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The U.S. has witnessed a resurgence of labor activism, with teachers at the forefront. We examine how teacher strikes affect compensation, working conditions, and productivity with an original dataset of 772 teacher strikes generating 48 million student days idle between 2007 and 2023. Using an event study framework, we find that, on average, strikes increase compensation by 8% and lower pupil-teacher ratios by 0.5 students, driven by new state revenues. We find little evidence of sizable impacts on student achievement up to five years post-strike, though strikes lasting 10 or more days decrease math achievement in the short-term.

We examine the efficiency of traditional school districts versus charter schools in providing students with teachers who meet their demographic and education needs. Using panel data from the state of Michigan, we estimate the relationship between enrollment of Black, Hispanic, special education, and English learner students and the presence of Black, Hispanic, Special Education, and ESL teachers, and test whether this relationship differs at charter and traditional district-run schools. Because charter schools typically have less market power in hiring than large districts, we compare charter school employment practices to traditional public schools in districts of comparable size. Our results suggest that charter schools are more likely to employ same race teachers for Black students but not Hispanic students, and districts schools are slightly better at providing ESL and SPED teachers. We conclude that charter autonomy does not necessary generate better student-teacher matches, but Michigan charters may occupy a market niche by serving Black students and staffing Black teachers.

In the context of an ongoing national conversation about teacher shortages, we build on prior literature on the efficacy of teacher certification pathways by comparing entry and exit patterns based on age at the time of certification. All trainees who complete a state certification process have invested substantial time and resources into entering teaching. Competing employment opportunities and expectations might vary with age. We use both linear regression and discrete-time hazard models to examine employment and subsequent exit of newly certified teacher trainees in Michigan from 2011 to 2023. We find that while mid-career entrants in their 30s and 40s compose a small share of new certificates, they are more likely to enter a teaching position and no more likely to subsequently exit than counterparts who were certified in their early 20s. Mid-career pathways also contribute to teacher diversity by attracting more Black and male teachers who enter and persist.

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.

With the rising adoption of dry dilution refrigerators across scientific and industrial domains, there has been a pressing demand for highly efficient vibration decoupling systems capable of operation at cryogenic temperatures in order to achieve the low vibration levels required for operation of sensitive equipment like cryogenic detectors or quantum devices. As part of the NUCLEUS experiment, a cryogenic spring pendulum has been engineered to effectively isolate pulse tube vibrations by establishing an autonomous frame of reference for the experimental volume, while sustaining temperatures below 10 mK. Attaining attenuation of up to two orders of magnitude within the region of interest of the NUCLEUS cryogenic detectors, we achieved displacement RMS values in the order of 1 nm in the axial direction and 100 pm radially, thereby reducing vibrations below typical environmental levels. Our successful detector operation across multiple cooldown cycles demonstrated negligible sensitivity to pulse tube induced vibrations, culminating in the achievement of an ultra-low $(6.22 \pm 0.07)$ eV baseline resolution on a gram-scale CaWO$_4$ cryogenic calorimeter during continuous pulse tube operation over the course of several weeks.

Various methods have been introduced to measure the orbital angular momentum (OAM) of light, from fork holograms to Dove prism interferometers, from tilted lenses to triangular apertures - each with their own benefits and limitations. Here we demonstrate how simple knife-edge diffraction can be used to identify the OAM of an optical phase vortex from the formation of fork dislocations within the Fresnel diffraction pattern. For vector vortex beams without net OAM, the conventional Fresnel fringes are recovered, whereas the polarization in the geometric shadow is changed in its ellipticity. The observed diffraction patterns agree with simulations and their features can be explained by considering diffraction as an interference phenomenon. Knife-edge diffraction provides not only an instructive illustration of various properties of phase and polarization vortices, but can also serve as an ideal method for the quick determination of optical OAM, with potential applications beyond optics, where alternative detection measurement methods may be harder to realize., Comment: 6 pages and 5 figures

