans, utilizing the theory of group representations over a fixed idempotent semifield as developed by Giansiracusa--Manaker. In dimension two we completely classify all two dimensional tropical subrepresentations of $\mathbb{B}[G]$, provide an explicit characterization for the set of bases of the corresponding matroids, and show an equivalence with the subgroups of $G$. In dimension three we show such an equivalence no longer holds. Towards a classification in dimension three we give a collection of tropical subrepresentations corresponding to subgroups of index 2, and we show that in the special case of finite cyclic groups, one can find three dimensional tropical subrepresentations that do not correspond to subgroups in a similar way., Comment: 18 pages

We give an overview of the science objectives and mission design of the Spectroscopic Time-Resolving Observatory for Broadband Energy X-rays (STROBE-X) observatory, which has been proposed as a NASA probe-class (~$1.5B) mission in response to the Astro2020 recommendation for an X-ray probe., Comment: 11 pages, 5 figures, accepted for publication in JATIS

Type II supernovae (SNeII) mark the endpoint in the lives of hydrogen-rich massive stars. Their large explosion energies and luminosities allow us to measure distances, metallicities, and star formation rates into the distant Universe. To fully exploit their use in answering different astrophysical problems, high-quality low-redshift data sets are required. Such samples are vital to understand the physics of SNeII, but also to serve as calibrators for distinct - and often lower-quality - samples. We present uBgVri optical and YJH near-infrared (NIR) photometry for 94 low-redshift SNeII observed by the Carnegie Supernova Project (CSP). A total of 9817 optical and 1872 NIR photometric data points are released, leading to a sample of high-quality SNII light curves during the first ~150 days post explosion on a well-calibrated photometric system. The sample is presented and its properties are analysed and discussed through comparison to literature events. We also focus on individual SNeII as examples of classically defined subtypes and outlier objects. Making a cut in the plateau decline rate of our sample (s2), a new subsample of fast-declining SNeII is presented. The sample has a median redshift of 0.015, with the nearest event at 0.001 and the most distant at 0.07. At optical wavelengths (V), the sample has a median cadence of 4.7 days over the course of a median coverage of 80 days. In the NIR (J), the median cadence is 7.2 days over the course of 59 days. The fast-declining subsample is more luminous than the full sample and shows shorter plateau phases. Of the non-standard SNeII highlighted, SN2009A particularly stands out with a steeply declining then rising light curve, together with what appears to be two superimposed P-Cygni profiles of H-alpha in its spectra. We outline the significant utility of these data, and finally provide an outlook of future SNII science., Comment: Accepted for publication in A&A. Photometric data will be uploaded to the CDS and the CSP website, and can also be requested from the first author

Large recommendation models (LRMs) are fundamental to the multi-billion dollar online advertising industry, processing massive datasets of hundreds of billions of examples before transitioning to continuous online training to adapt to rapidly changing user behavior. The massive scale of data directly impacts both computational costs and the speed at which new methods can be evaluated (R&D velocity). This paper presents actionable principles and high-level frameworks to guide practitioners in optimizing training data requirements. These strategies have been successfully deployed in Google's largest Ads CTR prediction models and are broadly applicable beyond LRMs. We outline the concept of data convergence, describe methods to accelerate this convergence, and finally, detail how to optimally balance training data volume with model size.

To refine formal methods for concurrent systems, there are several ways of enriching classical operational semantics of process calculi. One can enable the auditing and undoing of past synchronisations thanks to communication keys, thus easing the study of true concurrency as a by-product. Alternatively, proof labels embed information about the origins of actions in transition labels, facilitating syntactic analysis. Enriching proof labels with keys enables a theory of the relations on transitions and on events based on their labels only. We offer for the first time separate definitions of dependence relation and independence relation, and prove their complementarity on connected transitions instead of postulating it. Leveraging the recent axiomatic approach to reversibility, we prove the canonicity of these relations and provide additional tools to study the relationships between e.g., concurrency and causality on transitions and events. Finally, we make precise the subtle relationship between bisimulations based on both forward and backward transitions, on key ordering, and on dependency preservation, providing a direct definition of History Preserving bisimulation for CCS.

