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This research uses the implementation of a school suspension ban in Maryland to test whether a top-down state-initiated ban on suspensions in early primary grades can influence school behavior regarding school discipline. Beginning in the fall of 2017, the State of Maryland banned the use of out-of-school suspensions for grades PK-2, unless a student posed an "imminent threat" to staff or students. This research investigates (1) what was the effect of the ban on discipline outcomes for students in both treated grades and upper elementary grades not subject to the ban? (2) did schools bypass the ban by coding more events as threatening or increasing the use of in-school suspensions? and (3) were there differential effects for students in groups that are historically suspended more often? Using a comparative interrupted time series strategy, we find that the ban is associated with a substantial reduction in, but not a total elimination of, out-of-school suspensions for targeted grades without substitution of in-school suspensions. Disproportionalities by race and other characteristics remain after the ban. Grades not subject to the ban experienced few effects, suggesting the ban did not trigger a schoolwide response that reduced exclusionary discipline.

We perform an experimental two-color high-order harmonic generation study in argon with the fundamental of an ytterbium ultrashort pulse laser and its second harmonic. The intensity of the second harmonic and its phase relative to the fundamental are varied, in a large range compared to earlier works, while keeping the total intensity constant. We extract the optimum values for the relative phase and ratio of the two colors which lead to a maximum yield enhancement for each harmonic order in the extreme ultraviolet spectrum. Within the semi-classical three-step model, the yield maximum can be associated with a flat electron return time vs. return energy distribution. An analysis of different distributions allows to predict the required relative two-color phase and ratio for a given harmonic order, total laser intensity, fundamental wavelength, and ionization potential.

There exists an extremely wide array of LLM benchmarking tasks, whereas oftentimes a single number is the most actionable for decision-making, especially by non-experts. No such aggregation schema exists that is not Elo-based, which could be costly or time-consuming. Here we propose a method to aggregate performance across a general space of benchmarks, nicknamed Project "MPG," dubbed Model Performance and Goodness, additionally referencing a metric widely understood to be an important yet inaccurate and crude measure of car performance. Here, we create two numbers: a "Goodness" number (answer accuracy) and a "Fastness" number (cost or QPS). We compare models against each other and present a ranking according to our general metric as well as subdomains. We find significant agreement between the raw Pearson correlation of our scores and those of Chatbot Arena, even improving on the correlation of the MMLU leaderboard to Chatbot Arena.

In a context where the Brazilian judiciary system, the largest in the world, faces a crisis due to the slow processing of millions of cases, it becomes imperative to develop efficient methods for analyzing legal texts. We introduce uBERT, a hybrid model that combines Transformer and Recurrent Neural Network architectures to effectively handle long legal texts. Our approach processes the full text regardless of its length while maintaining reasonable computational overhead. Our experiments demonstrate that uBERT achieves superior performance compared to BERT+LSTM when overlapping input is used and is significantly faster than ULMFiT for processing long legal documents., Comment: To appear at 15th STIL @ BRACIS'24

Amyotrophic lateral sclerosis (ALS), a progressive neuromuscular degenerative disease, severely restricts patient communication capacity within a few years of onset, resulting in a significant deterioration of quality of life. The P300 speller brain computer interface (BCI) offers an alternative communication medium by leveraging a subject's EEG response to characters traditionally highlighted on a character grid on a graphical user interface (GUI). A recurring theme in P300-based research is enhancing performance to enable faster subject interaction. This study builds on that theme by addressing key limitations, particularly in the training of multi-subject classifiers, and by integrating advanced language models to optimize stimuli presentation and word prediction, thereby improving communication efficiency. Furthermore, various advanced large language models such as Generative Pre-Trained Transformer (GPT2), BERT, and BART, alongside Dijkstra's algorithm, are utilized to optimize stimuli and provide word completion choices based on the spelling history. In addition, a multi-layered smoothing approach is applied to allow for out-of-vocabulary (OOV) words. By conducting extensive simulations based on randomly sampled EEG data from subjects, we show substantial speed improvements in typing passages that include rare and out-of-vocabulary (OOV) words, with the extent of improvement varying depending on the language model utilized. The gains through such character-level interface optimizations are approximately 10%, and GPT2 for multi-word prediction provides gains of around 40%. In particular, some large language models achieve performance levels within 10% of the theoretical performance limits established in this study. In addition, both within and across subjects, training techniques are explored, and speed improvements are shown to hold in both cases., Comment: 21 pages, 11 figures, 1 table. arXiv admin note: substantial text overlap with arXiv:2405.13329

Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines are one of the most-cost effective outbreak response interventions, with the potential to avert significant morbidity and mortality through timely delivery. Models can contribute to the design of vaccine response by investigating the importance of timeliness, identifying high-risk areas, prioritising the use of limited vaccine supply, highlighting surveillance gaps and reporting, and determining the short- and long-term benefits. In this review, we examine how models have been used to inform vaccine response for 10 VPDs, and provide additional insights into the challenges of outbreak response modelling, such as data gaps, key vaccine-specific considerations, and communication between modellers and stakeholders. We illustrate that while models are key to policy-oriented outbreak vaccine response, they can only be as good as the surveillance data that inform them., Comment: 19 pages including references and abstract, 1 figure

Despite efforts from cloud and content providers to lower latency to acceptable levels for current and future services (e.g., augmented reality or cloud gaming), there are still opportunities for improvement. A major reason that traffic engineering efforts are challenged to lower latency is that the Internet's inter-domain routing protocol, the Border Gateway Protocol, is oblivious to any performance metric, and circuitous routing is still pervasive. In this work, we propose two implementation modifications that networks can leverage to make BGP latency-aware and reduce excessive latency inflation. These proposals, latency-proportional AS prepending and local preference neutralization, show promise towards providing a method for propagating abstract latency information with a reasonable increase in routing overhead.

Large language models (LLMs) frequently make errors when handling even simple numerical problems, such as comparing two small numbers. A natural hypothesis is that these errors stem from how LLMs represent numbers, and specifically, whether their representations of numbers capture their numeric values. We tackle this question from the observation that LLM errors on numerical tasks are often distributed across \textit{the digits} of the answer rather than normally around \textit{its numeric value}. Through a series of probing experiments and causal interventions, we show that LLMs internally represent numbers with individual circular representations per-digit in base 10. This digit-wise representation, as opposed to a value representation, sheds light on the error patterns of models on tasks involving numerical reasoning and could serve as a basis for future studies on analyzing numerical mechanisms in LLMs.

Incorporating a priori physics knowledge into machine learning leads to more robust and interpretable algorithms. In this work, we combine deep learning techniques and classic numerical methods for differential equations to solve two challenging problems in dynamical systems theory: dynamics discovery and parameter estimation. Results demonstrate the effectiveness of the proposed approaches on a suite of test problems exhibiting oscillatory and chaotic dynamics. When comparing the performance of various numerical schemes, such as the Runge-Kutta and linear multistep families of methods, we observe promising results in predicting the system dynamics and estimating physical parameters, given appropriate choices of spatial and temporal discretization schemes and numerical method orders., Comment: 30 pages, 11 figures

Objective: Molecular testing (MT) classifies cytologically indeterminate thyroid nodules as benign or malignant with high sensitivity but low positive predictive value (PPV), only using molecular profiles, ignoring ultrasound (US) imaging and biopsy. We address this limitation by applying attention multiple instance learning (AMIL) to US images. Methods: We retrospectively reviewed 333 patients with indeterminate thyroid nodules at UCLA medical center (259 benign, 74 malignant). A multi-modal deep learning AMIL model was developed, combining US images and MT to classify the nodules as benign or malignant and enhance the malignancy risk stratification of MT. Results: The final AMIL model matched MT sensitivity (0.946) while significantly improving PPV (0.477 vs 0.448 for MT alone), indicating fewer false positives while maintaining high sensitivity. Conclusion: Our approach reduces false positives compared to MT while maintaining the same ability to identify positive cases, potentially reducing unnecessary benign thyroid resections in patients with indeterminate nodules., Comment: 9 pages, 3 figures

