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This appendix volume supplements report NCEE 2025-005r, describing the effects of two experimental Pell Grant expansions: the first allowed income-eligible students with a bachelor's degree to obtain this form of federal financial aid for short-term occupational training programs and the second allowed income-eligible students to obtain this aid for very short-term programs lasting as little as eight weeks. The report updated earlier results from a rigorous evaluation of the experiments conducted by the U.S. Department of Education's (ED's) Institute of Education Sciences, adding new information about the experiments' effects on employment and earnings outcomes. This appendix volume similarly updates the evaluation's previous published appendix to detail the steps taken to assess these effects. The volume first describes the Pell Grant experiments as overseen by ED's Office of Federal Student Aid, including information about the schools offering the experimental Pell Grants. The next sections document how the study was conducted, including the research questions asked and the approach used to answer the research questions, additional details supporting the key findings of the study, and additional analyses to assess the sensitivity of the key findings.

Pell Grants are the cornerstone of federal financial aid for students with low income who are enrolled in postsecondary education. Currently, these grants are available only to those who seek an initial undergraduate degree or credential requiring at least a typical semester of instruction. Because these rules may restrict access to programs providing skills needed for new or better jobs, in 2011 the U.S. Department of Education (ED) began pilots of two experimental expansions to Pell Grant eligibility. The first experiment allowed income-eligible students with a bachelor's degree to obtain Pell Grants for short-term occupational training programs. The second experiment allowed income-eligible students to obtain Pell Grants for very short-term programs lasting as little as eight weeks. This report updates earlier results from a rigorous evaluation of the experiments conducted by ED's Institute of Education Sciences (IES), adding new information about the experiments' impacts on labor market success. This fuller picture could help Congress as it considers legislation to make Pell Grants for short-term occupational training permanent policy.

Paraeducators are among the largest categories of public education employees and are increasingly seen as a pool of potential teachers. However, little is known about paraeducator-to-teacher transitions. Using statewide administrative data, we show that while paraeducators may be more racially/ethnically diverse than the teacher workforce, Black and Hispanic paraeducators are less likely than White paraeducators to transition into teaching. We additionally show that teachers with paraeducator experience are similarly effective to teachers without paraeducator experience. Lastly, we use simulations to show that the potential for the paraeducator-to-teacher pipeline to diversify the teaching profession may be limited unless they are highly targeted. Our results have policy design implications for efforts to expand the paraeducator-to-teacher pipeline or to diversify the teacher workforce.

From October 2021, the government introduced a pilot in 30 local authorities to support 16 to 18-year-old children looked-after (CLA) and care leavers (CLs) in general further education (FE) colleges through the extension of Pupil Premium Plus funding to post-16 (PP+ Post-16). The 6-month pilot was completed between autumn 2021 and spring 20221 , before funding was extended to a further 28 local authorities in autumn 2022 and subsequently extended to all local authorities in England in autumn 2023. The purpose of PP+ Post-16 was also extended in 2023/24 to provide funding to support all CLA and CLs at post-16, rather than focusing on support for CLA and CLs in general FE colleges. This mixed methods evaluation is formative in intention and involves an exploratory study of the use of the funding by virtual schools (VS). It also considers early evidence about progress towards the outcomes in the Theory of Change (ToC), developed during the pilot evaluation and updated at the start of this evaluation through a series of ToC workshops with VSHs. The outcomes are arranged in the ToC under 3 headings, which are outcomes relating to young people, post-16 settings and joint working. This interim report presents findings from year 1 of the evaluation (2023/24) based on a national online survey of VSHs, case study interviews in 6 local authorities involving interviews with a range of stakeholders, and documentary analysis. [This report was produced with support from the Cordis Bright and the Rees Centre, University of Oxford.]

The path to becoming a school principal is characterized by a variety of trajectories that reflect the diverse experiences and backgrounds of aspiring leaders. While ideally the road to the principalship would result in a proportional and representative body of principals, research has shown this is rarely the case. To gain a better understanding of where sorting mechanisms may occur along the principal pipeline, this paper longitudinally analyzes the full, start-to-finish career trajectories of over 1.6 million educators in Texas for 30 years. Using social sequence analysis and discrete-time hazard modeling, we find that (1) emergent principals tend to stay in their first teaching position longer than other educators and most often take a direct pathway towards the principalship; (2) proportionally, more principals emerge from elementary, ELA, Social Studies, or STEM fields, while fewer come from Special Education; (3) holding other features constant, male and Black educators are more likely to become a principal while female and Hispanic educators are less likely; and (4) educators are more likely to first become principals when transitioning to a smaller school with more Black and/or Hispanic students. While the pipeline does result in a balanced principal market in some areas, increasing efforts to encourage a more diverse content area representation as well as representation for Hispanic educators in Texas will be particularly important.

The United States is facing growing teacher shortages that may disproportionately affecting schools serving high proportions of students of color, low-income students, and those in rural or urban areas. Special education teachers (SETs) are particularly in demand. Each year, nearly half of all vacancies are filled with teachers switching from one school to another, yet little research has addressed the nuances of within-career sorting, especially by subject. Utilizing longitudinal data covering 27 years and over 1.2 million teachers in Texas, this study examines SET switching patterns relative to core subject teachers, utilizing discrete time hazard modeling, fixed-effect regressions, and geographic information system mapping. Results show SETs switch schools at much higher rates, associated with experience, salary, and student demographics, yet generally transfer shorter distances than their peers. These findings highlight differential subject-specific labor market dynamics, suggesting targeted recruitment and retention strategies to address widespread shortages.

Catholic schools have seen more than a 30% decline in enrollment over the past 20 years. While some of the decline in enrollment may have been spurred by secular trends or the Church abuse scandal, the increase in schools of choice, principally public charter schools, may explain at least some of this decline. In this paper we estimate the effect of the opening of charter schools in proximity to Catholic schools across the entire U.S. We find that the opening of a nearby charter school has a negative impact on Catholic school enrollment and increases the likelihood that the school will close. We also find that charter openings induce greater racial isolation. Findings are especially pronounced in K8 schools, rather than high schools.

