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OpenSciEd is a set of Creative Commons licensed, standards aligned curriculum and teacher professional learning materials that will be available for grades K-12. Using a storyline model that gives students the responsibility of "figuring out" science phenomena by engaging in science practices and classroom discussion to achieve consensus, OpenSciEd "empowers educators to go beyond traditional science teaching methods." This instructional model is ambitious for teachers, who must serve as facilitators and continually adapt instruction in response to directions students take. Because of this instructional shift, districts must also shift in how they adopt and implement science curriculum. Among the aims of the OpenSciEd Research Community, led by Digital Promise with support from the Carnegie Corporation of New York, is to develop and disseminate resources that support advancement of OpenSciEd-enabled research. Toward this goal, Digital Promise has released this report that synthesizes perspectives from OpenSciEd practitioners on their needs. The report identifies: (1) supports that teachers need to enact OpenSciEd with integrity, engage all their students, and gather evidence of students' standards-based learning outcomes; (2) ways districts can achieve deep, sustained adoption and meet their teachers' needs; and (3) potential research questions and opportunities for innovation that can improve OpenSciEd implementation in districts and classrooms. [This report was produced in collaboration with OpenSciEd.]

Early childhood educators play an essential role in providing child care for families and learning and development supports for young children, yet they have long faced challenges due to low wages. Recognizing this, the District of Columbia (DC) introduced the Early Childhood Pay Equity Fund in 2022. This first-of-its-kind initiative aims to bridge the pay gap between early educators and teachers at public schools, addressing historical inequities and improving recruitment and retention efforts. This report provides an in-depth exploration of early educators' experiences with the Pay Equity Fund during its initial year of implementation. Drawing on data from surveys conducted in May 2023 and follow-up focus groups, the report offers insights into the transition from direct payments to an opt-in payment structure by employers in FY 2024. It covers educators' introduction to the Fund, their application and payment experiences, financial impacts, and perceived benefits for child care programs and the broader early childhood education field.

Nontuition expenses--including housing, food, transportation, and miscellaneous expenses, in addition to books and supplies--constitute a significant financial burden for many students pursuing higher education, even if they receive grant aid to help them pay tuition and fees. Well-designed policy interventions at both the federal and state levels could effectively address the hardships some students face. A large increase in the maximum Pell grant to provide students additional funds for living expenses may seem like a straightforward solution, but that approach is not optimal. Doubling the maximum Pell grant would increase the aid most current full-time recipients receive by the same dollar amount, regardless of the size of their current Pell grant. In other words, it would double the grant for the lowest-income students, who currently receive the maximum, but would increase it by much larger proportions for those whose somewhat higher incomes qualify them for smaller grants. The program's cost would more than double. In this report, we propose alternative design options for a need-based basic living stipend. Awarding two Pell grants, with the second grant explicitly for nontuition expenses, would be less expensive and more targeted to the lowest-income students than doubling the maximum. Our preferred option would be a distinct need-based living stipend supplementing an increased Pell grant. The stipend would be standard, not based on individual cost of attendance, which is an inconsistent metric. The maximum stipend could be lower than the maximum Pell grant. The stipend would decline at a chosen rate as incomes increase. This approach would be most flexible and would be less expensive and better targeted than an increase in the maximum Pell grant that would give the same amount of funding to the lowest-income students. A federal matching program providing incentives to states to provide need-based living stipends would be a useful complement to this federal program and could tailor aid to local circumstances.

This manuscript studies the Gaussian approximation of the coordinate-wise maximum of self-normalized statistics in high-dimensional settings. We derive an explicit Berry-Esseen bound under weak assumptions on the absolute moments. When the third absolute moment is finite, our bound scales as $\log^{5/4}(d)/n^{1/8}$ where $n$ is the sample size and $d$ is the dimension. Hence, our bound tends to zero as long as $\log(d)=o(n^{1/10})$. Our results on self-normalized statistics represent substantial advancements, as such a bound has not been previously available in the high-dimensional central limit theorem (CLT) literature.

Cumulative Games were introduced by Larsson, Meir, and Zick (2020) to bridge some conceptual and technical gaps between Combinatorial Game Theory (CGT) and Economic Game Theory. The partizan ruleset {\sc Robin Hood} is an instance of a Cumulative Game, viz., {\sc Wealth Nim}. It is played on multiple heaps, each associated with a pair of cumulations, interpreted here as wealth. Each player chooses one of the heaps, removes tokens from that heap not exceeding their own wealth, while simultaneously diminishing the other player's wealth by the same amount. In CGT, the {\em temperature} of a {\em disjunctive sum} game component is an estimate of the urgency of moving first in that component. It turns out that most of the positions of {\sc Robin Hood} are {\em hot}. The temperature of {\sc Robin Hood} on a single large heap shows a dichotomy in behavior depending on the ratio of the wealths of the players. Interestingly, this bifurcation is related to Pingala (Fibonacci) sequences and the Golden Ratio $\phi$: when the ratio of the wealths lies in the interval $(\phi^{-1},\phi)$, the temperature increases linearly with the heap size, and otherwise it remains constant, and the mean values has a reciprocal property. It turns out that despite {\sc Robin Hood} displaying high temperatures, playing in the hottest component might be a sub-optimal strategy.