Timely disease diagnosis is challenging due to increasing disease burdens and limited clinician availability. AI shows promise in diagnosis accuracy but faces real-world application issues due to insufficient validation in clinical workflows and diverse populations. This study addresses gaps in medical AI downstream accountability through a case study on age-related macular degeneration (AMD) diagnosis and severity classification. We designed and implemented an AI-assisted diagnostic workflow for AMD, comparing diagnostic performance with and without AI assistance among 24 clinicians from 12 institutions with real patient data sampled from the Age-Related Eye Disease Study (AREDS). Additionally, we demonstrated continual enhancement of an existing AI model by incorporating approximately 40,000 additional medical images (named AREDS2 dataset). The improved model was then systematically evaluated using both AREDS and AREDS2 test sets, as well as an external test set from Singapore. AI assistance markedly enhanced diagnostic accuracy and classification for 23 out of 24 clinicians, with the average F1-score increasing by 20% from 37.71 (Manual) to 45.52 (Manual + AI) (P-value < 0.0001), achieving an improvement of over 50% in some cases. In terms of efficiency, AI assistance reduced diagnostic times for 17 out of the 19 clinicians tracked, with time savings of up to 40%. Furthermore, a model equipped with continual learning showed robust performance across three independent datasets, recording a 29% increase in accuracy, and elevating the F1-score from 42 to 54 in the Singapore population.

In general, large solar flares are more efficient at accelerating high-energy electrons than microflares. Nonetheless, we sometimes observe microflares that accelerate electrons to high energies. We statistically characterize 39 microflares with strikingly hard spectra in the hard X-ray (HXR) range, which means that they are efficient in accelerating high-energy electrons. We refer to these events as "hard microflares." The statistical analysis is built upon spectral and imaging information from STIX, combined with EUV and magnetic field maps from SDO. The key observational result is that all hard microflares in this dataset have one of the footpoints rooted directly within a sunspot (either in the umbra or the penumbra). This clearly indicates that the underlying magnetic flux densities are large. For the events with the classic two-footpoints morphology, the absolute value of the mean line-of-sight magnetic flux density (and vector magnetic field strength) at the footpoint rooted within the sunspot ranges from 600 to 1800 G (1500 to 2500 G), whereas the outer footpoint measures from 10 to 200 G (100 to 400 G), therefore about 10 times weaker. Approximately 78% of hard microflares, which exhibited two HXR footpoints, have similar or even stronger HXR flux from the footpoint rooted within the sunspot. This contradicts the magnetic mirroring scenario. In addition, about 74% of the events could be approximated by a single loop geometry, demonstrating that hard microflares typically have a relatively simple morphology. We conclude that all hard microflares are rooted in sunspots, which implies that the magnetic field strength plays a key role in efficiently accelerating high-energy electrons, with hard HXR spectra associated with strong fields. This key result will allow us to further constrain our understanding of the electron acceleration mechanisms in flares and space plasmas., Comment: Accepted for publication in A&A. 16 pages, 11 figures

Pulmonary diseases are a public health problem that requires accurate and fast diagnostic techniques. In this paper, a method based on convolutional neural networks (CNN), Data Augmentation, ResNet50 and Vision Transformers (ViT) is proposed to detect lung pathologies from medical images. A dataset of X-ray images and CT scans of patients with different lung diseases, such as cancer, pneumonia, tuberculosis and fibrosis, is used. The results obtained by the proposed method are compared with those of other existing methods, using performance metrics such as accuracy, sensitivity, specificity and area under the ROC curve. The results show that the proposed method outperforms the other methods in all metrics, achieving an accuracy of 98% and an area under the ROC curve of 99%. It is concluded that the proposed method is an effective and promising tool for the diagnosis of pulmonary pathologies by medical imaging., Comment: 10 pages

Language Models (LMs) recently incorporate mixture-of-experts layers consisting of a router and a collection of experts to scale up their parameter count given a fixed computational budget. Building on previous efforts indicating that token-expert assignments are predominantly influenced by token identities and positions, we trace routing decisions of similarity-annotated text pairs to evaluate the context sensitivity of learned token-expert assignments. We observe that routing in encoder layers mainly depends on (semantic) associations, but contextual cues provide an additional layer of refinement. Conversely, routing in decoder layers is more variable and markedly less sensitive to context.

