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In recognition of the importance of education attainment to individual economic mobility and state workforce development needs, state leaders are advancing policies to support access to pathways into postsecondary education, training and the workforce. Although college and career readiness has long been a priority for policymakers, a growing emphasis on creating student-centered pathways ensures students engage in high-quality learning experiences that are aligned with their goals, interests and skills. Student-centered pathways encompass practices designed to meet each student's individual needs. This means creating high school learning environments that are personalized, competency-based, student-driven and connected to real-world experiences. Student-centered pathways support the development of the skills and knowledge necessary for success in college, career and civic life. Comprehensive approaches that address each of these elements present the strongest potential for states to improve student experiences and outcomes. This Policy Brief explores key levers within each of the elements identified above. It offers insight into relevant policy trends and components of student-centered policies, provides examples of state efforts to leverage policy to develop student-centered pathways and offers considerations for state leaders. The brief is informed by a 50-state scan of relevant state policies included in this report.

In ABP systems, phase separation is accompanied by the emergence of vapor bubbles within liquid domains. Using large-scale particle-based simulations, we study the stochastic dynamics of these bubbles and find that most nucleate, grow, and dissolve within liquid domains. We show that their area dynamics can be described by a Langevin equation with a constant negative drift and noise proportional to the perimeter, fully characterizing bubble area and lifetime statistics. Additionally, we develop a lattice gas model that captures the morphological properties, including the decrease in bubble asphericity with increasing area. These findings provide new insights into phase separation in active matter and highlight limitations in current continuum theories., Comment: 6 pages, 4 figures

The task of policy compliance detection (PCD) is to determine if a scenario is in compliance with respect to a set of written policies. In a conversational setting, the results of PCD can indicate if clarifying questions must be asked to determine compliance status. Existing approaches usually claim to have reasoning capabilities that are latent or require a large amount of annotated data. In this work, we propose logical decomposition for policy compliance (LDPC): a neuro-symbolic framework to detect policy compliance using large language models (LLMs) in a few-shot setting. By selecting only a few exemplars alongside recently developed prompting techniques, we demonstrate that our approach soundly reasons about policy compliance conversations by extracting sub-questions to be answered, assigning truth values from contextual information, and explicitly producing a set of logic statements from the given policies. The formulation of explicit logic graphs can in turn help answer PCDrelated questions with increased transparency and explainability. We apply this approach to the popular PCD and conversational machine reading benchmark, ShARC, and show competitive performance with no task-specific finetuning. We also leverage the inherently interpretable architecture of LDPC to understand where errors occur, revealing ambiguities in the ShARC dataset and highlighting the challenges involved with reasoning for conversational question answering.

This study delves into the aerodynamic behavior of a highly flexible NACA 0012 aerofoil, drawing inspiration from avian feathers to handle a gust. We first examined unsteady flow on a rigid wing both experimentally and numerically and then explored the implications of introducing wing flexibility purely numerically. Our findings underscore the potential of composite materials in alleviating the oscillating aerodynamic forces on a wing under a streamwise gust. This behavior is attributed to its capacity to destabilize the laminar separation bubble, fostering a more stable turbulent boundary layer. While direct avian evidence remains limited, it is postulated that in nature, such mechanisms could mitigate undesired wing flapping, optimizing energy consumption in perturbed environments.

Deep surveys reveal tidal debris and associated compact stellar systems. Euclid's unique combination of capabilities (spatial resolution, depth, and wide sky coverage) will make it a groundbreaking tool for galactic archaeology in the local Universe, bringing low surface brightness (LSB) science into the era of large-scale astronomical surveys. Euclid's Early Release Observations (ERO) demonstrate this potential with a field of view that includes several galaxies in the Dorado group. In this paper, we aim to derive from this image a mass assembly scenario for its main galaxies: NGC 1549, NGC 1553, and NGC 1546. We detect internal and external diffuse structures, and identify candidate globular clusters (GCs). By analysing the colours and distributions of the diffuse structures and candidate GCs, we can place constraints on the galaxies' mass assembly and merger histories. The results show that feature morphology, surface brightness, colours, and GC density profiles are consistent with galaxies that have undergone different merger scenarios. We classify NGC 1549 as a pure elliptical galaxy that has undergone a major merger. NGC 1553 appears to have recently transitioned from a late-type galaxy to early type, after a series of radial minor to intermediate mergers. NGC 1546 is a rare specimen of galaxy with an undisturbed disk and a prominent diffuse stellar halo, which we infer has been fed by minor mergers and then disturbed by the tidal effect from NGC 1553. Finally, we identify limitations specific to the observing conditions of this ERO, in particular stray light in the visible and persistence in the near-infrared bands. Once these issues are addressed and the extended emission from LSB objects is preserved by the data-processing pipeline, the Euclid Wide Survey will allow studies of the local Universe to be extended to statistical ensembles over a large part of the extragalactic sky., Comment: 25 pages, 20 figures

