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Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://gregorconsortium.org/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.

In this paper we introduce the target flow -- a specific curve shortening flow with an ambient forcing term -- that, given an embedded (not necessarily convex) target curve, will attempt to evolve a given source curve to that target. The motivation for this flow is to address a question of Yau. Our main result is that the target flow with uniformly normal graphical data converges smoothly to the target, broadening the class of known sources and targets such that Yau's problem has a solution., Comment: 14 pages, 1 figure

In this paper we consider the anisotropic curve shortening flow in the plane in the presence of an ambient force. We consider force fields in which all their derivatives are bounded in the $L^{\infty}$ sense. We prove that closed embedded curves that have a minimum of curvature sufficiently large shrink to round points. The method of proof follows along the same lines of Gage and Hamilton, in that we study a rescaling to prove curvature bounds. We additionally show that the influence of an ambient force field may make such a result untrue, by giving sufficient conditions on the ambient field that ensures eventual non-convexity of an initially convex curve evolving under the flow., Comment: 37 pages

This First Look report presents data on public elementary and secondary education revenues and expenditures at the local education agency (LEA) or school district level for fiscal year (FY) 2022. Specifically, this report includes the following types of school district finance data: (1) revenue, current expenditure, and capital outlay expenditure totals; (2) revenues by source; (3) current expenditures by function and object; (4) revenues and current expenditures per pupil; and (5) revenues and expenditures from COVID-19 Federal Assistance Funds. This First Look report focuses on education revenues and expenditures at the school district level. The finance data used in this report are from the School District Finance Survey (F-33), 2F 3 a component of the Common Core of Data (CCD). The CCD is a group of annual public elementary/secondary data collections administered by NCES. The F-33 survey consists of LEA-level finance data submitted annually to the U.S. Census Bureau (Census Bureau) by state education agencies (SEAs) in the 50 states and the District of Columbia. SEAs report financial data covering services that provide or support prekindergarten through high school for public education for a variety of types of LEAs. These LEAs include regular school districts, independent charter school districts, as well as a substantial number of administrative and operating LEAs that are unlike typical school districts (e.g., education service agencies that provide specialized education services for school districts, such as vocational and other specialized education services for school districts). The purpose of this First Look report is to introduce new data through the presentation of tables containing descriptive information. The selected findings chosen for this report demonstrate the range of information available when using F-33 data files and are not intended to emphasize any particular issue(s).

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schr\"{o}dinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power makes FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method's inherent stochastic nature. This study represents a community-wide effort to address the titular question, affirming that: Yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move (TM), the determinant locality approximation (DLA), or the determinant T-move (DTM) schemes, while the older locality approximation (LA) leads to considerable variability in results. This work lays the foundation to establish accurate and reproducible FN-DMC estimates for all future studies across applications in materials science, physics, chemistry, and biology.

We compute families of solutions to the Einstein vacuum equations of the type of Brill waves, but with slow fall-off towards spatial infinity. We prove existence and uniqueness of solutions for physical data and numerically construct some representative solutions. We numerically construct an explicit example with slow-off which does not exhibit antipodal symmetry at spatial infinity., Comment: 12 pages, 6 figures

We utilize the cosmological volume simulation, FIREbox, to investigate how a galaxy's environment influences its size and dark matter content. Our study focuses on approximately 1,200 galaxies (886 central and 332 satellite halos) in the low-mass regime, with stellar masses between $10^6$ to $10^9$ $M_{\odot}$. We analyze the size-mass relation ($r_{50} - M_{\star}$), inner dark matter mass-stellar mass ($M^{50}_{\rm DM} - M_{\star}$) relation, and the halo mass-stellar mass ($M_{\rm halo} - M_{\star}$) relation. At fixed stellar mass, we find the galaxies experiencing stronger tidal influences, indicated by higher Perturbation Indices (PI $>$ 1) are generally larger and have lower masses relative to their counterparts with lower Perturbation Indices (PI $<$ 1). Applying a Random Forest regression model, we show that both the environment (PI) and halo mass ($M_{rm halo}$) are significant predictors of a galaxy's relative size and dark matter content. Notably, because $M_{\rm halo}$ is also strongly affected by the environment, our findings indicate that environmental conditions not only influence galactic sizes and relative inner dark matter content directly, but also indirectly through their impact on halo mass. Our results highlight a critical interplay between environmental factors and halo mass in shaping galaxy properties, affirming the environment as a fundamental driver in galaxy formation and evolution., Comment: 17 pages, 9 figures; submitted to ApJ