Ultrasound localization microscopy (ULM) enables microvascular imaging at spatial resolutions beyond the acoustic diffraction limit, offering significant clinical potentials. However, ULM performance relies heavily on microbubble (MB) signal sparsity, the number of detected MBs, and signal-to-noise ratio (SNR), all of which vary in clinical scenarios involving bolus MB injections. These sources of variations underscore the need to optimize MB dosage, data acquisition timing, and imaging settings in order to standardize and optimize ULM of microvasculature. This pilot study investigated temporal changes in MB signals during bolus injections in both pig and human models to optimize data acquisition for clinical ULM. Quantitative indices were developed to evaluate MB signal quality, guiding selection of acquisition timing that balances the MB localization quality and adequate MB counts. The effects of transmitted voltage and dosage were also explored. In the pig model, a relatively short window (approximately 10 seconds) for optimal acquisition was identified during the rapid wash-out phase, highlighting the need for real-time MB signal monitoring during data acquisition. The slower wash-out phase in humans allowed for a more flexible imaging window of 1-2 minutes, while trade-offs were observed between localization quality and MB density (or acquisition length) at different wash-out phase timings. Guided by these findings, robust ULM imaging was achieved in both pig and human kidneys using a short period of data acquisition, demonstrating its feasibility in clinical practice. This study provides insights into optimizing data acquisition for consistent and reproducible ULM, paving the way for its standardization and broader clinical applications., Comment: 33 pages, 9 figures

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

Study Objectives: We investigate using Mamba-based deep learning approaches for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a minimally intrusive dual-sensor wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and temperature, as well as finger photoplethysmography (PPG) and temperature. Methods: We obtained wearable sensor recordings from 360 adults undergoing concurrent clinical polysomnography (PSG) at a tertiary care sleep lab. PSG recordings were scored according to AASM criteria. PSG and wearable sensor data were automatically aligned using their ECG channels with manual confirmation by visual inspection. We trained Mamba-based models with both convolutional-recurrent neural network (CRNN) and the recurrent neural network (RNN) architectures on these recordings. Ensembling of model variants with similar architectures was performed. Results: Our best approach, after ensembling, attains a 3-class (wake, NREM, REM) balanced accuracy of 83.50%, F1 score of 84.16%, Cohen's $\kappa$ of 72.68%, and a MCC score of 72.84%; a 4-class (wake, N1/N2, N3, REM) balanced accuracy of 74.64%, F1 score of 74.56%, Cohen's $\kappa$ of 61.63%, and MCC score of 62.04%; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 64.30%, F1 score of 66.97%, Cohen's $\kappa$ of 53.23%, MCC score of 54.38%. Conclusions: Deep learning models can infer major sleep stages from a wearable system without electroencephalography (EEG) and can be successfully applied to data from adults attending a tertiary care sleep clinic., Comment: 21 pages, 11 figures. Authors Andrew H. Zhang, Alex He-Mo, and Richard Fei Yin contributed equally

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

Quantum error correction is essential for bridging the gap between the error rates of physical devices and the extremely low logical error rates required for quantum algorithms. Recent error-correction demonstrations on superconducting processors have focused primarily on the surface code, which offers a high error threshold but poses limitations for logical operations. In contrast, the color code enables much more efficient logic, although it requires more complex stabilizer measurements and decoding techniques. Measuring these stabilizers in planar architectures such as superconducting qubits is challenging, and so far, realizations of color codes have not addressed performance scaling with code size on any platform. Here, we present a comprehensive demonstration of the color code on a superconducting processor, achieving logical error suppression and performing logical operations. Scaling the code distance from three to five suppresses logical errors by a factor of $\Lambda_{3/5}$ = 1.56(4). Simulations indicate this performance is below the threshold of the color code, and furthermore that the color code may be more efficient than the surface code with modest device improvements. Using logical randomized benchmarking, we find that transversal Clifford gates add an error of only 0.0027(3), which is substantially less than the error of an idling error correction cycle. We inject magic states, a key resource for universal computation, achieving fidelities exceeding 99% with post-selection (retaining about 75% of the data). Finally, we successfully teleport logical states between distance-three color codes using lattice surgery, with teleported state fidelities between 86.5(1)% and 90.7(1)%. This work establishes the color code as a compelling research direction to realize fault-tolerant quantum computation on superconducting processors in the near future.

Recently, we have witnessed the rise of novel ``event-based'' camera sensors for high-speed, low-power video capture. Rather than recording discrete image frames, these sensors output asynchronous ``event'' tuples with microsecond precision, only when the brightness change of a given pixel exceeds a certain threshold. Although these sensors have enabled compelling new computer vision applications, these applications often require expensive, power-hungry GPU systems, rendering them incompatible for deployment on the low-power devices for which event cameras are optimized. Whereas receiver-driven rate adaptation is a crucial feature of modern video streaming solutions, this topic is underexplored in the realm of event-based vision systems. On a real-world event camera dataset, we first demonstrate that a state-of-the-art object detection application is resilient to dramatic data loss, and that this loss may be weighted towards the end of each temporal window. We then propose a scalable streaming method for event-based data based on Media Over QUIC, prioritizing object detection performance and low latency. The application server can receive complementary event data across several streams simultaneously, and drop streams as needed to maintain a certain latency. With a latency target of 5 ms for end-to-end transmission across a small network, we observe an average reduction in detection mAP as low as 0.36. With a more relaxed latency target of 50 ms, we observe an average mAP reduction as low as 0.19.