This paper investigates the geometric structure of a quasigeostrophic approximation to a recently introduced reduced-gravity thermal rotating shallow-water model that accounts for stratification. Specifically, it considers a low-frequency approximation of a model for flow above the ocean thermocline, governed by primitive equations with buoyancy variations in both horizontal and vertical directions. Like the thermal model, the stratified variant generates circulation patterns reminiscent of submesoscale instabilities visible in satellite images. An improvement is its ability to model mixed-layer restratification due to baroclinic instability. The primary contribution of this paper is to demonstrate that the model is derived from an Euler--Poincar\'e variational principle, culminating in a Kelvin--Noether theorem, previously established solely for the primitive-equation parent model. The model's Lie--Poisson Hamiltonian structure, earlier obtained through direct calculation, is shown to result from a Legendre transform with the associated geometry elucidated by identifying the relevant momentum map. Another significant contribution of this paper is the identification of the Casimirs of the Lie--Poisson system, including a newly found weaker Casimir family forming the kernel of the Lie--Poisson bracket, which results in potential vorticity evolution independent of buoyancy details as it advects under the flow. These conservation laws related to particle relabeling symmetry are explicitly linked to Noether quantities from the Euler--Poincar\'e principle when variations are not constrained to vanish at integration endpoints. The dual Euler--Poincar\'e/Lie--Poisson formalism provides a unified framework for describing quasigeostrophic reduced-gravity stratified thermal flow, mirroring the approach used in the primitive-equation setting., Comment: Submitted to AIP Journal of Mathematical Physics. Comments welcome! Typo in 2nd author's last name fixed

We derive universal approximation results for the class of (countably) $m$-rectifiable measures. Specifically, we prove that $m$-rectifiable measures can be approximated as push-forwards of the one-dimensional Lebesgue measure on $[0,1]$ using ReLU neural networks with arbitrarily small approximation error in terms of Wasserstein distance. What is more, the weights in the networks under consideration are quantized and bounded and the number of ReLU neural networks required to achieve an approximation error of $\varepsilon$ is no larger than $2^{b(\varepsilon)}$ with $b(\varepsilon)=\mathcal{O}(\varepsilon^{-m}\log^2(\varepsilon))$. This result improves Lemma IX.4 in Perekrestenko et al. as it shows that the rate at which $b(\varepsilon)$ tends to infinity as $\varepsilon$ tends to zero equals the rectifiability parameter $m$, which can be much smaller than the ambient dimension. We extend this result to countably $m$-rectifiable measures and show that this rate still equals the rectifiability parameter $m$ provided that, among other technical assumptions, the measure decays exponentially on the individual components of the countably $m$-rectifiable support set.

The circumgalactic medium (CGM) around massive galaxies plays a crucial role in regulating star formation and feedback. Using the CAMELS simulation suite, we develop emulators for the X-ray surface brightness profile and the X-ray luminosity--stellar mass scaling relation to investigate how stellar and AGN feedback shape the X-ray properties of the hot CGM. Our analysis shows that at CGM scales ($10^{12} \lesssim M_{\rm halo}/M_\odot \lesssim 10^{13}$, $10\lesssim r/{\rm kpc} \lesssim 400$), stellar feedback more significantly impacts the X-ray properties than AGN feedback within the parameters studied. Comparing the emulators to recent eROSITA All-Sky Survey observations, it was found that stronger feedback than currently implemented in the IllustrisTNG, SIMBA, and Astrid simulations is required to match observed CGM properties. However, adopting these enhanced feedback parameters causes deviations in the stellar-mass-halo-mass relations from observational constraints below the group mass scale. This tension suggests possible unaccounted systematics in X-ray CGM observations or inadequacies in the feedback models of cosmological simulations., Comment: 13 pages, 6 figures, submitted to ApJ

Millicharged particles are generic in theories of dark sectors. A cosmic or local abundance of them may be produced by the early universe, stellar environments, or the decay or annihilation of dark matter/dark energy. Furthermore, if such particles are light, these production channels result in a background of millicharged radiation. We show that light-shining-through-wall experiments employing superconducting RF cavities can also be used as ``direct deflection" experiments to search for this relativistic background. The millicharged plasma is first subjected to an oscillating electromagnetic field of a driven cavity, which causes charge separation in the form of charge and current perturbations. In turn, these perturbations can propagate outwards and resonantly excite electromagnetic fields in a well-shielded cavity placed nearby, enabling detection. We estimate that future versions of the existing Dark SRF experiment can probe orders of magnitude of currently unexplored parameter space, including millicharges produced from the Sun, the cosmic neutrino background, or other mechanisms that generate a thermal abundance with energy density as small as $\sim 10^{-4}$ that of the cosmic microwave background., Comment: 31 pages, 8 figures

Integrating watermarking into the generation process of latent diffusion models (LDMs) simplifies detection and attribution of generated content. Semantic watermarks, such as Tree-Rings and Gaussian Shading, represent a novel class of watermarking techniques that are easy to implement and highly robust against various perturbations. However, our work demonstrates a fundamental security vulnerability of semantic watermarks. We show that attackers can leverage unrelated models, even with different latent spaces and architectures (UNet vs DiT), to perform powerful and realistic forgery attacks. Specifically, we design two watermark forgery attacks. The first imprints a targeted watermark into real images by manipulating the latent representation of an arbitrary image in an unrelated LDM to get closer to the latent representation of a watermarked image. We also show that this technique can be used for watermark removal. The second attack generates new images with the target watermark by inverting a watermarked image and re-generating it with an arbitrary prompt. Both attacks just need a single reference image with the target watermark. Overall, our findings question the applicability of semantic watermarks by revealing that attackers can easily forge or remove these watermarks under realistic conditions., Comment: 23 pages, 21 figures, 6 tables