Starting from the Izergin-Korepin 19-vertex model in the quadrant, we introduce two families of rational multivariate functions $F_S$ and $G_S$; these are in direct analogy with functions introduced by Borodin in the context of the higher-spin 6-vertex model in the quadrant. We prove that $F_S(x_1,\dots,x_N;z)$ and $G_S(y_1,\dots,y_M;z)$ are symmetric functions in their alphabets $(x_1,\dots,x_N)$ and $(y_1,\dots,y_M)$, and pair together to yield a Cauchy identity. Both properties are consequences of the Yang-Baxter equation of the model. We show that, in an appropriate limit of the spectral parameters $z$, $F_S$ tends to a stable symmetric function denoted $H_S$. This leads to a simplified version of the Cauchy identity with a fully factorized kernel, and suggests self-duality of the functions $H_S$. We obtain a symmetrization formula for the function $F_S(x_1,\dots,x_N;z)$, which exhibits its symmetry in $(x_1,\dots,x_N)$. In contrast to the 6-vertex model, where $F^{6{\rm V}}_S(x_1,\dots,x_N;z)$ is cast as a sum over the symmetric group $\mathfrak{S}_N$, the symmetrization formula in the 19-vertex model is over a larger set of objects that we define; we call these objects 2-permutations. As a byproduct of the proof of our symmetrization formula, we obtain explicit formulas for the monodromy matrix elements of the 19-vertex model in a basis that renders them totally spatially symmetric., Comment: 83 pages

At sub-Kelvin temperatures, two-level systems (TLS) present in amorphous dielectrics source a permittivity noise, degrading the performance of a wide range of devices using superconductive resonators such as qubits or kinetic inductance detectors. We report here on measurements of TLS noise in hydrogenated amorphous silicon (a-Si:H) films deposited by plasma-enhanced chemical vapor deposition (PECVD) in superconductive lumped-element resonators using parallel-plate capacitors (PPCs). The TLS noise results presented in this article for two recipes of a-Si:H improve on the best achieved in the literature by a factor >5 for a-Si:H and other amorphous dielectrics and are comparable to those observed for resonators deposited on crystalline dielectrics.

Let $R$ be a regular $F$-finite ring of prime characteristic $p$. We prove that the injective dimension of every unit Frobenius module $M$ in the category of unit Frobenius modules is at most $\operatorname{dim}(\operatorname{Supp}_R(M))+1$. We further show that for unit Cartier modules the same bound holds over any noetherian $F$-finite ring $A$ of prime characteristic $p$. This shows that $\dim A+1$ is a uniform upper bound for the injective dimension of any unit Cartier module over a noetherian $F$-finite ring $A$., Comment: 10 pages

We introduce the Habiro ring of a number field $\mathbb{K}$ and modules over it graded by $K_3(\mathbb{K})$. Elements of these modules are collections of power series at each complex root of unity that arithmetically glue with each other after applying a Frobenius endomorphism, and after dividing at each prime by a collection of series that depends solely on an element of the Bloch group. We prove that the perturbative Chern-Simons invariants of knots and 3-manifolds are elements of these modules and identify these elements with expansions of certain admissible series of Kontsevich-Soibelman at roots of unity, suggesting that some Donaldson-Thomas invariants have arithmetic meaning and that some elements of the Habiro ring of a number field have enumerative meaning., Comment: 71 pages

This work presents a generative pre-trained transformer (GPT) designed for modeling financial time series. The GPT functions as an order generation engine within a discrete event simulator, enabling realistic replication of limit order book dynamics. Our model leverages recent advancements in large language models to produce long sequences of order messages in a steaming manner. Our results demonstrate that the model successfully reproduces key features of order flow data, even when the initial order flow prompt is no longer present within the model's context window. Moreover, evaluations reveal that the model captures several statistical properties, or 'stylized facts', characteristic of real financial markets and broader macro-scale data distributions. Collectively, this work marks a significant step toward creating high-fidelity, interactive market simulations., Comment: 13 pages, 8 figures