We investigate nonclassical Bethe - Zel'dovich-Thompson (BZT) rarefaction shocks and the QCD phase transition in the dense core of a cold quark star in beta equilibrium subject to the multicomponent van der Waals (MvdW) equation of state (EoS) as a model of internal structure. When this system is expressed in terms of multiple components it can be used to explore the impact of a phase transition from a hadronic state to a quark plasma state with a complex clustering structure. The clustering can take the form of colored diquarks or triquarks and bound colorless meson, baryon, or hyperon states at the phase transition boundary. The resulting multicomponent EoS system is nonconvex which can give rise to nonclassical BZT phase changing shock waves. Using the BZT shock wave condition we find constraints on the quark density and examine how this changes the compactness and tidal deformability of the compact core. These results are then combined with the TOV equations to find the changes in the mass-radius relationship. These states are compared to the recent astrophysical high mass neutron star systems which may provide evidence for a core that has undergone a quark-gluon phase transition such as PSR 0943+10 or GW 190814., Comment: 19 pages, 11 figures, 2 tables

Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned reconstruction methods, including improved resilience to different scanner setups and advanced image distribution modeling. SGM-based reconstruction has recently been applied to simulated positron emission tomography (PET) datasets, showing improved contrast recovery for out-of-distribution lesions relative to the state-of-the-art. However, existing methods for SGM-based reconstruction from PET data suffer from slow reconstruction, burdensome hyperparameter tuning and slice inconsistency effects (in 3D). In this work, we propose a practical methodology for fully 3D reconstruction that accelerates reconstruction and reduces the number of critical hyperparameters by matching the likelihood of an SGM's reverse diffusion process to a current iterate of the maximum-likelihood expectation maximization algorithm. Using the example of low-count reconstruction from simulated $[^{18}$F]DPA-714 datasets, we show our methodology can match or improve on the NRMSE and SSIM of existing state-of-the-art SGM-based PET reconstruction while reducing reconstruction time and the need for hyperparameter tuning. We evaluate our methodology against state-of-the-art supervised and conventional reconstruction algorithms. Finally, we demonstrate a first-ever implementation of SGM-based reconstruction for real 3D PET data, specifically $[^{18}$F]DPA-714 data, where we integrate perpendicular pre-trained SGMs to eliminate slice inconsistency issues., Comment: 11 pages, 12 figures. Submitted to Transactions on Medical Imaging

Large high-quality medical image datasets are difficult to acquire but necessary for many deep learning applications. For positron emission tomography (PET), reconstructed image quality is limited by inherent Poisson noise. We propose a novel method for synthesising diverse and realistic pseudo-PET images with improved signal-to-noise ratio. We also show how our pseudo-PET images may be exploited as a generative prior for single-subject PET image reconstruction. Firstly, we perform deep-learned deformable registration of multi-subject magnetic resonance (MR) images paired to multi-subject PET images. We then use the anatomically-learned deformation fields to transform multiple PET images to the same reference space, before averaging random subsets of the transformed multi-subject data to form a large number of varying pseudo-PET images. We observe that using MR information for registration imbues the resulting pseudo-PET images with improved anatomical detail compared to the originals. We consider applications to PET image reconstruction, by generating pseudo-PET images in the same space as the intended single-subject reconstruction and using them as training data for a diffusion model-based reconstruction method. We show visual improvement and reduced background noise in our 2D reconstructions as compared to OSEM, MAP-EM and an existing state-of-the-art diffusion model-based approach. Our method shows the potential for utilising highly subject-specific prior information within a generative reconstruction framework. Future work may compare the benefits of our approach to explicitly MR-guided reconstruction methodologies., Comment: 2 pages, 3 figures. Accepted as a poster presentation at IEEE NSS MIC RTSD 2024 (submitted May 2024; accepted July 2024; presented Nov 2024)

Score-based generative models (SGMs) have recently shown promising results for image reconstruction on simulated positron emission tomography (PET) datasets. In this work we have developed and implemented practical methodology for 3D image reconstruction with SGMs, and perform (to our knowledge) the first SGM-based reconstruction of real fully 3D PET data. We train an SGM on full-count reference brain images, and extend methodology to allow SGM-based reconstructions at very low counts (1% of original, to simulate low-dose or short-duration scanning). We then perform reconstructions for multiple independent realisations of 1% count data, allowing us to analyse the bias and variance characteristics of the method. We sample from the learned posterior distribution of the generative algorithm to calculate uncertainty images for our reconstructions. We evaluate the method's performance on real full- and low-count PET data and compare with conventional OSEM and MAP-EM baselines, showing that our SGM-based low-count reconstructions match full-dose reconstructions more closely and in a bias-variance trade-off comparison, our SGM-reconstructed images have lower variance than existing baselines. Future work will compare to supervised deep-learned methods, with other avenues for investigation including how data conditioning affects the SGM's posterior distribution and the algorithm's performance with different tracers., Comment: 2 pages, 2 figures. Accepted for oral presentation at IEEE NSS MIC RTSD 2024 (submitted May 2024; accepted July 2024; presented Nov 2024)

We introduce the Bias-free Extragalactic Analysis for Cosmic Origins with NIRCam (BEACON) survey, a JWST Cycle2 program allocated up to 600 pure-parallel hours of observations. BEACON explores high-latitude areas of the sky with JWST/NIRCam over $\sim100$ independent sightlines, totaling $\sim0.3$deg$^2$, reaching a median F444W depth of $\approx28.2$AB mag (5$\sigma$). Based on existing JWST observations in legacy fields, we estimate that BEACON will photometrically identify 25--150 galaxies at $z>10$ and 500--1000 at $z\sim7$--10 uniquely enabled by an efficient multiple filter configuration spanning $0.9$--5.0$\mu$m. The expected sample size of $z>10$ galaxies will allow us to obtain robust number density estimates and to discriminate between different models of early star formation. In this paper, we present an overview of the survey design and initial results using the first 19 fields. We present 129 galaxy candidates at $z>7$ identified in those fields, including 11 galaxies at $z>10$ and several UV-luminous ($M_{\rm UV}<-21$mag) galaxies at $z\sim8$. The number densities of $z<13$ galaxies inferred from the initial fields are overall consistent with those in the literature. Despite reaching a considerably large volume ($\sim10^5$Mpc$^3$), however, we find no galaxy candidates at $z>13$, providing us with a complimentary insight into early galaxy evolution with minimal cosmic variance. We publish imaging and catalog data products for these initial fields. Upon survey completion, all BEACON data will be coherently processed and distributed to the community along with catalogs for redshift and other physical quantities., Comment: Submitted to ApJ; DR1 data release will be made on the team website (https://beacon-jwst.github.io); Fig. 8 has been updated

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources., Comment: 26 pages, 5 figures

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation., Comment: The code is accessible at https://github.com/mahmoodlab/TITAN