Model predictive control (MPC) has become increasingly popular for the control of robot manipulators due to its improved performance compared to instantaneous control approaches. However, tuning these controllers remains a considerable hurdle. To address this hurdle, we propose a practical MPC formulation which retains the more interpretable tuning parameters of the instantaneous control approach while enhancing the performance through a prediction horizon. The formulation is motivated at hand of a simple example, highlighting the practical tuning challenges associated with typical MPC approaches and showing how the proposed formulation alleviates these challenges. Furthermore, the formulation is validated on a surface-following task, illustrating its applicability to industrially relevant scenarios. Although the research is presented in the context of robot manipulator control, we anticipate that the formulation is more broadly applicable., Comment: Accepted for the IEEE Robotics and Automation Letters

High-quality work-based learning opportunities enable students to develop necessary skills and contribute to positive employment outcomes. State leaders are recognizing the value of work-based learning experiences -- not only for individuals but in service of state attainment and workforce development goals. As leaders recognize the value, financial incentives can help offset costs associated with offering, engaging in and supporting work-based learning and serve as one tool to address barriers to access and expansion of work-based learning. Informed by a 50-state policy scan, this Policy Brief highlights key elements of financial incentives in state policy for each group: students, employers, and education institutions and intermediaries. Each section is informed by a 50-state policy scan and state examples are included throughout.

High-quality career and technical education (CTE) and work-based learning opportunities can support students along a pathway to credential attainment, employment and upward economic mobility through intentional skill development and experiential learning. CTE concentration in high school can increase graduation rates while helping students build employability skills that can support positive outcomes in the workforce. When CTE concentration is paired with opportunities for postsecondary credit or a quality work-based learning experience, students are more likely to attain postsecondary and workforce credentials, successfully enter the workforce and earn higher wages. The Education Commission of the States staff members interviewed state leaders and policymakers, including state education agency staff, state workforce development agency staff, higher education agency staff and district leaders in Delaware, Iowa, Maryland, North Dakota, South Carolina and Washington to better understand how they align CTE and work-based learning policies and programs with workforce needs. This Policy Brief highlights key findings from the interviews, including insight into obstacles and promising practices in increasing student access, program quality and stakeholder engagement.

High-Performance Computing (HPC) systems are among the most energy-intensive scientific facilities, with electric power consumption reaching and often exceeding 20 megawatts per installation. Unlike other major scientific infrastructures such as particle accelerators or high-intensity light sources, which are few around the world, the number and size of supercomputers are continuously increasing. Even if every new system generation is more energy efficient than the previous one, the overall growth in size of the HPC infrastructure, driven by a rising demand for computational capacity across all scientific disciplines, and especially by artificial intelligence workloads (AI), rapidly drives up the energy demand. This challenge is particularly significant for HPC centers in Germany, where high electricity costs, stringent national energy policies, and a strong commitment to environmental sustainability are key factors. This paper describes various state-of-the-art strategies and innovations employed to enhance the energy efficiency of HPC systems within the national context. Case studies from leading German HPC facilities illustrate the implementation of novel heterogeneous hardware architectures, advanced monitoring infrastructures, high-temperature cooling solutions, energy-aware scheduling, and dynamic power management, among other optimizations. By reviewing best practices and ongoing research, this paper aims to share valuable insight with the global HPC community, motivating the pursuit of more sustainable and energy-efficient HPC operations., Comment: 30 pages, 3 figures, 4 tables

In this article, we consider nonlocal Hamilton-Jacobi Equations on networks with Kirchhoff type conditions for the interior vertices and Dirichlet boundary conditions for the boundary ones: our aim is to provide general existence and comparison results in the case when the integro-differential operators are of order strictly less than 1. The main originality of these results is to allow these nonlocal terms to have contributions on several different edges of the network. The existence of Lipschitz continuous solutions is proved in two ways: either by using the vanishing viscosity method or by the usual Perron's method. The comparison proof relies on arguments introduced by Lions and Souganidis. We also introduce a notion of flux-limited solution, nonlocal analog to the one introduced by Imbert and Monneau, and prove that the solutions of the Kirchhoff problem are flux-limited solutions for a suitable flux-limiter. After treating in details the case when we only have one interior vertex, we extend our approach to treat general networks.