Data access -- or the availability of new and archival data for use by the larger community -- is key for scientific advancement. How data is presented, searched, and formatted determines accessibility and it can be difficult to find a solution that fits the needs of a given subdiscipline. We present a generalized roadmap for developing a specialty astronomy database with web application based on the development of the SpExoDisks (Spectra of Exoplanet forming Disks) database (spexodisks.com), which provides infrared spectra of protoplanetary disks. Expertise in an astronomy subdiscipline can provide two necessary components for creating a database: access to a large volume of specialized data and knowledge of how that data should be presented to the community. However, there are a variety of steps and decisions for database development that can fall outside astronomy expertise. Here we offer generalized discussions on design and process that are accompanied by real-world examples from the SpExoDisks developer team and website. Starting from the database portal design and data organization, we demonstrate on-demand data distribution and query using publicly accessible database software. These systems support interactive visualizations such that users can explore spectra directly from their browsers. We also offer details that show how the technical concepts in SpExoDisks are implemented, particularly emphasizing sustainability and long-term management of the codebase and processes. Finally, we illustrate the utility that a specialty website can offer to the community by providing a specific example of how the combined spectra from SpExoDisks can enhance our understanding of protoplanetary disks., Comment: 25 pages, 2 appendices, 6 figures, 1 table, accepted to PASP

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

We present supernova (SN) 2023ufx, a unique Type IIP SN with the shortest known plateau duration ($t_\mathrm{PT}$ $\sim$47 days), a luminous V-band peak ($M_{V}$ = $-$18.42 $\pm$ 0.08 mag), and a rapid early decline rate ($s1$ = 3.47 $\pm$ 0.09 mag (50 days)$^{-1}$). By comparing observed photometry to a hydrodynamic MESA+STELLA model grid, we constrain the progenitor to be a massive red supergiant with M$_\mathrm{ZAMS}$ $\simeq$19 - 25 M$_{\odot}$. Independent comparisons with nebular spectral models also suggest an initial He-core mass of $\sim$6 M$_{\odot}$, and thus a massive progenitor. For a Type IIP, SN 2023ufx produced an unusually high amount of nickel ($^{56}$Ni) $\sim$0.14 $\pm$ 0.02 M$_{\odot}$, during the explosion. We find that the short plateau duration in SN 2023ufx can be explained with the presence of a small hydrogen envelope (M$_\mathrm{H_\mathrm{env}}$ $\simeq$1.2 M$_{\odot}$), suggesting partial stripping of the progenitor. About $\simeq$0.09 M$_{\odot}$ of CSM through mass loss from late-time stellar evolution of the progenitor is needed to fit the early time ($\lesssim$10 days) pseudo-bolometric light curve. Nebular line diagnostics of broad and multi-peak components of [O I] $\lambda\lambda$6300, 6364, H$\alpha$, and [Ca II] $\lambda \lambda$7291, 7323 suggest that the explosion of SN 2023ufx could be inherently asymmetric, preferentially ejecting material along our line-of-sight., Comment: Submitted to ApJ, 30 pages, 19 figures

We prove conjectures of Garoufalidis-Gu-Mari\~no that perturbative series associated with the hyperbolic knots $4_1$ and $5_2$ are resurgent and Borel summable. In the process, we give an algorithm that can be used to explicitly compute the Borel-Laplace resummation as a combination of state integrals of Andersen-Kashaev. This gives a complete description of the resurgent structure in these examples and allows for explicit computations of Stokes constants., Comment: 44 pages, 32 figures