We study ground-penetrating synthetic aperture radar measurements of scattering by targets located below a rough air-soil interface. By considering the inherent space/angle limitations of this imaging modality, we introduce a simplified model for measurements. This model assumes (i) first-order interactions between the target and the air-soil interface, (ii) scattering by the target below a flat air-soil interface, and (iii) a point target model. Using the method of fundamental solutions to simulate two-dimensional simulations of scalar waves for the direct scattering problem, we systematically study each of these data modeling assumptions. To test and validate these assumptions, we apply principal component analysis to approximately remove ground bounce signals from measurements and then apply Kirchhoff migration on that processed data to produce images. We show that images using this modeled data are nearly identical to those that use simulated measurements from the full direct scattering problem. In that way, we show that this model contains the essential information contained in measurements. Consequently, it provides a theoretical framework for understanding how inherent space/angle limitations affect subsurface imaging systems.

LiDAR scenes constitute a fundamental source for several autonomous driving applications. Despite the existence of several datasets, scenes from adverse weather conditions are rarely available. This limits the robustness of downstream machine learning models, and restrains the reliability of autonomous driving systems in particular locations and seasons. Collecting feature-diverse scenes under adverse weather conditions is challenging due to seasonal limitations. Generative models are therefore essentials, especially for generating adverse weather conditions for specific driving scenarios. In our work, we propose a latent diffusion process constituted by autoencoder and latent diffusion models. Moreover, we leverage the clear condition LiDAR scenes with a postprocessing step to improve the realism of the generated adverse weather condition scenes., Comment: This is an intermediate version of our work

The flexibility and versatility of nanoassembled plasmonic structures provide platforms for mesoscale tunable optical modulation. Our recently developed model for these nanoassembled plasmonic structures is composed of a dielectric spherical core surrounded by a concentric spherical shell containing a random distribution of AuNPs. This model provides a useful platform for studying the role of a controlled amount of disorder on scattering by a particle. In that context, we explore the angular distribution of scattered light for different sizes (5 - 20 nm) and filling fractions (0.1 - 0.3) of the AuNP in the coatings. The simulations reveal that the coating of AuNPs redistributes power in a way that suppresses angular side lobes, thereby guiding the scattered power preferentially in the forward direction. These results highlight that with the ability to tune both the spatial and the spectral aspects of the scattering profile, these coated structures may serve as a platform for a variety of applications, including passive cloaking, scattering enhancement, and high-resolution imaging.

Realistic metric spaces (such as road/transportation networks) tend to be much more algorithmically tractable than general metrics. In an attempt to formalize this intuition, Abraham et al. (SODA 2010, JACM 2016) introduced the notion of highway dimension. A weighted graph $G$ has highway dimension $h$ if for every ball $B$ of radius $\approx 4r$ there is a hitting set of size $h$ hitting all the shortest paths of length $>r$ in $B$. Unfortunately, this definition fails to incorporate some very natural metric spaces such as the grid graph, and the Euclidean plane. We relax the definition of highway dimension by demanding to hit only approximate shortest paths. In addition to generalizing the original definition, this new definition also incorporates all doubling spaces (in particular the grid graph and the Euclidean plane). We then construct a PTAS for TSP under this new definition (improving a QPTAS w.r.t. the original more restrictive definition of Feldmann et al. (SICOMP 2018)). Finally, we develop a basic metric toolkit for spaces with small highway dimension by constructing padded decompositions, sparse covers/partitions, and tree covers. An abundance of applications follow.