The conversion of solar energy into chemical energy, stored in the form of hydrogen, bears enormous potential as a sustainable fuel for powering emerging technologies. Photoactive oxynitrides are promising materials for splitting water into molecular oxygen and hydrogen. However, one of the issues limiting widespread commercial use of oxynitrides is the degradation during operation. While recent studies have shown the loss of nitrogen, its relation to the reduced efficiency has not been directly and systematically addressed with experiments. In this study, we demonstrate the impact of the anionic stoichiometry of BaTaO$_x$N$_y$ on its electronic structure and functional properties. Through experimental ion scattering, electron microscopy, and photoelectron spectroscopy investigations, we determine the anionic composition ranging from the bulk towards the surface of BaTaO$_x$N$_y$ thin films. This further serves as input for band structure computations modeling the substitutional disorder of the anion sites. Combining our experimental and computational approaches, we reveal the depth-dependent elemental composition of oxynitride films, resulting in downward band bending and the loss of semiconducting character towards the surface. Extending beyond idealized systems, we demonstrate the relation between the electronic properties of real oxynitride photoanodes and their performance, providing guidelines for engineering highly efficient photoelectrodes and photocatalysts for clean hydrogen production., Comment: 19 pages, 5 figures

Prostate cancer (PCa) was the most frequently diagnosed cancer among American men in 2023. The histological grading of biopsies is essential for diagnosis, and various deep learning-based solutions have been developed to assist with this task. Existing deep learning frameworks are typically applied to individual 2D cross-sections sliced from 3D biopsy tissue specimens. This process impedes the analysis of complex tissue structures such as glands, which can vary depending on the tissue slice examined. We propose a novel digital pathology data source called a "volumetric core," obtained via the extraction and co-alignment of serially sectioned tissue sections using a novel morphology-preserving alignment framework. We trained an attention-based multiple-instance learning (ABMIL) framework on deep features extracted from volumetric patches to automatically classify the Gleason Grade Group (GGG). To handle volumetric patches, we used a modified video transformer with a deep feature extractor pretrained using self-supervised learning. We ran our morphology-preserving alignment framework to construct 10,210 volumetric cores, leaving out 30% for pretraining. The rest of the dataset was used to train ABMIL, which resulted in a 0.958 macro-average AUC, 0.671 F1 score, 0.661 precision, and 0.695 recall averaged across all five GGG significantly outperforming the 2D baselines.

We propose sieve wild bootstrap analogues to the adaptive Lasso solution path unit root tests of Arnold and Reinschl\"ussel (2024) arXiv:2404.06205 to improve finite sample properties and extend their applicability to a generalised framework, allowing for non-stationary volatility. Numerical evidence shows the bootstrap to improve the tests' precision for error processes that promote spurious rejections of the unit root null, depending on the detrending procedure. The bootstrap mitigates finite-sample size distortions and restores asymptotically valid inference when the data features time-varying unconditional variance. We apply the bootstrap tests to real residential property prices of the top six Eurozone economies and find evidence of stationarity to be period-specific, supporting the conjecture that exuberance in the housing market characterises the development of Euro-era residential property prices in the recent past., Comment: 30 pages, 1 figure (colour)

The detection of stellar variability often relies on the measurement of selected activity indicators such as coronal emission lines and non-thermal emissions. On the flip side, the effective stellar temperature is normally seen as one of the key fundamental parameters (with mass and radius) to understanding the basic physical nature of a star and its relation with its environment (e.g., planetary instellation). We present a novel approach for measuring disk-averaged temperature variations to sub-Kelvin accuracy inspired by algorithms developed for precision radial velocity. This framework uses the entire content of the spectrum, not just pre-identified lines, and can be applied to existing data obtained with high-resolution spectrographs. We demonstrate the framework by recovering the known rotation periods and temperature modulation of Barnard star and AU Mic in datasets obtained in the infrared with SPIRou at CHFT and at optical wavelengths on $\epsilon$ Eridani with HARPS at ESO 3.6-m telescope. We use observations of the transiting hot Jupiter HD189733\,b, obtained with SPIRou, to show that this method can unveil the minute temperature variation signature expected during the transit event, an effect analogous to the Rossiter-McLaughlin effect but in temperature space. This method is a powerful new tool for characterizing stellar activity, and in particular temperature and magnetic features at the surfaces of cool stars, affecting both precision radial velocity and transit spectroscopic observations. We demonstrate the method in the context of high-resolution spectroscopy but the method could be used at lower resolution., Comment: accepted for publication in AJ