Astronomers have found more than a dozen planets transiting 10-40 million year old stars, but even younger transiting planets have remained elusive. A possible reason for the lack of such discoveries is that newly formed planets are not yet in a configuration that would be recognized as a transiting planet or cannot exhibit transits because our view is blocked by a protoplanetary disk. However, we now know that many outer disks are warped; provided the inner disk is depleted, transiting planets may thus be visible. Here we report the observations of the transiting planet IRAS 04125+2902 b orbiting a 3 Myr, 0.7 M$_\odot$, pre-main sequence star in the Taurus Molecular Cloud. IRAS 04125+2902 hosts a nearly face-on (i $\sim$ 30$^\circ$) transitional disk and a wide binary companion. The planet has a period of 8.83 days, a radius of 10.9 R$_\oplus$ (0.97R$_J$), and a 95%-confidence upper limit on its mass of 90M$_\oplus$ (0.3M$_J$) from radial velocity measurements, making it a possible precursor of the super-Earths and sub-Neptunes that are commonly found around main-sequence stars. The rotational broadening of the star and the orbit of the wide (4", 635 AU) companion are both consistent with edge-on orientations. Thus, all components of the system appear to be aligned except the outer disk; the origin of this misalignment is unclear. Given the rare set of circumstances required to detect a transiting planet at ages when the disk is still present, IRAS 04125+2902 b likely provides a unique window into sub-Neptunes immediately following formation., Comment: Initial version submitted to Nature. Stellar, and hence planetary, parameters slightly differ from final version. Published version available at https://www.nature.com/articles/s41586-024-08123-3

Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models in downstream tasks like classification and segmentation. Materials and Methods: We utilized a latent diffusion model to condition the generation of synthetic chest X-rays on text prompts and/or segmentation masks. We explored methods like using a proxy model and using radiologist feedback to improve the quality of synthetic data. These synthetic images were then generated from relevant disease information or geometrically transformed segmentation masks and added to ground truth training set images from the CheXpert, CANDID-PTX, SIIM, and RSNA Pneumonia datasets to measure improvements in classification and segmentation model performance on the test sets. F1 and Dice scores were used to evaluate classification and segmentation respectively. One-tailed t-tests with Bonferroni correction assessed the statistical significance of performance improvements with synthetic data. Results: Across all experiments, the synthetic data we generated resulted in a maximum mean classification F1 score improvement of 0.150453 (CI: 0.099108-0.201798; P=0.0031) compared to using only real data. For segmentation, the maximum Dice score improvement was 0.14575 (CI: 0.108267-0.183233; P=0.0064). Conclusion: Best practices for generating synthetic chest X-ray images for downstream tasks include conditioning on single-disease labels or geometrically transformed segmentation masks, as well as potentially using proxy modeling for fine-tuning such generations.

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

We report on the heterostructure and interfacial engineering of metalorganic chemical vapor deposition (MOCVD) grown reverse-graded contacts to ultra-wide bandgap AlGaN. A record low contact resistivity of 1.4 x 10-6 Ohm.cm2 was reported on an Al0.82Ga0.18N metal semiconductor field effect transistor (MESFET) by compositionally grading the contact layer from Al0.85Ga0.15N to Al0.14Ga0.86N with degenerate doping and proper interfacial engineering considering bandgap-narrowing-induced band offset between channel and contact layer. This represents orders-of-magnitude of lower contact resistivity than that obtained in similar MOCVD-grown structures. A detailed, layer-by-layer analysis of the reverse graded contact and TCAD simulation of the bandgap narrowing effect highlighted that the reverse graded contact layer itself is extremely conductive and interfacial resistance due to bandgap-narrowing-induced barrier between contact and channel dominates the contact resistance.

As globular clusters (GCs) orbit the Milky Way, their stars are tidally stripped forming tidal tails that follow the orbit of the clusters around the Galaxy. The morphology of these tails is complex and shows correlations with the phase of the orbit and the orbital angular velocity, especially for GCs on eccentric orbits. Here, we focus on two GCs, NGC 1261 and NGC 1904, that have potentially been accreted alongside Gaia-Enceladus and that have shown signatures of having, in addition of tidal tails, structures formed by distributions of extra-tidal stars that are misaligned with the general direction of the clusters' respective orbits. To provide an explanation for the formation of these structures, we make use of spectroscopic measurements from the Southern Stellar Stream Spectroscopic Survey ($S^5$) as well as proper motion measurements from Gaia's third data release (DR3), and apply a Bayesian mixture modeling approach to isolate high-probability member stars. We recover extra-tidal features similar to those found in Shipp et al. (2018) surrounding each cluster. We conduct N-body simulations and compare the expected distribution and variation in the dynamical parameters along the orbit with those of our potential member sample. Furthermore, we use Dark Energy Camera (DECam) photometry to inspect the distribution of the member stars in the color-magnitude diagram (CMD). We find that the potential members agree reasonably with the N-body simulations and that the majority of them follow a simple stellar population-like distribution in the CMD which is characteristic of GCs. In the case of NGC 1904, we clearly detect the tidal debris escaping the inner and outer Lagrange points which are expected to be prominent when at or close to the apocenter of its orbit. Our analysis allows for further exploration of other GCs in the Milky Way that exhibit similar extra-tidal features.

We present the discovery of TOI-6303b and TOI-6330b, two massive transiting super-Jupiters orbiting a M0 and a M2 star respectively, as part of the Searching for GEMS survey. These were detected by TESS and then confirmed via ground-based photometry and radial velocity observations with the Habitable-zone Planet Finder (HPF). TOI-6303b has a mass of 7.84 +/- 0.31 MJ, a radius of 1.03 +/- 0.06 RJ , and an orbital period of 9.485 days. TOI-6330b has a mass of 10.00 +/- 0.31 MJ , a radius of 0.97 +/- 0.03 RJ , and an orbital period of 6.850 days. We put these planets in context of super-Jupiters around M-dwarfs discovered from radial-velocity surveys, as well as recent discoveries from astrometry. These planets have masses that can be attributed to two dominant planet formation mechanisms - gravitational instability and core-accretion. Their masses necessitate massive protoplanetary disks that should either be gravitationally unstable, i.e. forming through gravitational instability, or be amongst some of the most massive protoplanetary disks to form objects through core-accretion. We also discuss the eccentricity distribution of these objects, as a potential indicator of their formation and evolutionary mechanisms., Comment: arXiv admin note: text overlap with arXiv:2408.14694