Many common Skyrme functionals present ferromagnetic instabilities or unrealistic density dependence of the spin-1 Landau parameters. To solve these problems, we consider the Skyrme interaction as a density-functional rather than a density-dependent two-body force. This allows us to adjust the spin-dependent terms of the new extended Skyrme functionals of our previous paper [M. Duan and M. Urban, Phys. Rev. C 110, 065806 (2024)] independently without altering the properties of spin saturated matter. The parameters of the spin-dependent terms are determined by fitting the Landau parameters $G_0$ and $G_0^\prime$ in neutron matter and symmetric nuclear matter and the effective-mass splitting of up and down particles in spin polarized matter to the results of microscopic calculations. Using the new parametrizations, called Sky3s and Sky4s, the spin-related properties of nuclear matter are in good agreement with the microscopic results. As an application, we compute response functions and neutrino scattering rates of neutron-star matter with the new functionals having realistic effective masses and Landau parameters., Comment: 9 pages, 8 figures

The development of photonic integrated components for terahertz has become an active and growing research field. Despite its numerous applications, several challenges are still present in hardware design. We demonstrate an on-chip active wavelength division multiplexer (WDM) operating at THz frequencies. The WDM architecture is based on an inverse design topology optimization, which is applied in this case to the active quantum cascade heterostructure material embedded within a polymer in a planarized double metal cavity. Such an approach enables the fabrication of a strongly subwavelength device, with a normalized volume of only $V/\lambda^3 \simeq 0.5$. The WDM input is integrated with a THz quantum cascade laser frequency comb, providing three broadband output ports, ranging from 2.2 THz to 3.2 THz, with $\approx$ 330 GHz bandwidth and a maximum crosstalk of -6 dB. The three ports are outcoupled via integrated broadband patch array antennas with surface emission. Such a device can be also function as a stand-alone element, unlocking complex on-chip signal processing in the THz range

The rapid development of advanced urban air mobility, particularly electric vertical take-off and landing (eVTOL) aircraft, requires interdisciplinary approaches involving the future urban air mobility ecosystem. Operational cost efficiency, regulatory aspects, sustainability, and environmental compatibility must be incorporated directly into the preliminary design of aircraft and across operational and regulatory strategies. In this work, we present a novel multidisciplinary design optimization framework for the preliminary design of eVTOL aircraft. The framework optimizes conventional design elements of eVTOL aircraft over a generic mission and integrates a comprehensive operational cost model to directly capture economic incentives of the designed system through profit modeling for operators. We compare the optimized eVTOL system with various competing road, rail, and air transportation modes in terms of sustainability, cost, and travel time. We investigate four objective-specific eVTOL optimization designs in a broad scenario space, mapping regulatory, technical, and operational constraints to generate a representation of potential urban air mobility ecosystem conditions. The analysis of an optimized profit-maximizing eVTOL, cost-minimizing eVTOL, sustainability-maximizing eVTOL, and a combined figure of merit maximizing eVTOL design highlights significant trade-offs in the area of profitability, operational flexibility, and sustainability strategies. This underlines the importance of incorporating multiple operationally tangential disciplines into the design process., Comment: 54 pages, 24 figures

Modal analysis has long been consolidated as a basic tool to interpret dynamics and build low-order models of mechanical, thermal, and fluid systems. Eigenmodes arising from the spectral decomposition of the underlying linearized dynamics represent spatial patterns in vibration, temperature, or velocity fields associated with simple time dynamics. However, for systems that depend on one or more parameters, eigenmodes obtained for one set of parameter values are not necessarily dynamically relevant in other regions of parameter space. In this work, we formulate a method to obtain an optimal orthogonal basis of eigen-deformation modes (EDMs) that capture eigenmode variations across a range of parameter values. Through numerical examples of common parameterized dynamical systems in engineering, we show that EDMs are useful for parameterized model reduction and to provide physical insight into the effects of parameter changes on the underlying dynamics.

We study the confining and deconfining phases of pure $\mathrm{SU}(3)$ lattice gauge theory with topological data analysis. This provides unique insights into long range correlations of field configurations across the confinement-deconfinement transition. Specifically, we analyze non-trivial structures in electric and magnetic field energy densities as well as Polyakov loop traces and a Polyakov loop-based variant of the topological density. The Betti curves for filtrations based on the electric and magnetic field energy densities reveal signals of electromagnetic dualities. These dualities can be associated with an interchange in the roles of local lumps of electric and magnetic energy densities around the phase transition. Moreover, we show that plaquette susceptibilities can manifest in the geometric features captured by the Betti curves. We also confront the results obtained for $\mathrm{SU}(3)$ with that for $\mathrm{SU}(2)$ and elaborate on the significant differences. Our results demonstrate that topological data analysis can discriminate phase transitions of different order for non-Abelian lattice gauge theories. Moreover, it provides unprecedented insights into the relevant structures in their vicinity., Comment: 13 pages, 8 figures

Li containing transition metal oxides are known as good ionic conductors. Performing classical molecular dynamics simulations, the diffusion behavior of Li ions is investigated in crystalline and amorphous phases with the stoichiometries Li$_2$ZrO$_3$ and LiNbO$_3$. We first demonstrate the stability of the crystal structures for the used interatomic potential model and then analyze the amorphous phases, which result from melt-and-quench simulations, in terms of radial distribution functions. Diffusivities of Li ions in those systems are obtained from a statistical Arrhenius analysis of mean square displacement curves at different temperatures. The crystalline phase of Li$_2$ZrO$_3$ exhibits two well-defined migration mechanisms: vacancy-mediated migration is dominant below and a site exchange of Li ions above a crossover region between about 1700 and 1800 K. The latter mechanism also prevails in the amorphous phases of Li$_2$ZrO$_3$ with a strongly reduced activation energy, which is due to a smaller equilibrium separation of Li ions as in the crystal structure. This migration mechanism is found in amorphous LiNbO$_3$, too., Comment: 18 pages, 6 figures