Prime-Cam, one of the primary instruments for the Fred Young Submillimeter Telescope (FYST) developed by the CCAT Collaboration, will house up to seven instrument modules, with the first operating at 280 GHz. Each module will include three arrays of superconducting microwave kinetic inductance detectors (KIDs). The first KID array fabricated for the 280 GHz module uses titanium-nitride (TiN) as the superconducting material and has 3,456 individual detectors, while the other two arrays use aluminum. This paper presents the design and laboratory characterization of the 280 GHz TiN array, which is cooled below its critical temperature to ~0.1 K and read out over six RF feedlines. LED mapping, a technique for matching the measured resonant frequency of a detector to its physical position, was performed on the array so that the results can be used to lithographically trim the KID capacitors and increase the yield of the array by reducing frequency collisions. We present the methods and results of LED mapping the 280 GHz TiN KID array before deployment on FYST., Comment: 4 pages, 4 figures, submitted to IEEE Transactions on Applied Superconductivity (IEEE TAS)

metasnf is an R package that enables users to apply meta clustering, a method for efficiently searching a broad space of cluster solutions by clustering the solutions themselves, to clustering workflows based on similarity network fusion (SNF). SNF is a multi-modal data integration algorithm commonly used for biomedical subtype discovery. The package also contains functions to assist with cluster visualization, characterization, and validation. This package can help researchers identify SNF-derived cluster solutions that are guided by context-specific utility over context-agnostic measures of quality., Comment: 72 pages, 22 figures, submitted to Journal of Statistical Software

The Particle X-ray Coincidence Technique (PXCT) was originally developed to measure average lifetimes in the $10^{-17}-10^{-15}$~s range for proton-unbound states populated by electron capture (EC). We have designed and built the Lifetimes and Branching Ratios Apparatus (LIBRA) to be used in the stopped-beam area at the Facility for Rare Isotope Beams that extends PXCT to measure both lifetimes and decay branching ratios of resonances populated by EC/$\beta^+$ decay. The first application of LIBRA aims to obtain essential nuclear data from $^{60}$Ga EC/$\beta^+$ decay to constrain the thermonuclear rates of the $^{59}$Cu$(p,\gamma)^{60}$Zn and $^{59}$Cu$(p,\alpha)^{56}$Ni reactions, and in turn, the strength of the NiCu nucleosynthesis cycle, which is predicted to significantly impact the modeling of Type I X-ray burst light curves and the composition of the burst ashes. Detailed theoretical calculations, Monte Carlo simulations, and performance tests with radioactive sources have been conducted to validate the feasibility of employing LIBRA for the $^{60}$Ga experiment. The method introduced with LIBRA has the potential to measure nearly all essential ingredients for thermonuclear reaction rate calculations in a single experiment, in the absence of direct measurements, which are often impractical for radioactive reactants.

Panel data arise when time series measurements are collected from multiple, dynamically independent but structurally related systems. In such cases, each system's time series can be modeled as a partially observed Markov process (POMP), and the ensemble of these models is called a PanelPOMP. If the time series are relatively short, statistical inference for each time series must draw information from across the entire panel. Every time series has a name, called its unit label, which may correspond to an object on which that time series was collected. Differences between units may be of direct inferential interest or may be a nuisance for studying the commonalities. The R package panelPomp supports analysis of panel data via a general class of PanelPOMP models. This includes a suite of tools for manipulation of models and data that take advantage of the panel structure. The panelPomp package currently emphasizes recent advances enabling likelihood-based inference via simulation-based algorithms. However, the general framework provided by panelPomp supports development of additional, new inference methodology for panel data.

Embedded information displays (EIDs) are becoming increasingly ubiquitous on home appliances and devices such as microwaves, coffee machines, fridges, or digital thermostats. These displays are often multi-purpose, functioning as interfaces for selecting device settings, communicating operating status using simple visualizations, and displaying notifications. However, their usability for people in the late adulthood (PLA) development stage is not well-understood. We report on two focus groups with PLA (n = 11, ages 76-94) from a local retirement community. Participants were shown images of everyday home electronics and appliances, answering questions about their experiences using the EIDs. Using open coding, we qualitatively analyzed their comments to distill key themes regarding how EIDs can negatively affect PLA's ability to take in information (e.g., poor labels) and interact with these devices (e.g., unintuitive steps) alongside strategies employed to work around these issues. We argue that understanding the equitable design and communication of devices' functions, operating status, and messages is important for future information display designers. We hope this work stimulates further investigation into more equitable EID design., Comment: 5 pages, 1 figure, accepted to the 1st Workshop on Accessible Visualization at IEEE VIS '24