Fundamental research on two-dimensional (2D) magnetic systems based on van der Waals materials has been gaining traction rapidly since their recent discovery. With the increase of recent knowledge, it has become clear that such materials have also a strong potential for applications in devices that combine magnetism with electronics, optics, and nanomechanics. Nonetheless, many challenges still lay ahead. Several fundamental aspects of 2D magnetic materials are still unknown or poorly understood, such as their often-complicated electronic structure, optical properties, and magnetization dynamics, and their magnon spectrum. To elucidate their properties and facilitate integration in devices, advanced characterization techniques and theoretical frameworks need to be developed or adapted. Moreover, developing synthesis methods which increase critical temperatures and achieve large-scale, high-quality homogeneous thin films is crucial before these materials can be used for real-world applications. Therefore, the field of 2D magnetic materials provides many challenges and opportunities for the discovery and exploration of new phenomena, as well as the development of new applications. This Roadmap presents the background, challenges, and potential research directions for various relevant topics in the field on the fundamentals, synthesis, characterization, and applications. We hope that this work can provide a strong starting point for young researchers in the field and provide a general overview of the key challenges for more experienced researchers., Comment: 87 pages, 24 figures. Roadmap based on the discussions during the workshop on Quantum Magnetic Materials hosted by the Lorentz Centre in Leiden, the Netherlands (Oct. 2023)

We report pulse energy scaling enabled by the use of Laguerre-Gaussian single-vortex ($\text{LG}_{0,l}$) beams for spectral broadening in a sub-40 cm long Herriott-type bulk multi-pass cell. Beams with orders ${l= 1-3}$ are generated by a spatial light modulator, which facilitates rapid and precise reconfiguration of the experimental conditions. 180 fs pulses with 610 uJ pulse energy are post-compressed to 44 fs using an $\text{LG}_{0,3}$ beam, boosting the peak power of an Ytterbium laser system from 2.5 GW to 9.1 GW. The spatial homogeneity of the output $\text{LG}_{0,l}$ beams is quantified and the topological charge is spectrally-resolved and shown to be conserved after compression by employing a custom spatio-temporal coupling measurement setup., Comment: 8 pages, 9 figures

Weyl semimetals have a variety of intriguing physical properties, including topologically protected electronic states that coexist with conducting states. Possible exploitation of topologically protected states in a conducting material is promising for technological applications. Weyl semimetals that form in a non-centrosymmetric structure that also contain magnetic moments may host a variety of emergent phenomena that cannot be seen in magnetic, centrosymmetric Weyl materials. It can be difficult to distinguish definitively between a centrosymmetric structure and one of its non-centrosymmetric subgroups with standard powder X-ray diffractometers in cases where two atoms in the compound have nearly the same atomic number, as is the case for the candidate Weyl semimetal CeGaGe. In these cases, a careful single-crystal neutron diffraction experiment with high-angle reflections provides complimentary information to X-ray diffraction and definitively resolves any ambiguity between centrosymmetric and non-centrosymmetric crystal structures. Single-crystal neutron diffraction measurements on the candidate Weyl semimetal CeGaGe confirms that its structure is non-centrosymmetric, described by space group 109 $\left(I4_1md\right)$ rather than the centrosymmetric space group 141 $\left(I4_1/amd\right)$. There are many high-angle reflections in the data set that give clear, physically intuitive evidence that CeGaGe forms with $I4_1md$ symmetry since Bragg planes of these reflections can contain Ga with no Ge or vice versa whereas the Bragg planes for a structure with $I4_1/amd$ symmetry would have a mix of Ga and Ge. Further, in some crystals we have studied, there is clear evidence for a structural transition from body-centered $I4_1md$ symmetry to primitive $P4_3$ and/or $P4_1$ symmetry.

Spiking neural networks (SNNs) emulated on dedicated neuromorphic accelerators promise to offer energy-efficient signal processing. However, the neuromorphic advantage over traditional algorithms still remains to be demonstrated in real-world applications. Here, we describe an intensity-modulation, direct-detection (IM/DD) task that is relevant to high-speed optical communication systems used in data centers. Compared to other machine learning-inspired benchmarks, the task offers several advantages. First, the dataset is inherently time-dependent, i.e., there is a time dimension that can be natively mapped to the dynamic evolution of SNNs. Second, small-scale SNNs can achieve the target accuracy required by technical communication standards. Third, due to the small scale and the defined target accuracy, the task facilitates the optimization for real-world aspects, such as energy efficiency, resource requirements, and system complexity.