Terrestrial experiments that use electrons in Earth as a spin-polarized source have been demonstrated to provide strong bounds on exotic long-range spin-spin and spin-velocity interactions. These bounds constrain the coupling strength of many proposed ultralight bosonic dark-matter candidates. Recently, it was pointed out that a monopole-dipole coupling between the Sun and the spin-polarized electrons of Earth would result in a modification of the precession of the perihelion of Earth. Using an estimate for the net spin-polarization of Earth and experimental bounds on Earth's perihelion precession, interesting constraints were placed on the magnitude of this monopole-dipole coupling. Here we investigate the spin associated with Earth's electrons. We find that there are about $6 \times 10^{41}$ spin-polarized electrons in the mantle and crust of Earth oriented anti-parallel to their local magnetic field. However, when integrated over any spherically-symmetric Earth model, we find that the vector sum of these spins is zero. In order to establish a lower bound on the magnitude of the net spin along Earth's rotation axis we have investigated three of the largest breakdowns of Earth's spherical symmetry: the large low shear-velocity provinces of the mantle, the crustal composition, and the oblate spheroid of Earth. From these investigations we conclude that there are at least $5 \times 10^{38}$ spin-polarized electrons aligned anti-parallel to Earth's rotation axis. This analysis suggests that the bounds on the monopole-dipole coupling that were extracted from Earth's perihelion precession need to be relaxed by a factor of about 2000., Comment: 10 pages, 1 figure, 1 table

The Quantum Gravity Mediated Entanglement (QGEM) protocol offers a novel method to probe the quantumness of gravitational interactions at non-relativistic scales. This protocol leverages the Stern-Gerlach effect to create $\mathcal{O}(\sim \mu m)$ spatial superpositions of two nanodiamonds (mass $\sim 10^{-15}$ kg) with NV spins, which are then allowed to interact and become entangled solely through the gravitational interaction. Since electromagnetic interactions such as Casimir-Polder and dipole-dipole interactions dominate at this scale, screening them to ensure the masses interact exclusively via gravity is crucial. In this paper, we propose using magnetic traps based on micro-fabricated wires, which provide strong gradients with relatively modest magnetic fields to trap nanoparticles for interferometric entanglement experiments. The design consists of a small trap to cool the center-of-mass motion of the nanodiamonds and a long trap with a weak direction suitable for creating macroscopic superpositions. In contrast to permanent-magnet-based long traps, the micro-fabricated wire-based approach allows fast switching of the magnetic trapping and state manipulation potentials and permits integrated superconducting shielding, which can screen both electrostatic and magnetic interactions between nanodiamonds in a gravitational entanglement experiment. The setup also provides a possible platform for other tests of quantum coherence in macroscopic systems and searches for novel short-range forces., Comment: 11 pages, 8 figures

The purpose of the addendum is to document the findings from the Year 2 student achievement analyses of a teacher-level randomized experiment, which was designed to examine the implementation and impact of a scalable version of the 2-year MyTeachingPartner Secondary (MTP-S) program as part of a federally-funded Education Innovation and Research (EIR) project. Due to COVID-caused delays, we were unable to include findings about the impact of MTP-S on Year 2 student achievement in the final report produced at the end of the EIR project (Wayne et al., 2023). With the support of a grant from the William T. Grant Foundation, we were able to complete the collection and analyses of Year 2 student achievement data presented in this addendum. Similar to the Year 1 student achievement findings presented in our EIR final project report, the Year 2 student achievement findings did not reveal any significant program impact on students' math or English language arts achievement at the end of the 2-year program. Findings from this study need to be interpreted with caution given study limitations resulting largely from the influence of the pandemic.

The four-day school week is a school calendar that has become increasingly common following the COVID-19 pandemic. Proponents of the calendar often claim that offering teachers a regular 3-day weekend will help schools better retain existing teachers and recruit new teachers to their district without incurring additional costs due to higher salaries or other pecuniary benefits. However, there is scant empirical evidence assessing these claims. I use difference-in-differences and synthetic difference-in-differences models to assess the impact of four-day school week calendars on teacher retention and teacher quality in Arkansas. I find evidence that the calendar may help retain teachers who otherwise would have moved to another school and suggestive evidence that retention in non-adopting schools may be harmed by the four-day school week adoption in nearby districts. Results examining changes in teacher quality are inconclusive. These results have significant implications given the rapid growth in four-day school week calendars in recent years.

Attracting and retaining high-quality teachers is a pressing policy concern. Increasing teacher salaries and creating more attractive compensation packages are often proposed as a potential solution. Signed into law in March 2023, the LEARNS Act increased Arkansas's minimum teacher salary from $36,000 to $50,000, guaranteed all teachers a minimum raise of $2,000, and added flexibility allowing school districts to deviate from seniority-based traditional salary schedules. To study school districts' adjustments to the new legislation, we collected information about districts' teacher compensation policies one year before and the first year of implementation. We also integrated this data with teachers' administrative records to study patterns of teacher retention and mobility. Our results reveal a more equitable distribution of starting teacher salaries across districts, with minimal variation. The LEARNS Act notably increased funding for rural and high-poverty districts, mitigating the negative association between starting salaries and district poverty rates. However, the initial effects on teacher retention and mobility were modest. While some positive trends emerged, such as reduced probabilities of teachers transitioning to non-instructional roles and increased new teacher placement in geographic areas of shortage, broader impacts on retention and mobility were limited in the first year of implementation.

A later school start time policy has been recommended as a solution to adolescents' sleep deprivation. We estimated the impacts of later school start times on adolescents' sleep and substance use by leveraging a quasi-experiment in which school start time was delayed in some regions in South Korea. A later school start time policy was implemented in 2014 and 2015, which delayed school start times approximately 30-90 minutes. We applied difference-in-differences and event-study designs to longitudinal data on a nationally-representative cohort of adolescents from 2010 to 2015, which annually tracked sleep and substance use of 1,133 adolescents from grade 7 through grade 12. The adoption of a later school start time policy was initially associated with a 19-minute increase in sleep duration (95% CI, 5.52 to 32.04), driven by a delayed wake time and consistent bedtime. The policy was also associated with statistically significant reductions in monthly smoking and drinking frequencies. However, approximately a year after implementation, the observed increase in sleep duration shrank to 7-minute (95% CI, -12.60 to 25.86) and became statistically nonsignificant. Similarly, the observed reduction in smoking and drinking was attenuated a year after. Our findings suggest that policies that increase sleep in adolescents may have positive effects on health behaviors, but additional efforts may be required to sustain positive impacts over time. Physicians and education and health policymakers should consider the long-term effects of later school start times on adolescent health and well-being.