To maximize the payload mass, an aerocapture trajectory should be flown in such a way that both the final {\Delta}V and the total heat load are minimized. For some aerocapture missions, the heating due to radiation of high temperature gases in the shock-layer is so much larger than the heat due to convection, that the latter is negligible. This paper provides analytical proof and numerical validation that radiative heat is minimized by the same trajectory that minimizes the final {\Delta}V: a single switch bang-bang trajectory, starting with full lift-up, full lift-down commands. Further, a novel guidance that plans a bang-bang trajectory with constraints in the attitude kinematics is introduced. While achieving similar performance as the current state-of-the-art, the inclusion of constraints in attitude kinematics allows for much less tuning. Finally, a lateral guidance that makes use of information on the final inclination of the predicted trajectory is introduced. Such guidance allows for very high accuracy in the inclination requirements with only two reversals, by requiring a single parameter to be tuned., Comment: Submitted to the Journal of Guidance, Control, and Dynamics

Core ellipticals, massive early-type galaxies have an almost constant inner surface brightness profile. The size of the core region correlates with the mass of the finally merged black hole. Here we report the first Euclid-based dynamical mass determination of a supermassive black hole. We study the centre of NGC 1272, the second most luminous elliptical galaxy in the Perseus cluster, combining the Euclid VIS photometry coming from the Early Release Observations of the Perseus cluster with VIRUS spectroscopic observations at the Hobby-Eberly Telescope. The core of NGC 1272 is detected on the Euclid VIS image. Its size is $1.29\pm 0.07''$ or 0.45 kpc, determined by fitting PSF-convolved core-S\'ersic and Nuker-law functions. The two-dimensional stellar kinematics of the galaxy is measured from the VIRUS spectra by deriving optimally regularized non-parametric line-of-sight velocity distributions. Dynamical models of the galaxy are constructed using our axisymmetric and triaxial Schwarzschild codes. We measure a black hole mass of $(5\pm3) \times 10^9 M_\odot$, in line with the expectation from the $M_{\rm BH}$-$r_{\rm b}$ correlation, but eight times larger than predicted by the $M_{\rm BH}$-$\sigma$ correlation (at $1.8\sigma$ significance). The core size, rather than the velocity dispersion, allows one to select galaxies harboring the most massive black holes. The spatial resolution, wide area coverage, and depth of the \Euclid (Wide and Deep) surveys allow us to find cores of passive galaxies larger than 2 kpc up to redshift 1., Comment: Accepted for publication in A&A

We present the Baryon Pasted (BP) X-ray and thermal Sunyaev-Zel'dovich (tSZ) maps derived from the half-sky Uchuu Lightcone simulation. These BP-Uchuu maps are constructed using more than $75$ million dark matter halos with masses $M_{500c} \geq 10^{13} M_\odot$ within the redshift range $0 \leq z \leq 2$. A distinctive feature of our BP-Uchuu Lightcone maps is their capability to assess the influence of both extrinsic and intrinsic scatter caused by triaxial gaseous halos and internal gas characteristics, respectively, at the map level. We show that triaxial gas drives substantial scatter in X-ray luminosities of clusters and groups, accounting for nearly half of the total scatter in core-excised measurements. Additionally, scatter in the thermal pressure and gas density profiles of halos enhances the X-ray and SZ power spectra, leading to biases in cosmological parameter estimates. These findings are statistically robust due to the extensive sky coverage and large halo sample in the BP-Uchuu maps., Comment: 16 pages, 12 figures, submitted to ApJ. The BP-Uchuu maps are publicly available

The recent tension in the value of the cosmological parameter $S_8 \equiv \sigma_8(\Omega_M/0.3)^{1/2}$, which represents the amplitude of the matter density fluctuations of the universe, has not been resolved. In this work, we present constraints on $S_8$ with the X-ray angular power spectra of clusters and groups measured with the half-sky map from the eROSITA All Sky Survey data release 1 (eRASS1). Thanks to the extensive sky coverage of eRASS1, its power spectrum achieves unprecedented precision compared to previous measurements. Using a well-calibrated, physical halo gas model that includes astrophysics of feedback and non-thermal pressure support, we obtain $S_8 = 0.80^{+0.02}_{-0.01}$ with 1$\sigma$ uncertainty that is competitive against other cosmological probes. Our derived $S_8$ value is smaller than the primary CMB measurements from Planck, but still consistent to within $1\sigma$. We also obtain constraints on the astrophysics of feedback, non-thermal pressure, equation of state in cluster cores, and gas boundaries in clusters and groups. We discuss how additional X-ray observations, and cosmological surveys in microwave and optical, will further improve the cosmological constraints with the angular power spectrum. Our work demonstrates that the angular power spectrum of clusters and groups is a promising probe of both cosmology and astrophysics., Comment: 12 pages, 4 figures, submitted to ApJ

Nonlinear electron transport induced by polarized terahertz radiation is studied in two-dimensional tellurene at room temperature. A direct current, quadratic in the radiation's electric field, is observed. Contributions sensitive to radiation helicity, polarization orientation as well as polarization independent current are found. We show that these contributions can be modified by the magnitude of the external gate potential. We demonstrate that this terahertz-driven electric current arises from the Berry curvature dipole and the side-jump microscopic mechanisms.Nonlinear electron transport induced by polarized terahertz radiation is studied in two-dimensional tellurene at room temperature. A direct current, quadratic in the radiation's electric field, is observed. Contributions sensitive to radiation helicity, polarization orientation as well as polarization independent current are found. We show that these contributions can be modified by the magnitude of the external gate potential. We demonstrate that this terahertz-driven electric current arises from the Berry curvature dipole and the side-jump microscopic mechanisms., Comment: 6 pages and 5 figures