People often strive for deep engagement in activities which is usually associated with feelings of flow: a state of full task absorption accompanied by a sense of control and fulfillment. The intrinsic factors driving such engagement and facilitating subjective feelings of flow remain unclear. Building on computational theories of intrinsic motivation, this study examines how learning progress predicts engagement and directs cognitive control. Results showed that task engagement, indicated by feelings of flow and distractibility, is a function of learning progress. Electroencephalography data further revealed that learning progress is associated with enhanced proactive preparation (e.g., reduced pre-stimulus contingent negativity variance and parietal alpha desynchronization) and improved feedback processing (e.g., increased P3b amplitude and parietal alpha desynchronization). The impact of learning progress on cognitive control is observed at the task-block and goal-episode levels, but not at the trial level. This suggests that learning progress shapes cognitive control over extended periods as progress accumulates. These findings highlight the critical role of learning progress in sustaining engagement and cognitive control in goal-directed behavior.

The Capacitated Sum of Radii problem involves partitioning a set of points $P$, where each point $p\in P$ has capacity $U_p$, into $k$ clusters that minimize the sum of cluster radii, such that the number of points in the cluster centered at point $p$ is at most $U_p$. We begin by showing that the problem is APX-hard, and that under gap-ETH there is no parameterized approximation scheme (FPT-AS). We then construct a $\approx5.83$-approximation algorithm in FPT time (improving a previous $\approx7.61$ approximation in FPT time). Our results also hold when the objective is a general monotone symmetric norm of radii. We also improve the approximation factors for the uniform capacity case, and for the closely related problem of Capacitated Sum of Diameters., Comment: 56 pages, 9 figures

This article proves the following theorem, first enunciated by Roger Penrose about 70 years ago: In $\mathbb{R}P^2$, if regular conics are assigned to seven of the vertices of a combinatorial cube such that (i) conics connected by an edge are in double contact, and (ii) the chords of contact associated to a cube face meet in a common point, then there exists an eighth conic such that the completed cube satisfies (i) and (ii). This conic is unique if not all the common points of condition (ii) are the same. The proof is based on the following analogous theorem, which is also proved: In $\mathbb{R}P^3$, if regular quadrics are assigned to seven of the vertices of a combinatorial cube such that (i) quadrics connected by an edge are in ring contact, and (ii) the ring planes associated to a cube face meet in a common axis, then there exists an eighth quadric such that the completed cube satisfies (i) and (ii). This quadric is unique if not all the common axes of condition (ii) are the same., Comment: 26 pages, 12 figures

Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for cortical registration, parcellation, and thickness estimation. Traditionally, these analyses require high-resolution, isotropic scans with good gray-white matter contrast, typically a 1mm T1-weighted scan. This excludes most clinical MRI scans, which are often anisotropic and lack the necessary T1 contrast. To enable large-scale neuroimaging studies using vast clinical data, we introduce recon-all-clinical, a novel method for cortical reconstruction, registration, parcellation, and thickness estimation in brain MRI scans of any resolution and contrast. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions (SDFs) and classical geometry processing for accurate surface placement while maintaining topological and geometric constraints. The method does not require retraining for different acquisitions, thus simplifying the analysis of heterogeneous clinical datasets. We tested recon-all-clinical on multiple datasets, including over 19,000 clinical scans. The method consistently produced precise cortical reconstructions and high parcellation accuracy across varied MRI contrasts and resolutions. Cortical thickness estimates are precise enough to capture aging effects independently of MRI contrast, although accuracy varies with slice thickness. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all-clinical, enabling researchers to perform detailed cortical analysis on the huge amounts of already existing clinical MRI scans. This advancement may be particularly valuable for studying rare diseases and underrepresented populations where research-grade MRI data is scarce., Comment: 16 pages in the manuscript with 11 page supplementary material