Artificial neural network (ANN) potentials enable highly accurate atomistic simulations of complex materials at unprecedented scales. Despite their promise, training ANN potentials to represent intricate potential energy surfaces (PES) with transferability to diverse chemical environments remains computationally intensive, especially when atomic force data are incorporated to improve PES gradients. Here, we present an efficient ANN potential training methodology that uses Gaussian process regression (GPR) to incorporate atomic forces into ANN training, leading to accurate PES models with fewer additional first-principles calculations and a reduced computational effort for training. Our GPR-ANN approach generates synthetic energy data from force information in the reference dataset, thus augmenting the training datasets and bypassing direct force training. Benchmark tests on hybrid density-functional theory data for ethylene carbonate (EC) molecules and Li metal-EC interfaces, relevant for lithium metal battery applications, demonstrate that GPR-ANN potentials achieve accuracies comparable to fully force-trained ANNs with a significantly reduced computational overhead. Detailed comparisons show that the method improves both data efficiency and scalability for complex interfaces and heterogeneous environments. This work establishes the GPR-ANN method as a powerful and scalable framework for constructing high-fidelity machine learning interatomic potentials, offering the computational and memory efficiency critical for the large-scale simulations needed for the simulation of materials interfaces., Comment: 32 pages, 7 figures, 20 SI figures

In this paper, we study the past asymptotics of $(2+1)$-dimensional solutions to the Einstein scalar-field Vlasov system which are close to Friedman-Lema\^itre-Robertson-Walker spacetimes on an initial hypersurface diffeomorphic to a closed orientable surface $M$ of arbitrary genus. We prove that such solutions are past causally geodesically incomplete and exhibit stable Kretschmann scalar blow-up in the contracting direction. In particular, they are $C^2$-inextendible towards the past where causal geodesics become incomplete. Moreover, we show that geometry and matter are asymptotically velocity term dominated toward the past, remaining close to their background counterparts. Where the asymptotics do not coincide with those of the isotropic background solution, the momentum support of the Vlasov distribution approaches a smooth one-dimensional subbundle of the mass shell. Compared to previous results in higher dimensions, inhomogeneous terms in the wave and Vlasov equations factor in more strongly in our setting, which a priori creates additional hurdles to establish stability. As a corollary, our main result shows that the Strong Cosmic Censorship conjecture holds for certain polarized $U(1)$-symmetric solutions to the Einstein vacuum equations that emanate from a spatial hypersurface diffeomorphic to $M\times\mathbb{S}^1$., Comment: 45 pages. Comments welcome!

The properties of dilute neutron matter are mostly determined by the s-wave two-body (2N) interaction, while three-body (3N) interactions are suppressed by the Pauli principle. In a previous work, we showed that it can be advantageous to use the renormalization group based effective interactions with cutoffs scaled with the Fermi momentum, especially at low densities. In that case, induced 3N interactions may become important. In this work, we compute the 3N interaction induced by the similarity renormalization group flow of the s-wave 2N interaction. We work in the momentum-space hyperspherical partial wave basis and investigate its convergence properties. Then we study the effect of the induced 3N interaction on the equation of state of dilute neutron matter. We observe that the cutoff dependence of the equation of state is strongly reduced when the effect of induced 3N interaction is included., Comment: 11 pages, 10 figures

Tissue viscoelasticity is becoming an increasingly useful biomarker beyond elasticity and can theoretically be estimated using shear wave elastography (SWE), by inverting the propagation and attenuation characteristics of shear waves. Estimating viscosity is often more difficult than elasticity because attenuation, the main effect of viscosity, leads to poor signal-to-noise ratio of the shear wave motion. In the present work, we provide an alternative to existing methods of viscoelasticity estimation that is robust against noise. The method minimizes the difference between simulated and measured versions of two sets of peaks (twin peaks) in the frequency-wavenumber domain, obtained first by traversing through each frequency and then by traversing through each wavenumber. The slopes and deviation of the twin peaks are sensitive to elasticity and viscosity respectively, leading to the effectiveness of the proposed inversion algorithm for characterizing mechanical properties. This expected effectiveness is confirmed through in silico verification, followed by ex vivo validation and in vivo application, indicating that the proposed approach can be effectively used in accurately estimating viscoelasticity, thus potentially contributing to the development of enhanced biomarkers., Comment: 18 pages, 11 figures

In a laser, the control of its spectral emission depends on the physical dimensions of the optical resonator, limiting it to a set of discrete cavity modes at specific frequencies. Here, we overcome this fundamental limit by demonstrating a monolithic semiconductor laser with a continuously tunable repetition rate from 4 up to 16 GHz, by employing a microwave driving signal that induces a spatiotemporal gain modulation along the entire laser cavity, generating intracavity mode-locked pulses with a continuously tunable group velocity. At the output, frequency combs with continuously tunable mode spacings are generated in the frequency domain, and coherent pulse trains with continuously tunable repetition rates are generated in the time domain. Our results pave the way for fully tunable chip-scale lasers and frequency combs, advantageous for use in a diverse variety of fields, from fundamental studies to applications such as high-resolution and dual-comb spectroscopy., Comment: 9 pages, 5 figures