Modern unmanned aerial vehicles (UAVs) are irreplaceable in search and rescue (SAR) missions to obtain a situational overview or provide closeups without endangering personnel. However, UAVs heavily rely on global navigation satellite system (GNSS) for localization which works well in open spaces, but the precision drastically degrades in the vicinity of buildings. These inaccuracies hinder aggregation of diverse data from multiple sources in a unified georeferenced frame for SAR operators. In contrast, CityGML models provide approximate building shapes with accurate georeferenced poses. Besides, LiDAR works best in the vicinity of 3D structures. Hence, we refine coarse GNSS measurements by registering LiDAR maps against CityGML and digital elevation map (DEM) models as a prior for allocentric mapping. An intuitive plausibility score selects the best hypothesis based on occupancy using a 2D height map. Afterwards, we integrate the registration results in a continuous-time spline-based pose graph optimizer with LiDAR odometry and further sensing modalities to obtain globally consistent, georeferenced trajectories and maps. We evaluate the viability of our approach on multiple flights captured at two distinct testing sites. Our method successfully reduced GNSS offset errors from up-to 16 m to below 0.5 m on multiple flights. Furthermore, we obtain globally consistent maps w.r.t. prior 3D geospatial models., Comment: Presented at IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), New York City, USA, November 2024

In medical reporting, the accuracy of radiological reports, whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper: image-conditioned autocorrection of inaccuracies within these reports. Using the MIMIC-CXR dataset, we first intentionally introduce a diverse range of errors into reports. Subsequently, we propose a two-stage framework capable of pinpointing these errors and then making corrections, simulating an \textit{autocorrection} process. This method aims to address the shortcomings of existing automated medical reporting systems, like factual errors and incorrect conclusions, enhancing report reliability in vital healthcare applications. Importantly, our approach could serve as a guardrail, ensuring the accuracy and trustworthiness of automated report generation. Experiments on established datasets and state of the art report generation models validate this method's potential in correcting medical reporting errors.

This paper focuses on the task of measuring and forecasting incivility in conversations following replies to hate speech. Identifying replies that steer conversations away from hatred and elicit civil follow-up conversations sheds light into effective strategies to engage with hate speech and proactively avoid further escalation. We propose new metrics that take into account various dimensions of antisocial and prosocial behaviors to measure the conversation incivility following replies to hate speech. Our best metric aligns with human perceptions better than prior work. Additionally, we present analyses on a) the language of antisocial and prosocial posts, b) the relationship between antisocial or prosocial posts and user interactions, and c) the language of replies to hate speech that elicit follow-up conversations with different incivility levels. We show that forecasting the incivility level of conversations following a reply to hate speech is a challenging task. We also present qualitative analyses to identify the most common errors made by our best model., Comment: The 19th International AAAI Conference on Web and Social Media (ICWSM 2025) Accepted

Solar type V radio bursts are associated with type III bursts. Several processes have been proposed to interpret the association, electron distribution, and emission. We present the observation of a unique type V event observed by e-CALLISTO on 7 May 2021. The type V radio emission follows a group of U bursts. Unlike the unpolarized U bursts, the type V burst is circularly polarized, leaving room for a different emission process. Its starting edge drifts to higher frequency four times slower than the descending branch of the associated U burst. The type V processes seem to be ruled by electrons of lower energy. The observations conform to a coherent scenario where a dense electron beam drives the two-stream instability (causing type III emission) and, in the nonlinear stage, becomes unstable to another instability, previously known as the electron firehose instability (EFI). The secondary instability scatters some beam electrons into velocities perpendicular to the magnetic field and produces, after particle loss, a trapped distribution prone to electron cyclotron masering (ECM). A reduction in beaming and the formation of an isotropic halo are predicted for electron beams continuing to interplanetary space, possibly observable by Parker Solar Probe and Solar Orbiter., Comment: 12 pages, 5 figures

We consider a setting in which an evolving surface is implicitly characterized as the zero level of a level set function. Such an implicit surface does not encode any information about the path of a single point on the evolving surface. In the literature different approaches for determining a velocity that induces corresponding paths of points on the surface have been proposed. One of these is based on minimization of the strain energy functional. This then leads to a constrained minimization problem, which has a corresponding equivalent formulation as a saddle point problem. The main topic of this paper is a detailed analysis of this saddle point problem and of a finite element discretization of this problem. We derive well-posedness results for the continuous and discrete problems and optimal error estimates for a finite element discretization that uses standard $H^1$-conforming finite element spaces., Comment: 24 pages, 9 figures