We use student-level data on elementary special education identification from Washington state to explore student identification rates in the months immediately after the onset of the COVID-19 pandemic and over 2 subsequent years. Special education identification rates dropped dramatically in March 2020 through the end of the 2019-20 school year and remained below historical norms through 2020-21 before returning to pre-pandemic levels early in 2021-22. The magnitude of these effects implies that over 8,000 fewer elementary students were identified for special education services during the pandemic in Washington than would have been expected based on prior trends.

There has been a paradigm shift from accommodations to the identification of levels of accessibility (e.g., universal features, designated features, and accommodations). A consequence of this shift is that many students with disabilities who previously were assigned accommodations now access many of them as universal or designated features. This Brief highlights what states and Individualized Education Program (IEP) teams can do to avoid possible unintended consequences of the paradigm shift.

The vision of adaptive architecture proposes that robotic technologies could enable interior spaces to physically transform in a bidirectional interaction with occupants. Yet, it is still unknown how this interaction could unfold in an understandable way. Inspired by HRI studies where robotic furniture gestured intents to occupants by deliberately positioning or moving in space, we hypothesise that adaptive architecture could also convey intents through gestures performed by a mobile robotic partition. To explore this design space, we invited 15 multidisciplinary experts to join co-design improvisation sessions, where they manually manoeuvred a deactivated robotic partition to design gestures conveying six architectural intents that varied in purpose and urgency. Using a gesture elicitation method alongside motion-tracking data, a Laban-based questionnaire, and thematic analysis, we identified 20 unique gestural strategies. Through categorisation, we introduced architectonic gestures as a novel strategy for robotic furniture to convey intent by indexically leveraging its spatial impact, complementing the established deictic and emblematic gestures. Our study thus represents an exploratory step toward making the autonomous gestures of adaptive architecture more legible. By understanding how robotic gestures are interpreted based not only on their motion but also on their spatial impact, we contribute to bridging HRI with Human-Building Interaction research.

Time-varying random objects have been increasingly encountered in modern data analysis. Moreover, in a substantial number of these applications, periodic behavior of the random objects has been observed. We introduce a new, powerful scan statistic and corresponding test for the precise identification and localization of abrupt changes in the distribution of non-Euclidean random objects with possibly periodic behavior. Our approach is nonparametric and effectively captures the entire distribution of these random objects. Remarkably, it operates with minimal tuning parameters, requiring only the specification of cut-off intervals near endpoints, where change points are assumed not to occur. Our theoretical contributions include deriving the asymptotic distribution of the test statistic under the null hypothesis of no change points, establishing the consistency of the test in the presence of change points under contiguous alternatives and providing rigorous guarantees on the near-optimal consistency in estimating the number and locations of change points, whether dealing with a single change point or multiple ones. We demonstrate that the most competitive method currently in the literature for change point detection in random objects is degraded by periodic behavior, as periodicity leads to blurring of the changes that this procedure aims to discover. Through comprehensive simulation studies, we demonstrate the superior power and accuracy of our approach in both detecting change points and pinpointing their locations, across scenarios involving both periodic and nonperiodic random objects. Our main application is to weighted networks, represented through graph Laplacians. The proposed method delivers highly interpretable results, as evidenced by the identification of meaningful change points in the New York City Citi Bike sharing system that align with significant historical events., Comment: arXiv admin note: text overlap with arXiv:2311.16025 by other authors

We introduce QuArch, a dataset of 1500 human-validated question-answer pairs designed to evaluate and enhance language models' understanding of computer architecture. The dataset covers areas including processor design, memory systems, and performance optimization. Our analysis highlights a significant performance gap: the best closed-source model achieves 84% accuracy, while the top small open-source model reaches 72%. We observe notable struggles in memory systems, interconnection networks, and benchmarking. Fine-tuning with QuArch improves small model accuracy by up to 8%, establishing a foundation for advancing AI-driven computer architecture research. The dataset and leaderboard are at https://harvard-edge.github.io/QuArch/.

Recent discoveries from time-domain surveys are defying our expectations for how matter accretes onto supermassive black holes (SMBHs). The increased rate of short-timescale, repetitive events around SMBHs, including the newly-discovered quasi-periodic eruptions (QPEs), are garnering further interest in stellar-mass companions around SMBHs and the progenitors to mHz frequency gravitational wave events. Here we report the discovery of a highly significant mHz Quasi-Periodic Oscillation (QPO) in an actively accreting SMBH, 1ES 1927+654, which underwent a major optical, UV, and X-ray outburst beginning in 2018. The QPO was first detected in 2022 with a roughly 18-minute period, corresponding to coherent motion on scales of less than 10 gravitational radii, much closer to the SMBH than typical QPEs. The period decreased to 7.1 minutes over two years with a decelerating period evolution ($\ddot{P} > 0$). This evolution has never been seen in SMBH QPOs or high-frequency QPOs in stellar mass black holes. Models invoking orbital decay of a stellar-mass companion struggle to explain the period evolution without stable mass transfer to offset angular momentum losses, while the lack of a direct analog to stellar mass black hole QPOs means that many instability models cannot explain all of the observed properties of the QPO in 1ES 1927+654. Future X-ray monitoring will test these models, and if it is a stellar-mass orbiter, the Laser Interferometer Space Antenna (LISA) should detect its low-frequency gravitational wave emission., Comment: Accepted in Nature

The goal of this paper is speech separation and enhancement in multi-speaker and noisy environments using a combination of different modalities. Previous works have shown good performance when conditioning on temporal or static visual evidence such as synchronised lip movements or face identity. In this paper, we present a unified framework for multi-modal speech separation and enhancement based on synchronous or asynchronous cues. To that end we make the following contributions: (i) we design a modern Transformer-based architecture tailored to fuse different modalities to solve the speech separation task in the raw waveform domain; (ii) we propose conditioning on the textual content of a sentence alone or in combination with visual information; (iii) we demonstrate the robustness of our model to audio-visual synchronisation offsets; and, (iv) we obtain state-of-the-art performance on the well-established benchmark datasets LRS2 and LRS3.

