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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).

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).

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.

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

The Knaster-Tarski theorem, also known as Tarski's theorem, guarantees that every monotone function defined on a complete lattice has a fixed point. We analyze the query complexity of finding such a fixed point on the $k$-dimensional grid of side length $n$ under the $\leq$ relation. Specifically, there is an unknown monotone function $f: \{0,1,\ldots, n-1\}^k \to \{0,1,\ldots, n-1\}^k$ and an algorithm must query a vertex $v$ to learn $f(v)$. Our main result is a randomized lower bound of $\Omega\left( k + \frac{k \cdot \log{n}}{\log{k}} \right)$ for the $k$-dimensional grid of side length $n$, which is nearly optimal in high dimensions when $k$ is large relative to $n$. As a corollary, we characterize the randomized and deterministic query complexity on the Boolean hypercube $\{0,1\}^k$ as $\Theta(k)$., Comment: 12 pages, 2 figures

We describe a novel splitting approach to numerical relativistic magnetohydrodynamics (RMHD) designed to expand its applicability to the domain of ultra-high magnetisation (high-$\sigma$). In this approach, the electromagnetic field is split into the force-free component and its perturbation due to the plasma inertia. Accordingly, the system of RMHD equations is extended to include the subsystem of force-free degenerate electrodynamics and the subsystem governing the plasma dynamics and the perturbation of the force-free field. The combined system of conservation laws is integrated simultaneously, to which aim various numerical techniques can be used, and the force-free field is recombined with its perturbation at the end of every timestep. To explore the potential of this splitting approach, we combined it with a 3rd-order WENO method, and carried out a variety of 1D and 2D test simulations. The simulations confirm the robustness of the splitting method in the high-$\sigma$ regime, and also show that it remains accurate in the low-$\sigma$ regime, all the way down to $\sigma$ = 0. Thus, the method can be used for simulating complex astrophysical flows involving a wide range of physical parameters. The numerical resistivity of the code obeys a simple ansatz and allows fast magnetic reconnection in the plasmoid-dominated regime. The results of simulations involving thin and long current sheets agree very well with the theory of resistive magnetic reconnection., Comment: submitted to MNRAS, revised, typos corrected

Understanding the dynamical behavior of complex systems from their underlying network architectures is a long-standing question in complexity theory. Therefore, many metrics have been devised to extract network features like motifs, centrality, and modularity measures. It has previously been proposed that network symmetries are of particular importance since they are expected to underly the synchronization of a system's units, which is ubiquitously observed in nervous system activity patterns. However, perfectly symmetrical structures are difficult to assess in noisy measurements of biological systems, like neuronal connectomes. Here, we devise a principled method to infer network symmetries from combined connectome and neuronal activity data. Using nervous system-wide population activity recordings of the \textit{C.elegans} backward locomotor system, we infer structures in the connectome called fibration symmetries, which can explain which group of neurons synchronize their activity. Our analysis suggests functional building blocks in the animal's motor periphery, providing new testable hypotheses on how descending interneuron circuits communicate with the motor periphery to control behavior. Our approach opens a new door to exploring the structure-function relations in other complex systems, like the nervous systems of larger animals., Comment: 32 pages, 7 figures

In his book 'A Beautiful Question', physicist Frank Wilczek argues that symmetry is 'nature's deep design,' governing the behavior of the universe, from the smallest particles to the largest structures. While symmetry is a cornerstone of physics, it has not yet been found widespread applicability to describe biological systems, particularly the human brain. In this context, we study the human brain network engaged in language and explore the relationship between the structural connectivity (connectome or structural network) and the emergent synchronization of the mesoscopic regions of interest (functional network). We explain this relationship through a different kind of symmetry than physical symmetry, derived from the categorical notion of Grothendieck fibrations. This introduces a new understanding of the human brain by proposing a local symmetry theory of the connectome, which accounts for how the structure of the brain's network determines its coherent activity. Among the allowed patterns of structural connectivity, synchronization elicits different symmetry subsets according to the functional engagement of the brain. We show that the resting state is a particular realization of the cerebral synchronization pattern characterized by a fibration symmetry that is broken in the transition from rest to language. Our findings suggest that the brain's network symmetry at the local level determines its coherent function, and we can understand this relationship from theoretical principles., Comment: 43 pages, 9 figures