The high-performance computing (HPC) community has recently seen a substantial diversification of hardware platforms and their associated programming models. From traditional multicore processors to highly specialized accelerators, vendors and tool developers back up the relentless progress of those architectures. In the context of scientific programming, it is fundamental to consider performance portability frameworks, i.e., software tools that allow programmers to write code once and run it on different computer architectures without sacrificing performance. We report here on the benefits and challenges of performance portability using a field-line tracing simulation and a particle-in-cell code, two relevant applications in computational plasma physics with applications to magnetically-confined nuclear-fusion energy research. For these applications we report performance results obtained on four HPC platforms with server-class CPUs from Intel (Xeon) and AMD (EPYC), and high-end GPUs from Nvidia and AMD, including the latest Nvidia H100 GPU and the novel AMD Instinct MI300A APU. Our results show that both Kokkos and OpenMP are powerful tools to achieve performance portability and decent "out-of-the-box" performance, even for the very latest hardware platforms. For our applications, Kokkos provided performance portability to the broadest range of hardware architectures from different vendors., Comment: 15 pages, 8 figures, this is a pre-print to be published in the Latin America High Performance Computing Conference (CARLA) 2024 proceedings

We present a study on the inference of cosmological and astrophysical parameters using stacked galaxy cluster profiles. Utilizing the CAMELS-zoomGZ simulations, we explore how various cluster properties--such as X-ray surface brightness, gas density, temperature, metallicity, and Compton-y profiles--can be used to predict parameters within the 28-dimensional parameter space of the IllustrisTNG model. Through neural networks, we achieve a high correlation coefficient of 0.97 or above for all cosmological parameters, including $\Omega_{\rm m}$, $H_0$, and $\sigma_8$, and over 0.90 for the remaining astrophysical parameters, showcasing the effectiveness of these profiles for parameter inference. We investigate the impact of different radial cuts, with bins ranging from $0.1R_{200c}$ to $0.7R_{200c}$, to simulate current observational constraints. Additionally, we perform a noise sensitivity analysis, adding up to 40\% Gaussian noise (corresponding to signal-to-noise ratios as low as 2.5), revealing that key parameters such as $\Omega_{\rm m}$, $H_0$, and the IMF slope remain robust even under extreme noise conditions. We also compare the performance of full radial profiles against integrated quantities, finding that profiles generally lead to more accurate parameter inferences. Our results demonstrate that stacked galaxy cluster profiles contain crucial information on both astrophysical processes within groups and clusters and the underlying cosmology of the universe. This underscores their significance for interpreting the complex data expected from next-generation surveys and reveals, for the first time, their potential as a powerful tool for parameter inference., Comment: Submitted to ApJ

The rapid development of automated vehicles (AVs) promises to revolutionize transportation by enhancing safety and efficiency. However, ensuring their reliability in diverse real-world conditions remains a significant challenge, particularly due to rare and unexpected situations known as edge cases. Although numerous approaches exist for detecting edge cases, there is a notable lack of a comprehensive survey that systematically reviews these techniques. This paper fills this gap by presenting a practical, hierarchical review and systematic classification of edge case detection and assessment methodologies. Our classification is structured on two levels: first, categorizing detection approaches according to AV modules, including perception-related and trajectory-related edge cases; and second, based on underlying methodologies and theories guiding these techniques. We extend this taxonomy by introducing a new class called "knowledge-driven" approaches, which is largely overlooked in the literature. Additionally, we review the techniques and metrics for the evaluation of edge case detection methods and identified edge cases. To our knowledge, this is the first survey to comprehensively cover edge case detection methods across all AV subsystems, discuss knowledge-driven edge cases, and explore evaluation techniques for detection methods. This structured and multi-faceted analysis aims to facilitate targeted research and modular testing of AVs. Moreover, by identifying the strengths and weaknesses of various approaches and discussing the challenges and future directions, this survey intends to assist AV developers, researchers, and policymakers in enhancing the safety and reliability of automated driving (AD) systems through effective edge case detection., Comment: Preprint submitted to IEEE Transactions on Intelligent Transportation Systems

Recent advances in machine learning have led to a surge in adoption of neural networks for various tasks, but lack of interpretability remains an issue for many others in which an understanding of the features influencing the prediction is necessary to ensure fairness, safety, and legal compliance. In this paper we consider one class of such tasks, tabular dataset classification, and propose a novel neuro-symbolic architecture, Neural Reasoning Networks (NRN), that is scalable and generates logically sound textual explanations for its predictions. NRNs are connected layers of logical neurons which implement a form of real valued logic. A training algorithm (R-NRN) learns the weights of the network as usual using gradient descent optimization with backprop, but also learns the network structure itself using a bandit-based optimization. Both are implemented in an extension to PyTorch (https://github.com/IBM/torchlogic) that takes full advantage of GPU scaling and batched training. Evaluation on a diverse set of 22 open-source datasets for tabular classification demonstrates performance (measured by ROC AUC) which improves over multi-layer perceptron (MLP) and is statistically similar to other state-of-the-art approaches such as Random Forest, XGBoost and Gradient Boosted Trees, while offering 43% faster training and a more than 2 orders of magnitude reduction in the number of parameters required, on average. Furthermore, R-NRN explanations are shorter than the compared approaches while producing more accurate feature importance scores.