Humans naturally obtain intuition about the interactions between and the stability of rigid objects by observing and interacting with the world. It is this intuition that governs the way in which we regularly configure objects in our environment, allowing us to build complex structures from simple, everyday objects. Robotic agents, on the other hand, traditionally require an explicit model of the world that includes the detailed geometry of each object and an analytical model of the environment dynamics, which are difficult to scale and preclude generalization. Instead, robots would benefit from an awareness of intuitive physics that enables them to similarly reason over the stable interaction of objects in their environment. Towards that goal, we propose StackGen, a diffusion model that generates diverse stable configurations of building blocks matching a target silhouette. To demonstrate the capability of the method, we evaluate it in a simulated environment and deploy it in the real setting using a robotic arm to assemble structures generated by the model.

Many climate processes are characterized using large systems of nonlinear differential equations; this, along with the immense amount of data required to parameterize complex interactions, means that Earth-System Model (ESM) simulations may take weeks to run on large clusters. Uncertainty quantification may require thousands of runs, making ESM simulations impractical for preliminary assessment. Alternatives may include simplifying the processes in the model, but recent efforts have focused on using machine learning to complement these models or even act as full surrogates. \textit{We leverage machine learning, specifically fully-connected neural networks (FCNNs) and graph convolutional neural networks (GCNNs), to enable rapid simulation and uncertainty quantification in order to inform more extensive ESM simulations.} Our surrogate simulated 80 years in approximately 310 seconds on a single A100 GPU, compared to weeks for the ESM model while having mean temperature errors below $0.1^{\circ}C$ and maximum errors below $2^{\circ}C$., Comment: 10 pages, 8 figures

The Epoch of Reionization Spectrometer (EoR-Spec) is an upcoming Line Intensity Mapping (LIM) instrument designed to study the evolution of the early universe (z = 3.5 to 8) by probing the redshifted [CII] 158 $\mu$m fine-structure line from aggregates of galaxies. The [CII] emission is an excellent tracer of star formation since it is the dominant cooling line from neutral gas heated by OB star light and thus can be used to probe the reionization of the early Universe due to star formation. EoR-Spec will be deployed on Prime-Cam, a modular direct-detection receiver for the 6-meter Fred Young Submillimeter Telescope (FYST), currently under construction by CPI Vertex Antennentechnik GmbH and to be installed near the summit of Cerro Chajnantor in the Atacama Desert. This instrument features an image plane populated with more than 6500 Microwave Kinetic Inductance Detectors (MKIDs) that are illuminated by a 4-lens optical design with a cryogenic, scanning Fabry-Perot Interferometer (FPI) at the pupil of the optical system. The FPI is designed to provide a spectral resolving power of $R\sim100$ over the full spectral range of 210--420 GHz. EoR-Spec will tomographically survey the E-COSMOS and E-CDFS fields with a depth of about 4000 hours over a 5 year period. Here we give an update on EoR-Spec's final mechanical/optical design and the current status of fabrication, characterization and testing towards first light in 2026., Comment: Presented at SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy XII

We study $L^p\rightarrow L^q(V^r_E)$ variation semi-norm estimates for the spherical averaging operator, where $E\subset [1,2]$., Comment: 25 pages, 3 figures

The R package panelPomp supports analysis of panel data via a general class of partially observed Markov process models (PanelPOMP). This package tutorial describes how the mathematical concept of a PanelPOMP is represented in the software and demonstrates typical use-cases of panelPomp. Monte Carlo methods used for POMP models require adaptation for PanelPOMP models due to the higher dimensionality of panel data. The package takes advantage of recent advances for PanelPOMP, including an iterated filtering algorithm, Monte Carlo adjusted profile methodology and block optimization methodology to assist with the large parameter spaces that can arise with panel models. In addition, tools for manipulation of models and data are provided that take advantage of the panel structure.