The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems can be approached in two fundamentally distinct ways: through either a discriminative or a generative method. In general, it is unclear which of these two approaches is most suitable for a given problem. The choice is expected to depend on factors such as the availability of system knowledge, dataset size, desired accuracy, computational resources, and other considerations. In this work, we answer the question of how one should approach the solution of phase-classification problems by performing a numerical case study on the thermal phase transition in the classical two-dimensional square-lattice ferromagnetic Ising model., Comment: 11+5 pages, 4+4 figures

Functional magnetic resonance imaging (fMRI) is extensively used in clinical and preclinical settings to study brain function, however, fMRI data is inherently noisy due to physiological processes, hardware, and external noise. Denoising is one of the main preprocessing steps in any fMRI analysis pipeline. This process is challenging in preclinical data in comparison to clinical data due to variations in brain geometry, image resolution, and low signal-to-noise ratios. In this paper, we propose a structure-preserved algorithm based on a 3D Wasserstein generative adversarial network with a 3D dense U-net based discriminator called, 3D U-WGAN. We apply a 4D data configuration to effectively denoise temporal and spatial information in analyzing preclinical fMRI data. GAN-based denoising methods often utilize a discriminator to identify significant differences between denoised and noise-free images, focusing on global or local features. To refine the fMRI denoising model, our method employs a 3D dense U-Net discriminator to learn both global and local distinctions. To tackle potential over-smoothing, we introduce an adversarial loss and enhance perceptual similarity by measuring feature space distances. Experiments illustrate that 3D U-WGAN significantly improves image quality in resting-state and task preclinical fMRI data, enhancing signal-to-noise ratio without introducing excessive structural changes in existing methods. The proposed method outperforms state-of-the-art methods when applied to simulated and real data in a fMRI analysis pipeline.

Correctness proofs for floating point programs are difficult to verify. To simplify the task, a similar, but less complex system, known as logarithmic arithmetic can be used. The Boyer-Moore Theorem Prover, NQTHM, mechanically verified the correctness of a simple implementation of logarithmic arithmetic. It also verified some useful theorems about accumulated relative error bounds for addition, multiplication and division in this logarithmic number system. These theorems were used to verify a program that approximates e^x using a truncated Taylor series. Axioms that characterize the finite precision of the logarithmic system using a rational base, b, were shown by the prover to be satisfiable for any choice of 1 < b < 2. The prover verified the correctness of a function for converting an arbitrary rational value to a logarithmic representation. It also verified that multiplication and division implementations produce exact results for exact inputs, and that addition implementation produces a result as accurate as possible for exact inputs. When these operations are used in combination by a program, such as evaluating a polynomial, the relative error increases in a way that can be bounded by simple expressions, referred to here as tolerances. Several mechanically verified theorems about tolerances allow us to construct mechanically verified proofs about logarithmic arithmetic programs. Although similar to interval arithmetic, tolerances are especially suited to logarithmic arithmetic., Comment: 21 pages, Originally a 1994 technical report from University of Wyoming and Computational Logic, Inc

Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional physics-based systems, such as the Integrated Forecast System (IFS), across several global metrics while requiring fewer computational resources. However, existing AI NWP models face limitations related to training datasets and timestep choices, often resulting in artifacts that reduce model performance. To address these challenges, we introduce the Community Research Earth Digital Intelligence Twin (CREDIT) framework, developed at NSF NCAR. CREDIT provides a flexible, scalable, and user-friendly platform for training and deploying AI-based atmospheric models on high-performance computing systems. It offers an end-to-end pipeline for data preprocessing, model training, and evaluation, democratizing access to advanced AI NWP capabilities. We demonstrate CREDIT's potential through WXFormer, a novel deterministic vision transformer designed to predict atmospheric states autoregressively, addressing common AI NWP issues like compounding error growth with techniques such as spectral normalization, padding, and multi-step training. Additionally, to illustrate CREDIT's flexibility and state-of-the-art model comparisons, we train the FUXI architecture within this framework. Our findings show that both FUXI and WXFormer, trained on six-hourly ERA5 hybrid sigma-pressure levels, generally outperform IFS HRES in 10-day forecasts, offering potential improvements in efficiency and forecast accuracy. CREDIT's modular design enables researchers to explore various models, datasets, and training configurations, fostering innovation within the scientific community.