Antiferromagnets exhibit rapid spin dynamics in a net zero magnetic background which enables novel spintronic applications and interrogation of many-body quantum phenomena. The layered antiferromagnet Sr$_2$IrO$_4$ hosts an exotic spin one-half Mott insulating state with an electronic gap arising from on-site Coulomb repulsion and strong spin-orbit coupling. This makes Sr$_2$IrO$_4$ an interesting candidate to interrogate dynamical attributes of the magnetic order using ultrafast laser pulses. We investigate the magnetization dynamics of Sr$_2$IrO$_4$ following circularly-polarized photoexcitation with below-gap mid-infrared (mid-IR -- 9 $\mu m$) and above-gap near-infrared (near-IR -- 1.3 $\mu m$) pulses. In both cases, we observe excitation of a zone-center coherent magnon mode featuring a 0.5 THz oscillation in the pump-induced Kerr-rotation signal. However, only below-gap excitation exhibits a helicity dependent response and linear (quadratic) scaling of the coherent magnon amplitude with excitation fluence (electric field). Moreover, below-gap excitation has a magnon generation efficiency that is at least two orders of magnitude greater in comparison to above-gap excitation. Our analysis indicates that the helicity dependence and enhanced generation efficiency arises from a unique one-photon two-magnon coupling mechanism for magnon generation. Thus, preferential spin-photon coupling without photoexcitation of electrons permits extremely efficient magnon generation. Our results reveal new possibilities for ultrafast control of antiferromagnets.

Superconductivity usually emerges from a metallic normal state which follows the Fermi-liquid paradigm. If, in contrast, the normal state is a fractionalized non-Fermi liquid, then pairing may either eliminate fractionalization via a Higgs-type mechanism leading to a conventional superconducting state, or pairing can occur in the presence of fractionalization. Here we discuss a simple model for the latter case: Using a combination of perturbation theory and functional renormalization group, we show that the Kitaev--Kondo lattice model displays a fractionalized superconducting phase at weak Kondo coupling. This phase is characterized by Cooper pairing of conventional electronic quasiparticles, coexisting with a spin-liquid background and topological order. Depending on the sign of the Kitaev coupling, we find the pairing to be either of chiral $d$-wave or $p$-wave type for extended doping regions around the van-Hove filling. We discuss applications and extensions., Comment: (7+7 pages, 3+7 figures)

Effectively controlling systems governed by Partial Differential Equations (PDEs) is crucial in several fields of Applied Sciences and Engineering. These systems usually yield significant challenges to conventional control schemes due to their nonlinear dynamics, partial observability, high-dimensionality once discretized, distributed nature, and the requirement for low-latency feedback control. Reinforcement Learning (RL), particularly Deep RL (DRL), has recently emerged as a promising control paradigm for such systems, demonstrating exceptional capabilities in managing high-dimensional, nonlinear dynamics. However, DRL faces challenges including sample inefficiency, robustness issues, and an overall lack of interpretability. To address these issues, we propose a data-efficient, interpretable, and scalable Dyna-style Model-Based RL framework for PDE control, combining the Sparse Identification of Nonlinear Dynamics with Control (SINDy-C) algorithm and an autoencoder (AE) framework for the sake of dimensionality reduction of PDE states and actions. This novel approach enables fast rollouts, reducing the need for extensive environment interactions, and provides an interpretable latent space representation of the PDE forward dynamics. We validate our method on two PDE problems describing fluid flows - namely, the 1D Burgers equation and 2D Navier-Stokes equations - comparing it against a model-free baseline, and carrying out an extensive analysis of the learned dynamics.

The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured molecular space. The junction tree variational autoencoder (JTVAE) has been shown to be an efficient generative model that can be used for suggesting legitimate novel drug-like small molecules with improved properties. While the performance of the generative molecular design (GMD) scheme strongly depends on the initial training data, one can improve its sampling efficiency for suggesting better molecules with enhanced properties by optimizing the latent space. In this work, we propose how mechanistic models - such as pathway models described by differential equations - can be used for effective latent space optimization(LSO) of JTVAEs and other similar models for GMD. To demonstrate the potential of our proposed approach, we show how a pharmacodynamic model, assessing the therapeutic efficacy of a drug-like small molecule by predicting how it modulates a cancer pathway, can be incorporated for effective LSO of data-driven models for GMD.

Families commonly make decisions about child care and early education (CCEE). CCEE supports children's learning and parents' work and education. Families may use CCEE in a child care center, home, or school setting. They may use a licensed program or relatives, friends, or neighbors to provide care. To support families who are looking for CCEE, the federal government provides funding to states, territories, and Tribes through the Child Care and Development Fund (CCDF) to design and implement consumer education activities. These activities are designed to help parents more easily access information about CCEE and available providers in their area, so they can make more informed decisions. In response to policy actions, researchers have examined questions related to how parents search for and select CCEE. This literature review report summarizes findings across recent publications. This literature review report was developed as part of the Consumer Education and Parental Choice in Early Care and Education project. This report summarizes research published from 2012 to 2021 on how parents look for and select CCEE. The report identifies key findings and areas for future research.