In this study we provide empirical evidence demonstrating that the quality of training data impacts model performance in Human Pose Estimation (HPE). Inaccurate labels in widely used data sets, ranging from minor errors to severe mislabeling, can negatively influence learning and distort performance metrics. We perform an in-depth analysis of popular HPE data sets to show the extent and nature of label inaccuracies. Our findings suggest that accounting for the impact of faulty labels will facilitate the development of more robust and accurate HPE models for a variety of real-world applications. We show improved performance with cleansed data., Comment: 15 pages, 7 figures, 5 tables

The sub-Jovian desert is a region in the mass-period and radius-period parameter space, typically encompassing short-period ranges between super-Earths and hot Jupiters, that exhibits an intrinsic dearth of planets. This scarcity is likely shaped by photoevaporation caused by the stellar irradiation received by giant planets that have migrated inward. We report the detection and characterization of TOI-3568 b, a transiting super-Neptune with a mass of $26.4\pm1.0$ M$_\oplus$, a radius of $5.30\pm0.27$ R$_\oplus$, a bulk density of $0.98\pm0.15$ g cm$^{-3}$, and an orbital period of 4.417965(5) d situated in the vicinity of the sub-Jovian desert. This planet orbiting a K dwarf star with solar metallicity, was identified photometrically by TESS. It was characterized as a planet by our high-precision radial velocity monitoring program using MAROON-X at Gemini North, supplemented by additional observations from the SPICE large program with SPIRou at CFHT. We performed a Bayesian MCMC joint analysis of the TESS and ground-based photometry, MAROON-X and SPIRou radial velocities, to measure the orbit, radius, and mass of the planet, as well as a detailed analysis of the high-resolution flux and polarimetric spectra to determine the physical parameters and elemental abundances of the host star. Our results reveal TOI-3568 b as a hot super-Neptune, rich in hydrogen and helium with a core of heavier elements with a mass between 10 and 25 M$_\oplus$. We analyzed the photoevaporation status of TOI-3568 b and found that it experiences one of the highest EUV luminosities among planets with a mass M$_{\rm p}$ $<2$ M$_{\rm Nep}$, yet it has an evaporation lifetime exceeding 5 Gyr. Positioned in the transition between two significant populations of exoplanets on the mass-period and energy diagrams, this planet presents an opportunity to test theories concerning the origin of the sub-Jovian desert., Comment: Accepted for publication in A&A on September 4, 2024

We present a benchmarking protocol that combines the characterization of boron nitride (BN) crystals and films with the evaluation of the electronic properties of graphene on these substrates. Our study includes hBN crystals grown under different conditions and scalable BN films deposited by either chemical or physical vapor deposition (CVD or PVD). We explore the complete process from boron nitride growth, over its optical characterization by time-resolved cathodoluminescence (TRCL), to the optical and electronic characterization of graphene by Raman spectroscopy after encapsulation and Hall bar processing. Within our benchmarking protocol we achieve a homogeneous electronic performance within each Hall bar device through a fast and reproducible processing routine. We find that a free exciton lifetime of 1 ns measured on as-grown hBN crystals by TRCL is sufficient to achieve high graphene room temperature charge carrier mobilities of 80,000 cm$^2$/(Vs) at a carrier density of |n| = 10$^{12}$ cm$^{-2}$, while respective exciton lifetimes around 100 ps yield mobilities up to 30,000 cm$^2$/(Vs). For scalable PVD-grown BN films, we measure carrier mobilities exceeding 10,000 cm$^2$/(Vs) which correlates with a graphene Raman 2D peak linewidth of 22 cm$^{-1}$. Our work highlights the importance of the Raman 2D linewidth of graphene as a critical metric that effectively assesses the interface quality (i.e. surface roughness) to the BN substrate, which directly affects the charge carrier mobility of graphene. Graphene 2D linewidth analysis is suitable for all BN substrates and is particularly advantageous when TRCL or BN Raman spectroscopy cannot be applied to specific BN materials such as amorphous or thin films. This underlines the superior role of spatially-resolved spectroscopy in the evaluation of BN crystals and films for the use of high-mobility graphene devices., Comment: 18 pages, 11 figures