We present an approach for the analysis of hybrid visual compositions in animation in the domain of ephemeral film. We combine ideas from semi-supervised and weakly supervised learning to train a model that can segment hybrid compositions without requiring pre-labeled segmentation masks. We evaluate our approach on a set of ephemeral films from 13 film archives. Results demonstrate that the proposed learning strategy yields a performance close to a fully supervised baseline. On a qualitative level the performed analysis provides interesting insights on hybrid compositions in animation film., Comment: Vision for Art (VISART VII) Workshop at the European Conference of Computer Vision (ECCV)

Emergent cooperative functionality in active matter systems plays a crucial role in various applications of active swarms, ranging from pollutant foraging and collective threat detection to tissue embolization. In nature, animals like bats and whales use acoustic signals to communicate and enhance their evolutionary competitiveness. Here, we show that information exchange by acoustic waves between active agents creates a large variety of multifunctional structures. In our realization of collective swarms, each unit is equipped with an acoustic emitter and a detector. The swarmers respond to the resulting acoustic field by adjusting their emission frequency and migrating toward the strongest signal. We find self-organized structures with different morphology, including snake-like self-propelled entities, localized aggregates, and spinning rings. These collective swarms exhibit emergent functionalities, such as phenotype robustness, collective decision-making, and environmental sensing. For instance, the collectives show self-regeneration after strong distortion, allowing them to penetrate through narrow constrictions. Additionally, they exhibit a population-scale perception of reflecting objects and a collective response to acoustic control inputs. Our results provide insights into fundamental organization mechanisms in information-exchanging swarms. They may inspire design principles for technical implementations in the form of acoustically or electromagnetically communicating microrobotic swarms capable of performing complex tasks and concerting collective responses to external cues.

Auto-chemotaxis, the directed movement of cells along gradients in chemicals they secrete, is central to the formation of complex spatiotemporal patterns in biological systems. Since the introduction of the Keller-Segel model, numerous variants have been analyzed, revealing phenomena such as coarsening of aggregates, stable aggregate sizes, and spatiotemporally chaotic dynamics. Here, we consider general mass-conserving Keller-Segel models, that is, models without cell growth and death, and analyze the generic long-time dynamics of the chemotactic aggregates. Building on and extending our previous work, which demonstrated that chemotactic aggregation can be understood through a generalized Maxwell construction balancing density fluxes and reactive turnover, we use singular perturbation theory to derive the full rates of mass competition between well-separated aggregates. We analyze how this mass-competition process drives coarsening in both diffusion- and reaction-limited regimes, with the diffusion-limited rate aligning with our previous quasi-steady-state analyses. Our results generalize earlier mathematical findings, demonstrating that coarsening is driven by self-amplifying mass transport and aggregate coalescence. Additionally, we provide a linear stability analysis of the lateral instability, predicting it through a nullcline-slope criterion similar to the curvature criterion in spinodal decomposition. Overall, our findings suggest that chemotactic aggregates behave similarly to phase-separating droplets, providing a robust framework for understanding the coarse-grained dynamics of auto-chemotactic cell populations and providing a basis for the analysis of more complex multi-species chemotactic systems., Comment: 24 pages, 5 figures

Chemotaxis allows single cells to self-organize at the population level, as classically described by Keller-Segel models. We show that chemotactic aggregation can be understood using a generalized Maxwell construction based on the balance of density fluxes and reactive turnover. This formulation implies that aggregates generically undergo coarsening, which is interrupted and reversed by cell growth and death. Together, both stable and spatiotemporally dynamic aggregates emerge. Our theory mechanistically links chemotactic self-organization to phase separation and reaction-diffusion patterns., Comment: 7 pages Main text, 25 pages Supplementary Information; 4 figures

For cellular functions like division and polarization, protein pattern formation driven by NTPase cycles is a central spatial control strategy. Operating far from equilibrium, no general theory links microscopic reaction networks and parameters to the pattern type and dynamics. We discover a generic mechanism giving rise to an effective interfacial tension organizing the macroscopic structure of non-equilibrium steady-state patterns. Namely, maintaining protein-density interfaces by cyclic protein attachment and detachment produces curvature-dependent protein redistribution which straightens the interface. We develop a non-equilibrium Neumann angle law and Plateau vertex conditions for interface junctions and mesh patterns, thus introducing the concepts of ``Turing mixtures'' and ``Turing foams''. In contrast to liquid foams and mixtures, these non-equilibrium patterns can select an intrinsic wavelength by interrupting an equilibrium-like coarsening process. Data from in vitro experiments with the E. coli Min protein system verifies the vertex conditions and supports the wavelength dynamics. Our study uncovers interface laws with correspondence to thermodynamic relations that arise from distinct physical processes in active systems. It allows the design of specific pattern morphologies with potential applications as spatial control strategies in synthetic cells., Comment: 11 pages main text, 5 pages Methods, 64 pages Supplementary Information; 12 figures

The self-organization of proteins into enriched compartments and the formation of complex patterns are crucial processes for life on the cellular level. Liquid-liquid phase separation is one mechanism for forming such enriched compartments. When phase-separating proteins are membrane-bound and locally disturb it, the mechanical response of the membrane mediates interactions between these proteins. How these membrane-mediated interactions influence the steady state of the protein density distribution is thus an important question to investigate in order to understand the rich diversity of protein and membrane-shape patterns present at the cellular level. This work starts with a widely used model for membrane-bound phase-separating proteins. We numerically solve our system to map out its phase space and perform a careful, systematic expansion of the model equations to characterize the phase transitions through linear stability analysis and free energy arguments. We observe that the membrane-mediated interactions, due to their long-range nature, are capable of qualitatively altering the equilibrium state of the proteins. This leads to arrested coarsening and length-scale selection instead of simple demixing and complete coarsening. In this study, we unambiguously show that long-range membrane-mediated interactions lead to pattern formation in a system that otherwise would not do so. This work provides a basis for further systematic study of membrane-bound pattern-forming systems.