Earlier this year, Representative Virginia Foxx (R-NC) introduced the College Cost Reduction Act (CCRA), a bill to reform higher education grant and loan programs and to establish new accountability rules for colleges. A key part of the bill would replace the myriad income-driven repayment (IDR) plans for federal student loans, including the Biden administration's Saving on a Valuable Education (SAVE) plan, with a new repayment plan. The bill features income-based payments, a cap on total payments, and monthly subsidies that waive unpaid interest and reduce principal balances for certain borrowers. In this brief, the authors compare the CCRA plan with the Biden administration's SAVE plan, the most generous IDR plan. The authors also compare the CCRA plan with another version of IDR, the Pay as You Earn (PAYE) plan.

Upward mobility is core to the American dream yet remains unobtainable for many Americans. And although education is widely viewed as a key lever to support students' upward mobility, there is too little actionable information about how schools promote lifelong success. As a result, PK-12 education tends to focus on conventional indicators of success, such as standardized test scores, that--though important--are unlikely to capture the full set of skills that drive upward mobility. This report reviews the available evidence on the direct links between PK-12 education and economic mobility. We find that the current research offers little guidance about which skills and competencies in PK-12 education are most important for economic success. In part, this is because few studies connect students' PK-12 experiences to their economic success as adults. Additionally, the existing research defines success narrowly in terms of wages, ignoring other dimensions like finding dignity in one's work and a sense of autonomy and belonging in one's community. Furthermore, current research does not adequately address the relationship between individual and contextual factors that affect children's readiness to learn, how school systems function, and how success in school translates into longer-term success. Some research examines how students across different groups (e.g., by race or ethnicity and gender) experience the PK-12 education system, but few explore why patterns differ through a lens that considers historical and contemporary forms of oppression such as racism and sexism. We argue that for schools and education policymakers to best equip all students to attain lifelong success, a new generation of research at the intersection of PK-12 education and economic mobility is necessary. This research should seek to understand how the PK-12 environment can foster skill development that drives upward mobility, how measures of those skills function across different people and places, and how student-, school-, and system-level measures are all shaped by broader factors both in and beyond schools.

To increase quality licensed child care supply, the District of Columbia implemented Capital Quality, a quality rating and improvement system (QRIS) that includes coaching supports. Urban Institute researchers interviewed leaders who oversee coaches working to help child care facilities improve quality and conducted focus groups with coaches in spring 2023. Researchers captured insights on the quality improvement process, promising practices for working with child care providers, methods to improve quality, and challenges faced by coaches and child care facility directors and owners. Prior Urban Institute research reported positive provider experiences with coaching and benefits from participating (such as improved morale and knowledge of quality practices), despite challenges. For the current study, the Urban research team reviewed a summary of the 2020 findings to obtain reactions and reflections about differences in 2023. This report presents findings from data collected in 2023 from the perspectives of leaders and coaches about the current benefits, promising practices, and challenges in quality improvement.

In this paper, we present ELEET, a novel execution engine that allows one to seamlessly query and process text as a first-class citizen along with tables. To enable such a seamless integration of text and tables, ELEET leverages learned multi-modal operators (MMOps) such as joins and unions that seamlessly combine structured with unstructured textual data. While large language models (LLM) such as GPT-4 are interesting candidates to enable such learned multimodal operations, we deliberately do not follow this trend to enable MMOps, since it would result in high overhead at query runtime. Instead, to enable MMOps, ELEET comes with a more efficient small language model (SLM) that is targeted to extract structured data from text. Thanks to our novel architecture and pre-training procedure, the ELEET-model enables high-accuracy extraction with low overheads. In our evaluation, we compare query execution based on ELEET to baselines leveraging LLMs such as GPT-4 and show that ELEET can speed up multi-modal queries over tables and text by up to 575x without sacrificing accuracy.

Algebraic spin liquids are quantum disordered phases of insulating magnets which exhibit fractionalized gapless excitations and power-law correlations. Quantum spin liquids in this category include the experimentally established 1D Luttinger liquid, as well as the U(1) Dirac spin liquid (DSL) which has been a focus of recent candidate materials searches. Most notably, several exchange-frustrated Heisenberg materials on the triangular lattice have shown evidence of the U(1) DSL. In this work, we measure the algebraic correlations of spin-singlet excitations in the $J_1$-$J_2$ antiferromagnetic Heisenberg model on the triangular lattice, prompting a detailed investigation of this model's stability under spin-phonon coupling using variational Monte Carlo. As seen before in 1D spin chains, we observe a low-temperature transition from a U(1) DSL to valence bond order and predict the parameter regime where the model realizes a stable DSL ground state. To achieve this, we employ a series of finite-size scaling Ans\"atze inspired by the low-energy DSL's conformal description in terms of quantum electrodynamics, and show that emergent monopole operators drive the instability. We compare the physics of this transition to the 1D Luttinger liquid throughout our analysis. We derive the regime of stability against spin-Peierls ordering and argue that the DSL ground state might still be achievable in candidate materials, despite its tendency to valence bond solid ordering.

The precision measurement of the tritium $\beta$-decay spectrum performed by the KATRIN experiment provides a unique way to search for general neutrino interactions (GNI). All theoretical allowed GNI terms involving neutrinos are incorporated into a low-energy effective field theory, and can be identified by specific signatures in the measured tritium $\beta$-spectrum. In this paper an effective description of the impact of GNI on the $\beta$-spectrum is formulated and the first constraints on the effective GNI parameters are derived based on the 4 million electrons collected in the second measurement campaign of KATRIN in 2019. In addition, constraints on selected types of interactions are investigated, thereby exploring the potential of KATRIN to search for more specific new physics cases, including a right-handed W boson, a charged Higgs or leptoquarks.