This paper presents a synthesis of three collaboratively conducted studies exploring the relationship between career-focused advising and the postsecondary transition with an emphasis on students enrolled in Career and Technical Education (CTE). The studies included a survey administered to high school seniors in New York City schools, an impact study of career coaches in North Carolina, and a qualitative study looking at implementation of advising in New York City and North Carolina. Key themes coming from the three studies included: 1) postsecondary transition advising is a schoolwide phenomenon; 2) the content and intensity shifts over a student's time in high school; 3) advising tends to focus more on college with less attention paid to career opportunities except in settings with an explicit career emphasis; 4) students with more advantaged backgrounds tend to participate in advising at higher levels; 5) higher participation in advising is associated with an increase in CTE-related activities; 6) college-focused advising is associated with higher enrollment in four-year schools while career-focused advising is associated with higher enrollment in two-year and lower enrollment in four-year institutions; and 7) additional research on how advising outcomes differ by student characteristics is needed. This article also summarizes key methodological takeaways about doing research related to advising. [This paper was created by the Early College Research Center, UNC Greensboro.]

A series of longstanding questions in harmonic analysis ask if the intersection of all prime ``$p$-adic versions" of an object, such as a doubling measure, or a Muckenhoupt or reverse H\"older weight, recovers the full object. Investigation into these questions was reinvigorated in 2019 by work of Boylan-Mills-Ward, culminating in showing that this recovery fails for a finite intersection in work of Anderson-Bellah-Markman-Pollard-Zeitlin. Via generalizing a new number-theoretic construction therein, we answer these questions.

Probabilistic logical models are a core component of neurosymbolic AI and are important models in their own right for tasks that require high explainability. Unlike neural networks, logical models are often handcrafted using domain expertise, making their development costly and prone to errors. While there are algorithms that learn logical models from data, they are generally prohibitively expensive, limiting their applicability in real-world settings. In this work, we introduce precision and recall for logical rules and define their composition as rule utility -- a cost-effective measure to evaluate the predictive power of logical models. Further, we introduce SPECTRUM, a scalable framework for learning logical models from relational data. Its scalability derives from a linear-time algorithm that mines recurrent structures in the data along with a second algorithm that, using the cheap utility measure, efficiently ranks rules built from these structures. Moreover, we derive theoretical guarantees on the utility of the learnt logical model. As a result, SPECTRUM learns more accurate logical models orders of magnitude faster than previous methods on real-world datasets., Comment: 21 pages

We present initial results of a forthcoming benchmark for evaluating LLM agents on white-collar tasks of economic value. We evaluate agents on real-world "messy" open-web research tasks of the type that are routine in finance and consulting. In doing so, we lay the groundwork for an LLM agent evaluation suite where good performance directly corresponds to a large economic and societal impact. We built and tested several agent architectures with o1-preview, GPT-4o, Claude-3.5 Sonnet, Llama 3.1 (405b), and GPT-4o-mini. On average, LLM agents powered by Claude-3.5 Sonnet and o1-preview substantially outperformed agents using GPT-4o, with agents based on Llama 3.1 (405b) and GPT-4o-mini lagging noticeably behind. Across LLMs, a ReAct architecture with the ability to delegate subtasks to subagents performed best. In addition to quantitative evaluations, we qualitatively assessed the performance of the LLM agents by inspecting their traces and reflecting on their observations. Our evaluation represents the first in-depth assessment of agents' abilities to conduct challenging, economically valuable analyst-style research on the real open web.

This work presents a proof-of-concept solution designed to improve the accuracy of radiographic feature characterisation in pre-surgical CT/MR volumes. The solution involves 3D co-location of 2D digital histology slides within ex-vivo, tumour tissue CT volumes. In the initial step, laboratory measurements obtained during histology dissection were used to seed the placement of the individual histology slices in corresponding tumour tissue CT volumes. The process was completed by aligning corresponding bone in histology images to bone in the CT using in-plane point-based registration. Six bisected canine humerus datasets of ex-vivo CT and corresponding measurements were used to validate dissection placements. Digital seeding exhibited a plane misalignment of 0.19 +- 1.8 mm. User input sensitivity caused 0.08 +- 0.2 mm in plane translation and between 0 and 1.6 degrees deviation. These are of similar magnitude to reported misalignment of 0.9-1.3 mm and 1.1-1.9 degrees in related prostate histology co-location [1]. Although this work only reported on animal femur sarcoma CT images and histology slide images, the solution can be generalised to various sarcoma geometries and presentation sites. Furthermore, the solution co-locates high-fidelity data to advance tissue characterisation with minimal disruption to existing clinical workflows. Improved tissue characterisation accuracy, supported by the resolution of histology images, can enhance surgical planning accuracy and patient outcomes by bringing the insights offered by histology closer to the start of the presentation-diagnosis-planning-surgery-recovery loop., Comment: 17 pages, 6 figures