We obtain the comparison principle for discontinuous viscosity sub- and supersolutions of nonlocal Hamilton-Jacobi equations, with superlinear and coercive gradient terms. The nonlocal terms are integro-differential operators in L\'evy form, with general measures: $x$-dependent, possibly degenerate and without any restriction on the order. The measures must satisfy a combined Wasserstein/Total Variation-continuity assumption, which is one of the weakest conditions used in the context of viscosity approach for this type of integro-differential PDEs. The proof relies on a regularizing effect due to the gradient growth. We present several examples of applications to PDEs with different types of nonlocal operators (measures with density, operators of variable order, L\'evy-It\^o operators).

This paper proposes Attention-Seeker, an unsupervised keyphrase extraction method that leverages self-attention maps from a Large Language Model to estimate the importance of candidate phrases. Our approach identifies specific components - such as layers, heads, and attention vectors - where the model pays significant attention to the key topics of the text. The attention weights provided by these components are then used to score the candidate phrases. Unlike previous models that require manual tuning of parameters (e.g., selection of heads, prompts, hyperparameters), Attention-Seeker dynamically adapts to the input text without any manual adjustments, enhancing its practical applicability. We evaluate Attention-Seeker on four publicly available datasets: Inspec, SemEval2010, SemEval2017, and Krapivin. Our results demonstrate that, even without parameter tuning, Attention-Seeker outperforms most baseline models, achieving state-of-the-art performance on three out of four datasets, particularly excelling in extracting keyphrases from long documents., Comment: This version has been accepted for presentation at COLING 2025, and all peer-reviewed changes have been incorporated

Chemo-mechanical waves on active deformable surfaces are a key component for many vital cellular functions. In particular, these waves play a major role in force generation and long-range signal transmission in cells that dynamically change shape, as encountered during cell division or morphogenesis. Reconstituting and controlling such chemically controlled cell deformations is a crucial but unsolved challenge for the development of synthetic cells. Here, we develop an optogenetic method to elucidate the mechanism responsible for coordinating surface contraction waves that occur in oocytes of the starfish Patiria miniata during meiotic cell division. Using spatiotemporally-patterned light stimuli as a control input, we create chemo-mechanical cortical excitations that are decoupled from meiotic cues and drive diverse shape deformations ranging from local pinching to surface contraction waves and cell lysis. We develop a quantitative model that entails the hierarchy of chemical and mechanical dynamics, which allows to relate the variety of mechanical responses to optogenetic stimuli. Our framework systematically predicts and explains transitions of programmed shape dynamics. Finally, we qualitatively map the observed shape dynamics to elucidate how the versatility of intracellular protein dynamics can give rise to a broad range of mechanical phenomenologies. More broadly, our results pave the way toward real-time control over dynamical deformations in living organisms and can advance the design of synthetic cells and life-like cellular functions., Comment: 36 pages, 16 figures, 11 movies

In this paper, we study geostrophic turbulence without external forcing or dissipation, using a Casimir-preserving numerical method. The research examines the formation of large zonal jets, common in geophysical flows, especially in giant gas planets. These jets form due to the east-west stretching of vortices, influenced by the gradient of the Coriolis parameter, leading to a critical latitude beyond which jets do not form. Using a global quasi-geostrophic model with a fully latitude-dependent Coriolis parameter, we investigate this critical latitude, which is theorized to depend only on the product of the Rossby number and the Lamb parameter. By simulating random flow fields, the critical latitude was identified through zonally averaged zonal velocity profiles. Results align with geostrophic theory, especially near typical Rossby and Lamb parameter values for Earth's atmosphere. However, in the regime of weak rotation (high Rossby numbers) and strong stratification (high Lamb values), no clear critical latitude emerges; instead, zonal jet amplitude and width decrease gradually towards the poles. This research paves the way for further study of jet dynamics under a fully latitude-dependent Coriolis parameter.

Accurate long-term predictions of large-scale flow features on planets are crucial for understanding global atmospheric and oceanic systems, necessitating the development of numerical methods that can preserve essential physical structures over extended simulation periods without excessive computational costs. Recent advancements in the study of global single-layer barotropic models have led to novel numerical methods based on Lie-Poisson discretization that preserve energy, enstrophy and higher-order moments of potential vorticity. This work extends this approach to more complex stratified quasi-geostrophic (QG) systems on the sphere. In this work, we present a formulation of the multi-layer QG equations on the full globe. This allows for extending the Lie-Poisson discretization to multi-layer QG models, ensuring consistency with the underlying structure and enabling long-term simulations without additional regularization. The numerical method is benchmarked through simulations of forced geostrophic turbulence and the long-term behaviour of unforced multi-layered systems. These results demonstrate the structure-preserving properties and robustness of the proposed numerical method, paving the way for a better understanding of the role of high-order conserved quantities in large-scale geophysical flow dynamics.