Integrability is an exceptional property believed to hold only for systems with fine-tuned parameters. Contrary, we explicitly show that in homogeneous nearest-neighbor qubit circuits with a U(1) symmetry, i.e., circuits that repeatedly apply the same magnetization-conserving two-qubit gate, this is not the case. There, integrability is generic: all such brickwall qubit circuits are integrable, even with a randomly selected gate. We identify two phases with different conservation laws, transport properties, and strong zero edge modes. Experimentally important is the fact that varying any one of the parameters in the generic U(1) gate, one will typically cross the critical manifold that separates the two phases. Finally, we report on an unconventional time-reversal symmetry causing the system with open boundary conditions to be in the orthogonal class, while the one with periodic boundary conditions is in the unitary class., Comment: 8 pages

Adversarial training is arguably the most popular way to provide empirical robustness against specific adversarial examples. While variants based on multi-step attacks incur significant computational overhead, single-step variants are vulnerable to a failure mode known as catastrophic overfitting, which hinders their practical utility for large perturbations. A parallel line of work, certified training, has focused on producing networks amenable to formal guarantees of robustness against any possible attack. However, the wide gap between the best-performing empirical and certified defenses has severely limited the applicability of the latter. Inspired by recent developments in certified training, which rely on a combination of adversarial attacks with network over-approximations, and by the connections between local linearity and catastrophic overfitting, we present experimental evidence on the practical utility and limitations of using certified training towards empirical robustness. We show that, when tuned for the purpose, a recent certified training algorithm can prevent catastrophic overfitting on single-step attacks, and that it can bridge the gap to multi-step baselines under appropriate experimental settings. Finally, we present a novel regularizer for network over-approximations that can achieve similar effects while markedly reducing runtime.

The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce greenhouse gas emissions that contribute to global warming. To enhance the analysis and storage of wind data, we introduce a deep learning framework designed to simultaneously enable effective dimensionality reduction and continuous representation of multi-altitude wind data from discrete observations. The framework consists of three key components: dimensionality reduction, cross-modal prediction, and super-resolution. We aim to: (1) improve data resolution across diverse climatic conditions to recover high-resolution details; (2) reduce data dimensionality for more efficient storage of large climate datasets; and (3) enable cross-prediction between wind data measured at different heights. Comprehensive testing confirms that our approach surpasses existing methods in both super-resolution quality and compression efficiency., Comment: arXiv admin note: text overlap with arXiv:2401.16936

In this work the Coulomb effects (Coulomb correlations) in $\pi^+\pi^-$ pairs produced in p + Ni collisions at 24 GeV/$c$, are studied using experimental $\pi^+\pi^-$ pair distributions in $Q$, the relative momentum in the pair center of mass system (c.m.s), and its projections $Q_L$ (longitudinal component) and $Q_t$ (transverse component) relative to the pair direction in the laboratory system (l.s.). The $Q$, $Q_L$, and $Q_t$ distributions of the {\sl Coulomb pairs} in the c.m.s. have been simulated assuming they are described by the phase space modified by the known point-like Coulomb correlation function $A_C(Q)$, corrected for small effects due to the nonpoint-like pair production and the strong two-pion interaction. The same distributions of {\sl non-Coulomb pairs} have been simulated according to the phase space, but without $A_C(Q)$. It is shown that the number of {\sl Coulomb pairs} in all $Q_t$ intervals, including the small $Q_t$ (small opening angles $\theta$ in the l.s.) is calculated with the theoretical precision better than 2\%. The comparison of the simulated and experimental numbers of {\sl Coulomb pairs} at small $Q_t$ allows us to check and correct the detection efficiency for the pairs with small $\theta$ (0.06 mrad and smaller). It is shown that {\sl Coulomb pairs} can be used as a new physical tool to check and correct the quality of the simulated events. The special property of the {\sl Coulomb pairs} is the possibility of checking and correcting the detection efficiency, especially for the pairs with small opening angles.

Sterile neutrinos are a minimal extension of the Standard Model of particle physics and a promising candidate for dark matter if their mass is in the keV-range. The Karlsruhe Tritium Neutrino experiment (KATRIN), equipped with a novel multi-pixel silicon drift detector array, the TRISTAN detector, will be capable of searching for these keV-scale sterile neutrinos by investigating the kinematics of the tritium $\beta$-decay. This measurement will be performed after the completion of the neutrino mass measurement campaign. To detect a sterile neutrino signal with a high sensitivity, a profound understanding of the detector response is required. In this work, we report on the characterization of a 7-pixel TRISTAN prototype detector with a laser system. We present the experimental results obtained in high-resolution scans of the detector surface with a focused laser beam and demonstrate how the charge collection and the timing of the signals generated in the detector is related to the detector geometry. A comparison of the experimental data with simulations shows a good agreement., Comment: 23 pages, 12 figures

This document contains an educational introduction to the problem of sparsifying parametric models with L0 regularization. We utilize this approach together with dictionary learning to learn sparse polynomial policies for deep reinforcement learning to control parametric partial differential equations. The code and a tutorial are provided here: https://github.com/nicob15/Sparsifying-Parametric-Models-with-L0.