Over the last two decades, spatial light modulators (SLMs) have revolutionised our ability to shape optical fields. They grant independent dynamic control over thousands of degrees-of-freedom within a single light beam. In this work we test a new type of SLM, known as a phase-only light modulator (PLM), that blends the high efficiency of liquid crystal SLMs with the fast switching rates of binary digital micro-mirror devices (DMDs). A PLM has a 2D mega-pixel array of micro-mirrors. The vertical height of each micro-mirror can be independently adjusted with 4-bit precision. Here we provide a concise tutorial on the operation and calibration of a PLM. We demonstrate arbitrary pattern projection, aberration correction, and control of light transport through complex media. We show high-speed wavefront shaping through a multimode optical fibre -- scanning over 2000 points at 1.44 kHz. We make available our custom high-speed PLM control software library developed in C++. As PLMs are based upon micro-electromechanical system (MEMS) technology, they are polarisation agnostic, and possess fundamental switching rate limitations equivalent to that of DMDs -- with operation at up to 10 kHz anticipated in the near future. We expect PLMs will find high-speed light shaping applications across a range of fields including adaptive optics, microscopy, optogenetics and quantum optics., Comment: 15 pages, 5 figures

Multimodal language models can exhibit hallucinations in their outputs, which limits their reliability. The ability to automatically detect these errors is important for mitigating them, but has been less explored and existing efforts do not localize hallucinations, instead framing this as a classification task. In this work, we first pose multimodal hallucination detection as a sequence labeling task where models must localize hallucinated text spans and present a strong baseline model. Given the high cost of human annotations for this task, we propose an approach to improve the sample efficiency of these models by creating corrupted grounding data, which we use for pre-training. Leveraging phrase grounding data, we generate hallucinations to replace grounded spans and create hallucinated text. Experiments show that pre-training on this data improves sample efficiency when fine-tuning, and that the learning signal from the grounding data plays an important role in these improvements.

This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

Generative Flow Networks (GFlowNets) are a class of generative models that sample objects in proportion to a specified reward function through a learned policy. They can be trained either on-policy or off-policy, needing a balance between exploration and exploitation for fast convergence to a target distribution. While exploration strategies for discrete GFlowNets have been studied, exploration in the continuous case remains to be investigated, despite the potential for novel exploration algorithms due to the local connectedness of continuous domains. Here, we introduce Adapted Metadynamics, a variant of metadynamics that can be applied to arbitrary black-box reward functions on continuous domains. We use Adapted Metadynamics as an exploration strategy for continuous GFlowNets. We show three continuous domains where the resulting algorithm, MetaGFN, accelerates convergence to the target distribution and discovers more distant reward modes than previous off-policy exploration strategies used for GFlowNets., Comment: 10 pages

The dynamics of domain walls in a square of permalloy (Ni$_{81}$Fe$_{19}$; Py) upon excitation with an oscillating magnetic field of 4 mT amplitude were recorded by pump-probe ptychography with X-ray magnetic circular dichroism (XMCD) at the Ni L$_3$-edge. The 2.5 $\mu$m Py square of 160 nm thickness forms a vortex flux-closure pattern with domain walls that fall into alternating out-of-plane magnetization states due to the interplay of in-plane shape and growth-induced perpendicular anisotropies. Dynamic modes of the domain wall structure were excitable along with the vortex core gyration with frequencies of 500 MHz and 1 GHz. Micromagnetic simulations served to corroborate the imaged domain wall motion.