Barred galaxies exhibit boxy/peanut or X-shapes (BP/X) protruding from their disks in edge-on views. Two types of BP/X morphologies exist depending on whether the X-wings meet at the center (CX) or are off-centered (OX). Orbital studies indicate that various orbital types can generate X-shaped structures. Here, we provide a classification approach that identifies the specific orbit families responsible for generating OX and CX-shaped structures. Applying this approach to three different N-body bar models, we show that both OX and CX structures are associated with the x1 orbit family, but OX-supporting orbits possess higher angular momentum (closer to x1 orbits) than orbits in CX structures. Consequently, as the bar slows down the contribution of higher angular momentum OX-supporting orbits decreases and that of lower angular momentum orbits increases resulting in an evolution of the morphology from OX to CX. If the bar does not slow down, the shape of the BP/X structure and the fractions of OX/CX supporting orbits remain substantially unchanged. Bars that do not undergo buckling but that do slow down initially show the OX structure and are dominated by high angular momentum orbits, transitioning to a CX morphology. Bars that buckle exhibit a combination of both OX and CX supporting orbits immediately after the buckling, but become more CX dominated as their pattern speed decreases. This study demonstrates that the evolution of BP/X morphology and orbit populations strongly depends on the evolution of the bar angular momentum., Comment: 16 pages, 12 figures, Accepted for publication in ApJ

Prime-Cam, a first-generation science instrument for the Atacama-based Fred Young Submillimeter Telescope, is being built by the CCAT Collaboration to observe at millimeter and submillimeter wavelengths using kinetic inductance detectors (KIDs). Prime-Cam's 280 GHz instrument module will deploy with two aluminum-based KID arrays and one titanium nitride-based KID array, totaling approximately 10,000 detectors at the focal plane, all of which have been fabricated and are currently undergoing testing. One complication of fielding large arrays of KIDs under dynamic loading conditions is tuning the detector tone powers to maximize signal-to-noise while avoiding bifurcation due to the nonlinear kinetic inductance. For aluminum-based KIDs, this is further complicated by additional nonlinear effects which couple tone power to resonator quality factors and resonant frequencies. While both nonequilibrium quasiparticle dynamics and two-level system fluctuations have been shown to give rise to qualitatively similar distortions, modeling these effects alongside nonlinear kinetic inductance is inefficient when fitting thousands of resonators on-sky with existing models. For this reason, it is necessary to have a detailed understanding of the nonlinear effects across relevant detector loading conditions, including how they impact on on-sky noise and how to diagnose the detector's relative performance. We present a study of the competing nonlinearities seen in Prime-Cam's 280 GHz aluminum KIDs, with a particular emphasis on the resulting distortions to the resonator line shape and how these impact detector parameter estimation., Comment: 10 pages, 4 figures, Conference proceedings from SPIE Astronomical Telescopes + Instrumentation (AS24)

We provide the first construction of overhanging gravity water waves having the approximate form of a disk joined to a strip by a thin neck. The waves are solitary with constant vorticity, and exist when an appropriate dimensionless gravitational constant $g>0$ is sufficiently small. Our construction involves combining three explicit solutions to related problems: a disk of fluid in rigid rotation, a linear shear flow in a strip, and a rescaled version of an exceptional domain discovered by Hauswirth, H\'elein, and Pacard \cite{hauswirth-helein-pacard}. The method developed here is related to the construction of constant mean curvature surfaces through gluing., Comment: 93 pages

We present an advanced $\textit{ab initio}$ quantum Monte Carlo (QMC) calculation of the ground state of undoped CaCuO$_2$. We extend the traditional single-determinant Slater-Jastrow approach to include multi-determinant wave functions, inhomogeneous Jastrow factors, and orbital optimization. Our results demonstrate not only an improvement in the variational bound of the ground state energy -- 2.3 eV per formula unit lower than previous state of the art techniques -- but also confirm the presence of spin fluctuations in multi-determinant wave functions in a strongly correlated cuprate system, which is integral to understanding high-$T_c$ superconductivity. This is the first demonstration of capturing spin fluctuations in QMC wave functions on a cuprate, establishing the groundwork for new studies on doped cuprates in the superconducting state, where spin fluctuations require more accurate characterization., Comment: 9 pages, 6 figures