In this work we considerably improve the state-of-the-art SMT solving on first-order quantified problems by efficient machine learning guidance of quantifier selection. Quantifiers represent a significant challenge for SMT and are technically a source of undecidability. In our approach, we train an efficient machine learning model that informs the solver which quantifiers should be instantiated and which not. Each quantifier may be instantiated multiple times and the set of the active quantifiers changes as the solving progresses. Therefore, we invoke the ML predictor many times, during the whole run of the solver. To make this efficient, we use fast ML models based on gradient boosting decision trees. We integrate our approach into the state-of-the-art cvc5 SMT solver and show a considerable increase of the system's holdout-set performance after training it on a large set of first-order problems collected from the Mizar Mathematical Library.

Instead of randomly acquiring training data points, Uncertainty-based Active Learning (UAL) operates by querying the label(s) of pivotal samples from an unlabeled pool selected based on the prediction uncertainty, thereby aiming at minimizing the labeling cost for model training. The efficacy of UAL critically depends on the model capacity as well as the adopted uncertainty-based acquisition function. Within the context of this study, our analytical focus is directed toward comprehending how the capacity of the machine learning model may affect UAL efficacy. Through theoretical analysis, comprehensive simulations, and empirical studies, we conclusively demonstrate that UAL can lead to worse performance in comparison with random sampling when the machine learning model class has low capacity and is unable to cover the underlying ground truth. In such situations, adopting acquisition functions that directly target estimating the prediction performance may be beneficial for improving the performance of UAL.

Objectives: Evaluating the effects and artifacts introduced by medical foreign bodies in clinical dark-field chest radiographs and assessing their influence on the evaluation of pulmonary tissue, compared to conventional radiographs. Material & Methods: This retrospective study analyzed data from subjects enrolled in clinical trials conducted between 2018 and 2021, focusing on chronic obstructive pulmonary disease (COPD) and COVID-19 patients. All patients obtained a radiograph using an in-house developed clinical prototype for grating-based dark-field chest radiography. The prototype simultaneously delivers a conventional and dark-field radiograph. Two radiologists independently assessed the clinical studies to identify patients with foreign bodies. Subsequently, an analysis was conducted on the effects and artifacts attributed to distinct foreign bodies and their impact on the assessment of pulmonary tissue. Results: Overall, 30 subjects with foreign bodies were included in this study (mean age, 64 years +/- 11 [standard deviation]; 15 men). Foreign bodies composed of materials lacking microstructure exhibited a diminished dark-field signal or no discernible signal. Foreign bodies with a microstructure, in our investigations the cementation of the kyphoplasty, produce a positive dark-field signal. Since most foreign bodies lack microstructural features, dark-field imaging revealed fewer signals and artifacts by foreign bodies compared to conventional radiographs. Conclusion: Dark-field radiography enhances the assessment of pulmonary tissue with overlaying foreign bodies compared to conventional radiography. Reduced interfering signals result in fewer overlapping radiopaque artifacts within the investigated regions. This mitigates the impact on image quality and interpretability of the radiographs and the projection-related limitations of radiography compared to CT.

A potential weakness of topological waveguides is that they act on a fixed narrow band of frequencies. However, by 3D printing samples from a photo-responsive polymer, we can obtain a device whose operating frequency can be fine-tuned dynamically using laser excitation. This greatly enhances existing static tunability strategies, typically based on modifying the geometry. We use a version of the classical Su-Schrieffer-Heeger model to demonstrate our approach., Comment: 5 pages, 3 figures

In the recently proposed Graph Vertex Model (GVM), cellular rearrangements are implemented as local graph transformations of the cell aggregate, represented by a knowledge graph [1]. This study extends GVM to incorporate cell division, a critical biological process involved in morphogenesis, homeostasis, and disease progression. Like cellular rearrangements, cell division in GVM begins by identifying a subgraph of nodes and links, involved in the division, by matching suitable graph patterns or templates within the full knowledge graph. The matched subgraph is then transformed to incorporate topological changes within the knowledge graph, caused by the division event. Importantly, when this transformation is applied to a polygon in a 2D tiling, it performs the transformation, required to divide a polygon, indicating that the 3D graph transformation is general and applicable also to 2D vertex models. Our extension of GVM enables the study of the dynamics of growing cell aggregates in 3D to offer new insights into developmental processes and cancer growth., Comment: 8 pages, 4 figures, 1 supplementary document, and 1 supplementary movie

The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after five years of data acquisition. The sensitivity is affected by the increased rate of the background electrons from KATRIN's main spectrometer. A special shifted-analysing-plane (SAP) configuration was developed to reduce this background by a factor of two. The complex layout of electromagnetic fields in the SAP configuration requires a robust method of estimating these fields. We present in this paper a dedicated calibration measurement of the fields using conversion electrons of gaseous $^\mathrm{83m}$Kr, which enables the neutrino-mass measurements in the SAP configuration., Comment: 19 pages, 11 figures

Data showing what students earn after attending higher education institutions have become increasingly available, bolstering calls from policymakers and advocates that government financial aid programs should be tied to those outcomes. Often overlooked, however, is that these data and policies usually reflect the earnings of only students who graduate. A key rationale for using earnings data in accountability policies is that they help reveal whether the education generates a positive return on investment. Ignoring the earnings outcomes of noncompleters, therefore, may provide an incomplete assessment of that metric, especially for institutions with high dropout rates. This brief uses undergraduate earnings data from the College Scorecard to estimate how much earnings differ based on whether noncompleters are included. These findings help reveal where current quality assurance policies that focus only on completers' earnings may be misaligned with what a typical student is likely to earn when enrolling at a higher education institution.