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tuned to address multiple, different use cases -- the developments on the weather and climate side largely focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Prithvi WxC employs an encoder-decoder-based architecture, incorporating concepts from various recent transformer models to effectively capture both regional and global dependencies in the input data. The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. Furthermore, it is trained with a mixed objective that combines the paradigms of masked reconstruction with forecasting. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation. The pretrained model with 2.3 billion parameters, along with the associated fine-tuning workflows, has been publicly released as an open-source contribution via Hugging Face.

Background: Advanced methods for causal inference, such as targeted maximum likelihood estimation (TMLE), require certain conditions for statistical inference. However, in situations where there is not differentiability due to data sparsity or near-positivity violations, the Donsker class condition is violated. In such situations, TMLE variance can suffer from inflation of the type I error and poor coverage, leading to conservative confidence intervals. Cross-validation of the TMLE algorithm (CVTMLE) has been suggested to improve on performance compared to TMLE in settings of positivity or Donsker class violations. We aim to investigate the performance of CVTMLE compared to TMLE in various settings. Methods: We utilised the data-generating mechanism as described in Leger et al. (2022) to run a Monte Carlo experiment under different Donsker class violations. Then, we evaluated the respective statistical performances of TMLE and CVTMLE with different super learner libraries, with and without regression tree methods. Results: We found that CVTMLE vastly improves confidence interval coverage without adversely affecting bias, particularly in settings with small sample sizes and near-positivity violations. Furthermore, incorporating regression trees using standard TMLE with ensemble super learner-based initial estimates increases bias and variance leading to invalid statistical inference. Conclusions: It has been shown that when using CVTMLE the Donsker class condition is no longer necessary to obtain valid statistical inference when using regression trees and under either data sparsity or near-positivity violations. We show through simulations that CVTMLE is much less sensitive to the choice of the super learner library and thereby provides better estimation and inference in cases where the super learner library uses more flexible candidates and is prone to overfitting., Comment: 20 pages, 3 figures, 1 table

We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839-10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less, and can detect 10x more FRBs than the current CRAFT incoherent sum system (i.e., 0.5-2 localised FRBs per day), enabling us to better constrain he models for FRBs and use them as cosmological probes., Comment: 26 pages, 19 figures, 9 tables, Accepted for publication in PASA

Scalar field comparison is a fundamental task in scientific visualization. In topological data analysis, we compare topological descriptors of scalar fields -- such as persistence diagrams and merge trees -- because they provide succinct and robust abstract representations. Several similarity measures for topological descriptors seem to be both asymptotically and practically efficient with polynomial time algorithms, but they do not scale well when handling large-scale, time-varying scientific data and ensembles. In this paper, we propose a new framework to facilitate the comparative analysis of merge trees, inspired by tools from locality sensitive hashing (LSH). LSH hashes similar objects into the same hash buckets with high probability. We propose two new similarity measures for merge trees that can be computed via LSH, using new extensions to Recursive MinHash and subpath signature, respectively. Our similarity measures are extremely efficient to compute and closely resemble the results of existing measures such as merge tree edit distance or geometric interleaving distance. Our experiments demonstrate the utility of our LSH framework in applications such as shape matching, clustering, key event detection, and ensemble summarization., Comment: IEEE VIS 2024

The spin structure functions of the proton and the deuteron were measured during the EG4 experiment at Jefferson Lab in 2006. Data were collected for longitudinally polarized electron scattering off longitudinally polarized NH$_3$ and ND$_3$ targets, for $Q^2$ values as small as 0.012 and 0.02 GeV$^2$, respectively, using the CEBAF Large Acceptance Spectrometer (CLAS). This is the archival paper of the EG4 experiment that summaries the previously reported results of the polarized structure functions $g_1$, $A_1F_1$, and their moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$, for both the proton and the deuteron. In addition, we report on new results on the neutron $g_1$ extracted by combining proton and deuteron data and correcting for Fermi smearing, and on the neutron moments $\overline \Gamma_1$, $\overline \gamma_0$, and $\overline I_{TT}$ formed directly from those of the proton and the deuteron. Our data are in good agreement with the Gerasimov-Drell-Hearn sum rule for the proton, deuteron, and neutron. Furthermore, the isovector combination was formed for $g_1$ and the Bjorken integral $\overline \Gamma_1^{p-n}$, and compared to available theoretical predictions. All of our results provide for the first time extensive tests of spin observable predictions from chiral effective field theory ($\chi$EFT) in a $Q^2$ range commensurate with the pion mass. They motivate further improvement in $\chi$EFT calculations from other approaches such as the lattice gauge method., Comment: 33 pages. 26 figures. Data table provided in supplementary material (30 pages)