The progenitor system(s) as well as the explosion mechanism(s) of thermonuclear (Type Ia) supernovae are long-standing issues in astrophysics. Here we present ejecta masses and other physical parameters for 28 recent Type Ia supernovae inferred from multiband photometric and optical spectroscopic data. Our results confirm that the majority of SNe Ia show {\it observable} ejecta masses below the Chandrasekhar-limit (having a mean $M_{\rm ej} \approx 1.1 \pm 0.3$ M$_\odot$), consistent with the predictions of recent sub-M$_{\rm Ch}$ explosion models. They are compatible with models assuming either single- or double-degenerate progenitor configurations. We also recover a sub-sample of supernovae within $1.2 $ M$_\odot$ $< M_{\rm {ej}} < 1.5$ M$_\odot$ that are consistent with near-Chandrasekhar explosions. Taking into account the uncertainties of the inferred ejecta masses, about half of our SNe are compatible with both explosion models. We compare our results with those in previous studies, and discuss the caveats and concerns regarding the applied methodology.

We propose an empirical approach centered on the spectral dynamics of weights -- the behavior of singular values and vectors during optimization -- to unify and clarify several phenomena in deep learning. We identify a consistent bias in optimization across various experiments, from small-scale ``grokking'' to large-scale tasks like image classification with ConvNets, image generation with UNets, speech recognition with LSTMs, and language modeling with Transformers. We also demonstrate that weight decay enhances this bias beyond its role as a norm regularizer, even in practical systems. Moreover, we show that these spectral dynamics distinguish memorizing networks from generalizing ones, offering a novel perspective on this longstanding conundrum. Additionally, we leverage spectral dynamics to explore the emergence of well-performing sparse subnetworks (lottery tickets) and the structure of the loss surface through linear mode connectivity. Our findings suggest that spectral dynamics provide a coherent framework to better understand the behavior of neural networks across diverse settings.

Regular measurement of carbon stock in the world's forests is critical for carbon accounting and reporting under national and international climate initiatives, and for scientific research, but has been largely limited in scalability and temporal resolution due to a lack of ground based assessments. Increasing efforts have been made to address these challenges by incorporating remotely sensed data. We present a new methodology which uses multi-sensor, multi-spectral imagery at a resolution of 10 meters and a deep learning based model which unifies the prediction of above ground biomass density (AGBD), canopy height (CH), canopy cover (CC) as well as uncertainty estimations for all three quantities. The model is trained on millions of globally sampled GEDI-L2/L4 measurements. We validate the capability of our model by deploying it over the entire globe for the year 2023 as well as annually from 2016 to 2023 over selected areas. The model achieves a mean absolute error for AGBD (CH, CC) of 26.1 Mg/ha (3.7 m, 9.9 %) and a root mean squared error of 50.6 Mg/ha (5.4 m, 15.8 %) on a globally sampled test dataset, demonstrating a significant improvement over previously published results. We also report the model performance against independently collected ground measurements published in the literature, which show a high degree of correlation across varying conditions. We further show that our pre-trained model facilitates seamless transferability to other GEDI variables due to its multi-head architecture.

The radial velocity catalog from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) is unique in its simultaneously large volume and high precision as a result of its decade-long survey duration, multiplexing (600 fibers), and spectral resolution of $R \sim 22,500$. However, previous data reductions of APOGEE have not fully realized the potential radial velocity (RV) precision of the instrument. Here we present an RV catalog based on a new reduction of all 2.6 million visits of APOGEE DR17 and validate it against improved estimates for the theoretical RV performance. The core ideas of the new reduction are the simultaneous modeling of all components in the spectra, rather than a separate subtraction of point estimates for the sky, and a marginalization over stellar types, rather than a grid search for an optimum. We show that this catalog, when restricted to RVs measured with the same fiber, achieves noise-limited precision down to 30 m/s and delivers well-calibrated uncertainties. We also introduce a general method for calibrating fiber-to-fiber constant RV offsets and demonstrate its importance for high RV precision work in multi-fiber spectrographs. After calibration, we achieve 47 m/s RV precision on the combined catalog with RVs measured with different fibers. This degradation in precision relative to measurements with only a single fiber suggests that refining line spread function models should be a focus in SDSS-V to improve the fiber-unified RV catalog., Comment: 22 pages, 13 figures, SDSS-V Technical Paper, Submitted to AJ