Funding for community colleges varies significantly, even within the same state. Several factors account for these differences, including more generous funding for smaller institutions to compensate for their higher costs per student, unequal local funding from property tax revenues, and political forces. In theory, this variation could lead to systemic inequities in funding levels by race, ethnicity, and economic status. Such inequities could arise if students from historically underserved groups are concentrated in community colleges that receive the lowest levels of funding from state and local appropriations. This analysis finds no such consistent patterns across the nation but does find concerning patterns in a few states. Significant differences in funding levels across states are likely to be the result of differences in state and local postsecondary support at all levels, priorities attached to two-year versus four-year institutions, and methods of allocating resources among eligible institutions. In addition, allocation systems, combined with demographic differences among community college students, can create sizeable differences in funding levels among the institutions within a state. This analysis focuses on these funding differences across community colleges within states, investigating the prevalence of differences in state and local funding levels between Black students and all other students, Hispanic students and all other students, and low-income students (proxied by the receipt of Pell grants) and higher-income students.

Discussions about college affordability often revolve around helping students pay their tuition and fees. But nontuition expenses, including food, housing, and other living expenses, make up a large portion of student budgets and contribute to increased student debt. These expenses can be a barrier to student enrollment and completion. Lack of information about the true full cost of attendance, stringent requirements to qualify for income support benefits, and limited grant aid make it hard for many students to access enough resources to cover their basic needs. In this report, we summarize our findings emerging from research and a convening we hosted on nontuition expenses. Currently, there is no standardized way to measure cost of attendance at institutions, making it difficult for students to compare nontuition costs across schools. Providing students personalized information about what their food, housing, and other living expenses may look like allows them to plan for the school year but is distinct from practical solutions for measuring student need for financial aid purposes. Doubling the Pell grant is a common suggestion when discussing the cost of education, and incorporating some level of basic living expenses, in addition to the costs of books and supplies, into estimates of adequate grant aid would be constructive. But increasing the Pell grant is insufficient, as using it explicitly to cover living expenses could lack political support and may lead to unintended consequences, such as tuition increases. Based on these findings, state and federal policymakers can do more to support students who cannot afford nontuition expenses through changes to federal, state, and institution-level policies. Both enabling students to have access to income support benefits and more generous grant aid are central to policy efforts for addressing financial barriers to student access and success. Increases in need-based financial aid from federal and state governments, as well as institutions, are important. But we should not view low-income students as entirely distinct from other low-income adults, and education policy alone cannot be responsible for meeting students' basic needs.

Despite growing attention to addressing racial, ethnic, and other inequities in many domains, limited research explores whether and how society's marginalization of people with disabilities compounds for immigrants to produce inequitable financial outcomes. When accessing education and employment, immigrants with disabilities face challenges due to their disability status, citizenship and immigration status, race, ethnicity, and language. This summary highlights key takeaways from a convening of nine experts from universities, research organizations, and community organizations knowledgeable about the intersection of immigration and disability. The goal of the meeting was to identify barriers to financial well-being among immigrants with disabilities in education and employment. Participants discussed how the Protection and Advocacy systems (P&As), which provide legal representation and advocacy for people with disabilities, can help better inform this population of their rights as disabled people and how community-based organizations (CBOs) can bridge gaps in access to education and employment supports. The summary concludes with directions for future research. [Additional funding provided by the Capital One Foundation.]

The Mississippi Integrated Basic Education and Skills Training (MIBEST) program provides pathways into college for adult education students across the state. This brief serves as a supplement to the Urban Institute's original evaluation of MIBEST (Briggs et al. 2022) with an analysis of two factors not explored in the original MIBEST report: the outcomes of student parents and the outcomes of students in different geographic regions. [This brief was prepared for the Mississippi Community College Board.]

Rising college prices and student debt, the growth and collapse of online for-profit colleges, and expansions of federal grant, loan, and loan forgiveness policies have increased calls for more accountability in our higher education system. And there is significant consensus among lawmakers that the existing set of quality assurance policies for federal aid programs are not protecting students and taxpayers. But although these conditions have generated many proposals for policies to hold colleges accountable, none have won broad support among lawmakers. To help advance the reform debate, we review and compare recent proposed policies that address accountability and, drawing on interviews we conducted with former congressional staff members and higher education advocates, what factors have prevented policymakers from enacting reforms, and what conditions may be necessary to bring about policy changes. The report reveals that lawmakers, researchers, and advocates have put forth a wide range of reform proposals to strengthen quality assurance rules for federal aid programs. This variety implies that there is considerable disagreement over what reforms are needed and disagreement over the underlying problem. Some proposals focus on graduation rates, while others assess how colleges spend money. The most common approach among the proposals is to judge colleges on how their students fare in repaying their student loans, suggesting a possible place for consensus. The report also shows there are many political and design roadblocks to reform despite broad support for more accountability. The current political environment makes bipartisan policymaking difficult. Additionally, higher education is an open-access and diverse system of institutions, making policymakers reluctant to pass policies that might reduce access, particularly for low-income students and students from underrepresented ethnic and racial groups. Still, the individuals we interviewed all discussed several major design issues that will further frustrate reform efforts, such as whether students who do not receive federal aid should be included in the policies and whether some fields and institutions should be exempt. Regardless, many of the recent reform proposals illustrate the need to improve assurance for aid programs and should ultimately encourage lawmakers to work toward reforms that strengthen our higher education system and federal aid programs.