To assess prior mean-field studies that the interacting Kane-Mele model supports a novel antiferromagnetic Chern insulating phase (AFCI) for a wide range of sublattice potentials, we analyze the Kane-Mele-Hubbard model in the presence of a sublattice potential using determinant quantum Monte Carlo (DQMC). Instead of an AFCI, we find that the ground state is a quantum spin Hall (QSH) insulator for intermediate values of the sublattice potential $\lambda_v$, albeit with a small gap. The QSH state gives way to a trivial band insulator as the sublattice potential increases beyond a critical value. Only at small sublattice potentials does the QSH state transition into a trivial Mott insulator with xy antiferromagnetic correlations. The QAH feature is only observed at high temperature. The QAH feature crosses over to an incipient QSH state when the topological gap stabilizes. Our work is consistent with the experimental observation that in twisted bilayer MoTe$_2$ and WSe$_2$ as well as AB stacked MoTe$_2$/WSe$_2$, where QSH is consistently observed at even-integer filling over a wide range of parameters.

We study in depth the charge susceptibility for the band Hatsugai-Kohmoto (HK) and orbital (OHK) models. As either of these models describes a Mott insulator, the charge susceptibility takes on the form of a modified Lindhard function with lower and upper Hubbard bands, thereby giving rise to a multi-pole structure. The particle-hole continuum consists of hot spots along the $\omega$ vs $q$ axis arising from inter-band transitions. Such transitions, which are strongly suppressed in non-interacting systems, are obtained here because of the non-rigidity of the Hubbard bands. This modified Lindhard function gives rise to a plasmon dispersion that is inversely dependent on the momentum, resulting in an additional contribution to the conventional f-sum rule. This extra contribution originates from a long-range diamagnetic contribution to the current. This results in a non-commutativity of the long-wavelength ($q\rightarrow 0$) and thermodynamic ($L\rightarrow\infty$) limits. When the correct limits are taken, we find that the Kubo response computed with either open or periodic boundary conditions yields identical results that are consistent with the continuity equation contrary to recent claims. We also show that the long wavelength pathology of the current noted previously also plagues the Anderson impurity model interpretation of dynamical mean-field theory (DMFT). Coupled with our previous work\cite{mai20231} which showed that HK is the correct $d=\infty$ limit of the Hubbard model, we arrive at the conclusion that single-orbital HK=DMFT., Comment: typos corrected and figures edited

Understanding the rapid formation of supermassive black holes (SMBHs) in the early universe requires an understanding of how stellar mass grows in the host galaxies. Here, we perform an analysis of rest-frame optical spectra and imaging from JWST of two quasar host galaxies at z>6 which exhibit Balmer absorption lines. These features in the stellar continuum indicate a lack of young stars, similar to low-redshift post-starburst galaxies whose star formation was recently quenched. We find that the stellar mass (log(M_* / M_sun) > 10.6) of each quasar host grew in a starburst episode at redshift 7 or 8. One of the targets exhibits little ongoing star formation, as evidenced by the photometric signature of the Balmer break and a lack of spatially resolved H-alpha emission, placing it well below the star formation main sequence at z = 6. The other galaxy is transitioning to a quiescent phase; together, the two galaxies represent the most distant massive post-starburst galaxies known. The maturity of these two galaxies is further supported by the stellar velocity dispersions of their host galaxies, placing them slightly above the upper end of the local M_BH - sigma_* relation. The properties of our two post-starburst galaxies, each hosting an active SMBH with log(M_BH / M_sun) > 9, suggests that black holes played a major role in shaping the formation of the first massive galaxies in the Universe., Comment: 24 pages, 7 figures, submitted to a Nature journal