White dwarfs (WDs) are the most abundant compact objects, and recent surveys have suggested that over a third of WDs in accreting binaries host a strong (B $\gtrsim$ 1 MG) magnetic field. However, the origin and evolution of WD magnetism remain under debate. Two WD pulsars, AR Sco and J191213.72-441045.1 (J1912), have been found, which are non-accreting binaries hosting rapidly spinning (1.97-min and 5.30-min, respectively) magnetic WDs. The WD in AR Sco is slowing down on a $P/\dot{P}\approx 5.6\times 10^6$ yr timescale. It is believed they will eventually become polars, accreting systems in which a magnetic WD (B $\approx 10-240$ MG) accretes from a Roche lobe-filling donor spinning in sync with the orbit ($\gtrsim 78$ min). Here, we present multiwavelength data and analysis of Gaia22ayj, which outbursted in March 2022. We find that Gaia22ayj is a magnetic accreting WD that is rapidly spinning down ($P/\dot{P} = 6.1^{+0.3}_{-0.2}\times 10^6$ yr) like WD pulsars, but shows clear evidence of accretion, like polars. Strong linear polarization (40%) is detected in Gaia22ayj; such high levels have only been seen in the WD pulsar AR Sco and demonstrate the WD is magnetic. High speed photometry reveals a 9.36-min period accompanying a high amplitude ($\sim 2$ mag) modulation. We associate this with a WD spin or spin-orbit beat period, not an orbital period as was previously suggested. Fast (60-s) optical spectroscopy reveals a broad ``hump'', reminiscent of cyclotron emission in polars, between 4000-8000 Angstrom. We find an X-ray luminosity of $L_X = 2.7_{-0.8}^{+6.2}\times10^{32} \textrm{ erg s}^{-1}$ in the 0.3-8 keV energy range, while two VLA radio campaigns resulted in a non-detection with a $F_r < 15.8\mu\textrm{Jy}$ 3$ \sigma$ upper limit. The shared properties of both WD pulsars and polars suggest that Gaia22ayj is a missing link between the two classes of magnetic WD binaries., Comment: Submitted to PASP; comments welcome

We present a detailed study of SN 2024ahr, a hydrogen-poor superluminous supernova (SLSN-I), for which we determine a redshift of $z=0.0861$. SN 2024ahr has a peak absolute magnitude of $M_g\approx M_r\approx -21$ mag, rest-frame rise and decline times (50$\%$ of peak) of about 40 and 80 days, respectively, and typical spectroscopic evolution in the optical band. Similarly, modeling of the UV/optical light curves with a magnetar spin-down engine leads to typical parameters: an initial spin period of $\approx 3.3$ ms, a magnetic field strength of $\approx 6\times 10^{13}$ G, and an ejecta mass of $\approx 9.5$ M$_\odot$. Due to its relatively low redshift we obtained a high signal-to-noise ratio near-IR spectrum about 43 rest-frame days post-peak to search for the presence of helium. We do not detect any significant feature at the location of the He I $\,\lambda 2.058$ $\mu$m feature, and place a conservative upper limit of $\sim 0.05$ M$_\odot$ on the mass of helium in the outer ejecta. We detect broad features of Mg I $\,\lambda 1.575$ $\mu$m and a blend of Co II $\,\lambda 2.126$ $\mu$m and Mg II, $\lambda 2.136$ $\mu$m, which are typical of Type Ic SNe, but with higher velocities. Examining the sample of SLSNe-I with NIR spectroscopy, we find that, unlike SN 2024ahr, these events are generally peculiar. This highlights the need for a large sample of prototypical SLSNe-I with NIR spectroscopy to constrain the fraction of progenitors with helium (Ib-like) and without helium (Ic-like) at the time of the explosion, and hence the evolutionary path(s) leading to the rare outcome of SLSNe-I., Comment: 20 pages, 7 figures

We present a transformer-based architecture for voice separation of a target speaker from multiple other speakers and ambient noise. We achieve this by using two separate neural networks: (A) An enrolment network designed to craft speaker-specific embeddings, exploiting various combinations of audio and visual modalities; and (B) A separation network that accepts both the noisy signal and enrolment vectors as inputs, outputting the clean signal of the target speaker. The novelties are: (i) the enrolment vector can be produced from: audio only, audio-visual data (using lip movements) or visual data alone (using lip movements from silent video); and (ii) the flexibility in conditioning the separation on multiple positive and negative enrolment vectors. We compare with previous methods and obtain superior performance.

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition., Comment: Palmprint recognition, biometrics, deep learning, feature extraction, recognition tasks

Azimuthal structures emerging in beam-generated partially magnetized E $\times$ B plasmas are investigated using three-dimensional particle-in-cell simulations. Two distinct instability regimes are identified at low pressures. When the gas pressure is sufficiently high, quasi-neutrality is attained and 2D spiral-arm structures form as a result of the development of a lower-hybrid instability, resulting in enhanced cross-field transport. At lower pressures, quasi-neutrality is not achieved and a 3D helical-rotating plasma structure forms due to development of the diocotron instability. Analytical formulas are proposed for the critical threshold pressure between these regimes and for the rotation frequency of the helical structures.