Combining high peak and high average power has long been a key challenge of ultrafast laser technology, crucial for applications such as laser-plasma acceleration and strong-field physics. A promising solution lies in post-compressed ytterbium lasers, but scaling these to high pulse energies presents a major bottleneck. Post-compression techniques, particularly Herriott-type multi-pass cells (MPCs), have enabled large peak power boosts at high average powers but their pulse energy acceptance reaches practical limits defined by setup size and coating damage threshold. In this work, we address this challenge and demonstrate a novel type of compact, energy-scalable MPC (CMPC). By employing a novel MPC configuration and folding the beam path, the CMPC introduces a new degree of freedom for downsizing the setup length, enabling compact setups even for large pulse energies. We experimentally and numerically verify the CMPC approach, demonstrating post-compression of 8 mJ pulses from 1 ps down to 51 fs in atmospheric air using a cell roughly 45 cm in length at low fluence values. Additionally, we discuss the potential for energy scaling up to 200 mJ with a setup size reaching 2.5 m. Our work presents a new approach to high-energy post-compression, with up-scaling potential far beyond the demonstrated parameters. This opens new routes for achieving the high peak and average powers necessary for demanding applications of ultrafast lasers., Comment: 18 pages, 13 figures

We present a numerical method for extrapolating polarizability measurements to dc as done in the assessment of blackbody radiation shifts for ion-based clocks. The method explicitly accounts for the frequency dependence of relevant atomic transitions without introducing an ad hoc modelling function. It incorporates \emph{a priori} atomic structure calculations, which allows measurements to be augmented by calculations if there is insufficient data to make a purely measurement based estimate. The method also provides indicators of inconsistencies between theory and experiment or inadequacies of the data for making an extrapolation. We use results from Al$^+$, Lu$^+$, and Yb$^+$ to illustrate features of the method., Comment: 8 pages, 1 figure

This report exam­ines how demographics and outcomes changed when schools that had enrolled a dis­pro­por­tion­ate share of the sys­tem's white stu­dents entered the city's cen­tral­ized enroll­ment sys­tem in New Orleans. It finds that the schools enter­ing that sys­tem (OneApp/?NCAP) led to increased access to those schools for Black and oth­er non­white stu­dents. While schools have been reluc­tant to give up con­trol over enroll­ment, these schools did not see declines in white enroll­ment or aca­d­e­m­ic per­for­mance after join­ing the enroll­ment system.

Unified enrollment (UE) systems were designed to improve efficiency, equity, and transparency in school choice processes, but research has focused on efficiency gains. This study examines whether moving from decentralized enrollment processes to UE mitigates or exacerbates racial segregation that often occurs in choice systems. Specifically, we examine a subset of charter schools in New Orleans that had enrolled disproportionately high numbers of white students prior to entering UE. We find that UE entry was associated with increased enrollment of nonwhite students in these schools without offsetting declines in white enrollment, facilitated by schools also increasing total enrollment after entering UE. We find no meaningful impacts of UE on school accountability measures, student or teacher mobility, or student discipline.

The American Enterprise Institute, EducationCounsel, and The Century Foundation undertook a joint process to examine--across ideological lines--the nature of the existing system that finances graduate education, including its benefits and downsides, and to design a framework that should guide the improvement of federal policy regarding graduate student financing. This report aims to outline policy options that address the burden on students, particularly the disproportionate burden facing students of color, and the growing cost of the graduate loan programs for taxpayers. The shared goal of this process was improved outcomes for students and better value for taxpayers. Analysis and discussion of different policy options led to the conclusion that any politically viable and effective approach must address five different dimensions of the problem through the following set of policy levers: (1) set reasonable loan limits; (2) award grant aid to students and institutions to address equity and social good considerations; (3) ensure sufficient value and return on investment for students and taxpayers; (4) enhance the regulatory structure and consumer protections for private lending; and (5) improve data disclosure and transparency.

While there are many different types of education data, policymakers and education leaders often place heavy emphasis on data from large-scale quantitative measures, such as annual state assessments. But data from these sources alone do not provide a complete picture of learning and are often not well suited to informing improvements at the local level. This brief provides seven recommendations to help educators, administrators, policymakers, and others use a wider range of data to more effectively support equitable learning outcomes.