Laser-plasma accelerators represent a promising technology for future compact accelerating systems, enabling the acceleration of tens of pC to above $1\,$GeV over just a few centimeters. Nonetheless, these devices currently lack the stability, beam quality and average current of conventional systems. While many efforts have focused on improving acceleration stability and quality, little progress has been made in increasing the beam's average current, which is essential for future laser-plasma-based applications. In this paper, we investigate a laser-plasma acceleration regime aimed at increasing the beam average current with energies up to few-MeVs, efficiently enhancing the beam charge. We present experimental results on configurations that allow reaching charges of $5-30\,$nC and a maximum conversion efficiency of around $14\,$%. Through comprehensive Particle-In-Cell simulations, we interpret the experimental results and present a detailed study on electron dynamics. From our analysis, we show that most electrons are not trapped in a plasma wave; rather, they experience ponderomotive acceleration. Thus, we prove the laser pulse as the main driver of the particles' energy gain process.

The Arkansas Better Chance (ABC) 2023 Program Longitudinal Study 2009-2022 evaluates the kindergarten through 12th grade outcomes of participation in pre-k services in Arkansas for ABC participants from 2009 to 2021 compared to low-income students not participating in pre-K. ADE DESE data is analyzed to determine outcomes for these two core groups from 2010 to 2022. Kindergarten meal statuses of direct certification and free lunch are in the core comparison. This study shows that compared to No Known Pre-K, the ABC participants consistently have: (1) Higher ACT Aspire on-time proficiency in ELA Literacy, Math, and Science; (2) More days attended in kindergarten through 12th grade; (3) Lower rates of grade level retention; and (4) Higher rates of graduation (a new measure for this 2023 study). The ABC participants of 2009 could have graduated from high school on-time during the 2022 academic year. This 2023 study allowed for the kindergarten rate of graduation to be calculated. The purpose of the ABC program is to offer high quality early education services to children exhibiting developmental and socio-economic risk factors. This study sets the two core comparison groups on those DESE kindergarten students which best represent the contrast between children who received ABC program services and those children who would have qualified for ABC program services but did not participate in ABC. This study evaluates the outcomes of pre-k participation, not an established DESE measure of accountability. The calculations keep the core comparison groups of ABC and NK unchanged. By establishing this core comparison, the impact of the ABC program can be isolated and studied longitudinally for outcomes of Arkansas K-12 public education through high school graduation.

In a recent paper, we have introduced a new model to describe front propagation in bushfires. This model describes temperature diffusion in view of an ignition process induced by an interaction kernel, the effect of the environmental wind and that of the fire wind. This has led to the introduction of a new partial differential equation of evolutionary type, with nonlinear terms both of integral kind and involving the gradient of the solution. This equation is, in a sense, ``hybrid'', since it encodes both analytical and geometric features of the front propagation. This new characteristic makes the equation particularly interesting also from the mathematical point of view, often falling outside the territory already covered by standard methods. In this paper, we start the mathematical treatment of this equation by establishing the short time and global existence theory for this equation.

We introduce a simple mathematical model for bushfires accounting for temperature diffusion in the presence of a combustion term which is activated above a given ignition state. The model also takes into consideration the effect of the environmental wind and of the pyrogenic flow. The simplicity of the model is highlighted from the fact that it is described by a single scalar equation, containing only four terms, making it very handy for rapid and effective numerical simulations which run in real-time. In spite of its simplicity, the model is in agreement with data collected from bushfire experiments in the lab, as well as with spreading of bushfires that have been observed in the real world. Moreover, the equation describing the temperature evolution can be easily linked to a geometric evolution problem describing the level sets of the ignition state., Comment: To appear in SIAM Journal on Applied Mathematics