Many cultural institutions have made large digitized visual collections available online, often under permissible re-use licences. Creating interfaces for exploring and searching these collections is difficult, particularly in the absence of granular metadata. In this paper, we introduce a method for using state-of-the-art multimodal large language models (LLMs) to enable an open-ended, explainable search and discovery interface for visual collections. We show how our approach can create novel clustering and recommendation systems that avoid common pitfalls of methods based directly on visual embeddings. Of particular interest is the ability to offer concrete textual explanations of each recommendation without the need to preselect the features of interest. Together, these features can create a digital interface that is more open-ended and flexible while also being better suited to addressing privacy and ethical concerns. Through a case study using a collection of documentary photographs, we provide several metrics showing the efficacy and possibilities of our approach., Comment: 16 pages, CHR 2024: Computational Humanities Research Conference, December 4 - 6, 2024, Aarhus University, Denmark

In the 1970s, the United States Environmental Protection Agency sponsored Documerica, a large-scale photography initiative to document environmental subjects nation-wide. While over 15,000 digitized public-domain photographs from the collection are available online, most of the images were scanned from damaged copies of the original prints. We present and evaluate a modified histogram matching technique based on the underlying chemistry of the prints for correcting the damaged images by using training data collected from a small set of undamaged prints. The entire set of color-adjusted Documerica images is made available in an open repository., Comment: 11 pages, CHR 2024: Computational Humanities Research Conference, December 4 - 6, 2024, Aarhus University, Denmark

Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and predict anomalies in critical systems. AI models have been trained to detect system faults, improving predictive maintenance efficiency. Typically there is a lack of fault data to train these models, due to organizations working to keep fault occurrences and down time to a minimum. For newly installed systems, no fault data exists since they have yet to fail. By using diffusion models for synthetic data generation, the complex training datasets for these predictive models can be supplemented with high level synthetic fault data to improve their performance in anomaly detection. By learning the relationship between healthy and faulty data in similar systems, a diffusion model can attempt to apply that relationship to healthy data of a newly installed system that has no fault data. The diffusion model would then be able to generate useful fault data for the new system, and enable predictive models to be trained for predictive maintenance. The following paper demonstrates a system for generating useful, multivariate synthetic data for predictive maintenance, and how it can be applied to systems that have yet to fail.

For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore, prevent costs associated with system failures. Typically, traditional PdM approaches are not transferable or multimodal. This work examines the use of Large Language Models (LLMs) for anomaly detection in complex and dynamic manufacturing systems. The research aims to improve the transferability of anomaly detection models by leveraging Large Language Models (LLMs) and seeks to validate the enhanced effectiveness of the proposed approach in data-sparse industrial applications. The research also seeks to enable more collaborative decision-making between the model and plant operators by allowing for the enriching of input series data with semantics. Additionally, the research aims to address the issue of concept drift in dynamic industrial settings by integrating an adaptability mechanism. The literature review examines the latest developments in LLM time series tasks alongside associated adaptive anomaly detection methods to establish a robust theoretical framework for the proposed architecture. This paper presents a novel model framework (AAD-LLM) that doesn't require any training or finetuning on the dataset it is applied to and is multimodal. Results suggest that anomaly detection can be converted into a "language" task to deliver effective, context-aware detection in data-constrained industrial applications. This work, therefore, contributes significantly to advancements in anomaly detection methodologies.

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: Presented at 15th Symposium in Information and Human Language Technology (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