We use two different methods, Monte Carlo sampling and variational inference (VI), to perform a Bayesian calibration of the effective-range parameters in ${}^3$He-${}^4$He elastic scattering. The parameters are calibrated to data from a recent set of $^{3}$He-${}^4$He elastic scattering differential cross section measurements. Analysis of these data for $E_{\rm lab} \leq 4.3$ MeV yields a unimodal posterior for which both methods obtain the same structure. However, the effective-range expansion amplitude does not account for the $7/2^-$ state of ${}^7$Be so, even after calibration, the description of data at the upper end of this energy range is poor. The data up to $E_{\rm lab}=2.6$ MeV can be well described, but calibration to this lower-energy subset of the data yields a bimodal posterior. After adapting VI to treat such a multi-modal posterior we find good agreement between the VI results and those obtained with parallel-tempered Monte Carlo sampling., Comment: 13 Pages, 6 Figures

The standard theory of musical scales since antiquity has been based on harmony, rather than melody. Some recent analyses support either view, and we lack a comparative test on cross-cultural data. We address this longstanding problem through a rigorous, computational comparison of the main theories against 1,314 scales from 96 countries. There is near-universal support for melodic theories, which predict step-sizes of 1-3 semitones. Harmony accounts for the prevalence of some simple-integer-ratio intervals, particularly for music-theoretic scales from Eurasian societies, which may explain their dominance amongst Western scholars. However, harmony poorly predicts scales measured from ethnographic recordings, particularly outside of Eurasia. Overall, we show that the historical emphasis on harmony is misguided and that melody is the primary determinant of the world's musical scales.

In recent years, multi-agent teaming has garnered considerable interest since complex objectives, such as intelligence, surveillance, and reconnaissance, can be divided into multiple cluster-level sub-tasks and assigned to a cluster of agents with the appropriate functionality. Yet, coordination and information dissemination between clusters may be necessary to accomplish a desired objective. Distributed consensus protocols provide a mechanism for spreading information within clustered networks, allowing agents and clusters to make decisions without requiring direct access to the state of the ensemble. Hence, we propose a strategy for achieving system-wide consensus in the states of identical linear time-invariant systems coupled by an undirected graph whose directed sub-graphs are available only at sporadic times. Within this work, the agents of the network are organized into pairwise disjoint clusters, which induce sub-graphs of the undirected parent graph. Some cluster sub-graph pairs are linked by an inter-cluster sub-graph, where the union of all cluster and inter-cluster sub-graphs yields the undirected parent graph. Each agent utilizes a distributed consensus protocol with components that are updated intermittently and asynchronously with respect to other agents. The closed-loop ensemble dynamics is modeled as a hybrid system, and a Lyapunov-based stability analysis yields sufficient conditions for rendering the agreement subspace (consensus set) globally exponentially stable. Furthermore, an input-to-state stability argument demonstrates the consensus set is robust to a class of perturbations. A numerical simulation considering both nominal and perturbed scenarios is provided for validation purposes.

Motor-driven cytoskeletal remodeling in cellular systems can often be accompanied by a diffusive-like effect at local scales, but distinguishing the contributions of the ordering process, such as active contraction of a network, from this active diffusion is difficult to achieve. Using light-dimerizable kinesin motors to spatially control the formation and contraction of a microtubule network, we deliberately photobleach a grid pattern onto the filament network serving as a transient and dynamic coordinate system to observe the deformation and translation of the remaining fluorescent squares of microtubules. We find that the network contracts at a rate set by motor speed but is accompanied by a diffusive-like spread throughout the bulk of the contracting network with effective diffusion constant two orders of magnitude lower than that for a freely-diffusing microtubule. We further find that on micron scales, the diffusive timescale is only a factor of approximately 3 slower than that of advection regardless of conditions, showing that the global contraction and long-time relaxation from this diffusive behavior are both motor-driven but exhibit local competition within the network bulk., Comment: 8 page manuscript, 6 figures, 43 page SI