When photographers and other editors of image material produce an image, they make a statement about what matters by situating some objects in the foreground and others in the background. While this prominence of objects is a key analytical category to qualitative scholars, recent quantitative approaches to automated image analysis have not yet made this important distinction but treat all areas of an image similarly. We suggest carefully considering objects' prominence as an essential step in analyzing images as data. Its modeling requires defining an object and operationalizing and measuring how much attention a human eye would pay. Our approach combines qualitative analyses with the scalability of quantitative approaches. Exemplifying object prominence with different implementations -- object size and centeredness, the pixels' image depth, and salient image regions -- we showcase the usefulness of our approach with two applications. First, we scale the ideology of eight US newspapers based on images. Second, we analyze the prominence of women in the campaign videos of the U.S. presidential races in 2016 and 2020. We hope that our article helps all keen to study image data in a conceptually meaningful way at scale., Comment: Working Paper

Earth's magnetotail, a night-side region characterized by stretched magnetic field lines and strong plasma currents, is the primary site for the release of magnetic field energy and its transformation into plasma heating and kinetic energy plus charged particle acceleration during magnetic reconnection. In this study, we demonstrate that the efficiency of this acceleration can be sufficiently high to produce populations of relativistic and ultra-relativistic electrons, with energies up to several MeV, which exceeds all previous theoretical and simulation estimates. Using data from the low altitude ELFIN and CIRBE CubeSats, we show multiple events of relativistic electron bursts within the magnetotail, far poleward of the outer radiation belt. These bursts are characterized by power-law energy spectra and can be detected during even moderate substorms.

With advanced synthetic techniques, a wide variety of well-defined graphene nano-ribbons (GNRs) can be produced with atomic precision. Hence, finding the relation between their structures and properties becomes important for the rational design of GNRs. In this work, we explore the complete chemical space of gulf-edged zigzag graphene nanoribbons (ZGNR-Gs), a subclass of zigzag GNRs in which the zigzag edges miss carbon atoms in a regular sequence. We demonstrate that the electronic properties of ZGNR-Gs depend on four structural parameters: ribbon width, gulf edge size, unit length, and gulf offset. Using tight-binding calculations and the Hubbard model, we find that all ZGNR-Gs are semiconductors with varying band gaps; there are no metals in this class of materials. Notably, when spin polarization is considered, most ZGNR-Gs exhibit antiferromagnetic behavior, with the spin moments and spin-induced band gap opening being stabilized by longer zigzag segments at the edges. Furthermore, we provide simple empirical rules that describe the Z2 topological invariant based on the aforementioned structural parameters. By analyzing the full chemical space of ZGNR-Gs, we offer insights into the design of GNRs with desired electronic, magnetic, and topological properties for nanoelectronic applications.

The discovery of the superconductivity in the infinite-layer nickelates, as topotactically reduced from their respective perovskite percussors via co-annealing with CaH2, extends the understanding in superconductivity. Nevertheless, whether the incorporated 1H composition is critical to the infinite-layer superconductivity recently arouses considerable debates, while the central challenge lies in the quantification of 1H that is easily interfered by the conventional electron or orbital associated processes. Herein, we demonstrate the irrelevance between the superconductivity in the infinite-layer nickelates and their incorporated 1H composition, assisted by nuclear reaction analysis (NRA) and heavy ion energy recoil detection analysis (HIERDA) based on the nuclear interactions at MeV energy scale. These approaches completely overwhelm the conventional interferes, such as ionization, activation and chemical bonds, and achieves the 1H quantification within superconducting La0.8Sr0.2NiO2 (or Nd0.8Sr0.2NiO2). A large diversity of 1H composition far beyond the previously expected critical dome was observed, while their TC were not changed significantly. Furthermore, the superconductivity was demonstrated to be achievable for La0.8Sr0.2NiO2 reduced by Al without any hydrogen associated process, while the superconducting properties for the CaH2 reduced La0.8Sr0.2NiO2 is rather stable after long term exposure in air, despite the high volatility of 1H within oxides. All these results indicate that the 1H incorporation composition is not critical to the superconductivity of the infinite-layer nickelates.