The high-redshift UV luminosity function provides important insights into the evolution of early galaxies. JWST has revealed an unexpectedly large population of bright ($M_\mathrm{UV} \lesssim -20$) galaxies at $z\gtrsim10$, implying fundamental changes in the star forming properties of galaxies at increasingly early times. However, constraining the fainter population ($M_\mathrm{UV} \gtrsim -18$) has been more challenging. In this work, we present the $z\gtrsim9$ UV luminosity function from the JWST Advanced Deep Extragalactic Survey. We calculate the UV luminosity function from several hundred $z\gtrsim9$ galaxy candidates that reach UV luminosities of $M_\mathrm{UV}\sim-17$ in redshift bins of $z\sim9-12$ (309 candidates) and $z\sim12-16$ (63 candidates). We search for candidates at $z\sim16-22.5$ and find none. We also estimate the $z\sim14-16$ luminosity function from the $z\geq14$ subset of the $z\sim12-16$ sample. Consistent with other measurements, we find an excess of bright galaxies that is in tension with many theoretical models, especially at $z\gtrsim12$. However, we also find high number densities at $-18\lesssim M_\mathrm{UV} \lesssim-17$, suggesting that there is a larger population of faint galaxies than expected, as well as bright ones. From our parametric fits for the luminosity function, we find steep faint end slopes of $-2.5\lesssim\alpha\lesssim-2.3$, suggesting a large population of faint ($M_\mathrm{UV} \gtrsim -17$) galaxies. Combined, the high normalization and steep faint end slope of the luminosity function could imply that the reionization process is appreciably underway as early as $z=10$., Comment: 32 pages (27 pages of main text), 12 figures, 6 tables

The decay $B \to \rho \ell \bar{\nu}$ is an attractive process for determining the magnitude of the smallest CKM matrix element, $|V_{ub}|$, and can provide new insights into the origin of the long-standing exclusive-inclusive discrepancy in determinations of this Standard-Model parameter. This requires a nonperturbative QCD calculation of the $B \to \rho$ form factors $V$, $A_0$, $A_1$, and $A_{12}$. The unstable nature of the $\rho$ resonance has prevented precise lattice QCD calculations of these form factors to date. Here, we present the first lattice QCD calculation of the $B \to \rho$ form factors in which the $\rho$ is treated properly as a resonance in $P$-wave $\pi \pi$ scattering. To this end, we use the Lellouch-L\"uscher finite-volume formalism to compute the $B \to \pi \pi$ form factors as a function of both momentum transfer and $\pi \pi$ invariant mass, and then analytically continue to the $\rho$ resonance pole. This calculation is performed with $2+1$ dynamical quark flavors at a pion mass of approximately 320 MeV, and demonstrates a clear path toward results at the physical point., Comment: 8 pages, 2 figures, ancillary files

Studies have indicated that personality is related to achievement, and several personality assessment models have been developed. However, most are either questionnaires or based on marker systems, which entails limitations. We proposed a physiological signal based model, thereby ensuring the objectivity of the data and preventing unreliable responses. Thirty participants were recruited from the Department of Electrical Engineering of Yuan Ze University in Taiwan. Wearable sensors were used to collect physiological signals as the participants watched and summarized a video. They then completed a personality questionnaire based on the big five factor markers system. The results were used to construct a personality prediction model, which revealed that galvanic skin response and heart rate variance were key factors predicting extroversion; heart rate variance also predicted agreeableness and conscientiousness. The results of this experiment can elucidate students personality traits, which can help educators select the appropriate pedagogical methods.

We characterize the atmosphere of the hot super-Neptune WASP-166b ($P = 5.44$ d, $R_p = 6.9 \pm 0.3$ R$_\oplus$, $M_p = 32.1 \pm 1.6$ M$_\oplus$, $T_\mathrm{eq} = 1270 \pm 30$ K) orbiting an F9V star using JWST transmission spectroscopy observations obtained with NIRISS SOSS Order-1 and NIRSpec BOTS G395M/F290LP. Our combined spectrum spans wavelengths $0.85$ to $5.17$ $\mu$m (GO ID 2062, PI: Mayo). WASP-166b resides near the edge of the Hot Neptune Desert, a scarcity of intermediate-sized planets at high insolation fluxes; thus, exploring the atmospheric composition and formation processes of WASP-166b can provide insights into the mechanisms sculpting this parameter space. Our POSEIDON free chemistry retrievals confirm the detection of H$_2$O ($15.2\sigma$ significance) and detect CO$_2$ ($14.7\sigma$) for the first time in the planet atmosphere. We also find a hint of NH$_3$ ($2.3\sigma$) and an intermediate pressure cloud deck ($2.6\sigma$). Finally, we report non-detections of CH$_4$, CO, C$_2$H$_2$, HCN, SO$_2$, and H$_2$S. We verify our results using a TauREx free chemistry retrieval. We also measure with POSEIDON a high planetary atmospheric metallicity ($\log(Z) = 1.57^{+0.17}_{-0.18}$, $Z = 37^{+18}_{-13}$) and a potentially substellar C/O ratio for the planet ($C/O = 0.282^{+0.078}_{-0.053}$) compared to the star ($C/O_* = 0.41 \pm 0.08$), suggesting a formation pathway for WASP-166b that includes planetesimal accretion followed by core erosion or photoevaporation, which may indicate these to be plausible driving processes in the formation of the Hot Neptune Desert., Comment: Submitted to AAS Journals, 42 pages, 21 figures, 9 tables

Past researches show that personality trait is a strong predictor for ones academic performance. Today, mature and verified marker systems for assessing personality traits already exist. However, marker systems-based assessing methods have their own limitations. For example, dishonest responses cannot be avoided. In this research, the goal is to develop a method that can overcome the limitations. The proposed method will rely on physiological signals for the assessment. Thirty participants have participated in this experiment. Based on the statistical results, we found that there are correlations between students personality traits and their physiological signal change when learning via videos. Specifically, we found that participants degree of extraversion, agreeableness, conscientiousness, and openness to experiences are correlated with the variance of heart rates, the variance of GSR values, and the skewness of voice frequencies, etc.

Let $\sigma$ be a primitive substitution on an alphabet $\mathcal{A}$, and let $\mathcal{W}_n$ be the set of words of length $n$ determined by $\sigma$ (i.e., $w \in \mathcal{W}_n$ if $w$ is a subword of $\sigma^k(a)$ for some $a \in \mathcal{A}$ and $k \geq 1$). We show that the diameter of $\mathcal{W}_n$ in the edit distance is $o(n)$. Thus, any two words in $\mathcal{W}_n$ have a common subsequence of length $n-o(n)$, which implies that the corresponding substitution dynamical system is loosely Bernoulli. The main challenge is handling the case where $\sigma$ is non-uniform. Finally, we show that for the Thue--Morse substitution, the diameter of $\mathcal{W}_n$ is at least $\sqrt{n/6} - 1$., Comment: 26 pages, 3 figures