Recent theoretical studies of carrier-carrier scattering in degenerate two-dimensional systems have revealed radically different relaxation times for odd and even angular harmonics of distribution function. This theoretical concept, dubbed as 'tomographic Fermi liquid', is yet challenging to test with dc electrical measurements as electron scattering weakly affects the electrical resistivity. Here, we show that linewidth and amplitude of electromagnetic absorption at the multiple harmonics of the cyclotron resonance carries all necessary information to test the tomographic Fermi liquid hypothesis. Namely, the height and inverse width of $m$-th order cyclotron resonance ($m \ge 2$) is proportional to the lifetime of $m$-th angular harmonic of electron distribution function $\tau_m$, if probed at wavelengths exceeding the cyclotron radius $R_c$. Measurements of high-order cyclotron resonance at short wavelengths order of $R_c$ also enable a direct determination of all lifetimes $\tau_m$ from a simple linear system of equations that we hereby derive. Extraction of cyclotron resonance lifetimes from an experiment on terahertz photoconductivity in graphene shows that third-order resonance is systematically narrower than second-order one, supporting the prediction of tomographic Fermi liquid hypothesis.

The development of Automated Driving Systems (ADSs) has advanced significantly. To enable their large-scale deployment, the United Nations Regulation 157 (UN R157) concerning the approval of Automated Lane Keeping Systems (ALKSs) has been approved in 2021. UN R157 requires an activated ALKS to avoid any collisions that are reasonably preventable and proposes a method to distinguish reasonably preventable collisions from unpreventable ones using "the simulated performance of a skilled and attentive human driver". With different driver models, benchmarks are set for ALKSs in three types of scenarios. The three types of scenarios considered in the proposed method in UN R157 assume a certain parameterization without any further consideration. This work investigates the parameterization of these scenarios, showing that the choice of parameterization significantly affects the simulation outcomes. By comparing real-world and parameterized scenarios, we show that the influence of parameterization depends on the scenario type, driver model, and evaluation criterion. Alternative parameterizations are proposed, leading to results that are closer to the non-parameterized scenarios in terms of recall, precision, and F1 score. The study highlights the importance of careful scenario parameterization and suggests improvements to the current UN R157 approach., Comment: Accepted for the 2024 IEEE International Automated Vehicle Validation (IAVVC2024) Conference

Automated Driving Systems (ADSs) have the potential to make mobility services available and safe for all. A multi-pillar Safety Assessment Framework (SAF) has been proposed for the type-approval process of ADSs. The SAF requires that the test scenarios for the ADS adequately covers the Operational Design Domain (ODD) of the ADS. A common method for generating test scenarios involves basing them on scenarios identified and characterized from driving data. This work addresses two questions when collecting scenarios from driving data. First, do the collected scenarios cover all relevant aspects of the ADS' ODD? Second, do the collected scenarios cover all relevant aspects that are in the driving data, such that no potentially important situations are missed? This work proposes coverage metrics that provide a quantitative answer to these questions. The proposed coverage metrics are illustrated by means of an experiment in which over 200000 scenarios from 10 different scenario categories are collected from the HighD data set. The experiment demonstrates that a coverage of 100 % can be achieved under certain conditions, and it also identifies which data and scenarios could be added to enhance the coverage outcomes in case a 100 % coverage has not been achieved. Whereas this work presents metrics for the quantification of the coverage of driving data and the identified scenarios, this paper concludes with future research directions, including the quantification of the completeness of driving data and the identified scenarios., Comment: Accepted for the 2024 IEEE International Automated Vehicle Validation (IAVVC 2024) Conference

The assessment landscape is varied and complex. Because assessments serve many purposes, they can take multiple forms depending on what they are measuring and how they are used. The intended purpose behind each assessment influences its design, and the design influences how assessment data can be used by students and their families, school, district and state leaders. State and district policymakers have important decisions to make about when and how to gather and use assessment data across K-12 education systems, but federal policy is a key component of every state's assessment system. This report discusses how the Every Student Succeeds Act (ESSA) lays the foundation for state summative assessment systems and requires state education agencies to implement statewide assessments. In addition to federal compliance requirements, states may use assessment data in separate state accountability systems or opt to administer certain assessments to better inform policymaking and priorities.

An integrator for a class of stochastic Lie-Poisson systems driven by Stratonovich noise is developed. The integrator is suited for Lie-Poisson systems that also admit an isospectral formulation, which enables scalability to high-dimensional systems. Its derivation follows from discrete Lie-Poisson reduction of the symplectic midpoint scheme for stochastic Hamiltonian systems. We prove almost sure preservation of Casimir functions and coadjoint orbits under the numerical flow and provide strong and weak convergence rates of the proposed method. The scalability, structure-conservation, and convergence rates are illustrated numerically for the (generalized) rigid body, point vortex dynamics, and the two-dimensional Euler equations on the sphere., Comment: 29 pages, 11 figures, all comments are welcome!

In this work we derive several important equations in water waves and liquid crystals by deriving them as geodesic equations of right-invariant metrics on two infinite-dimensional groups. The equations we obtain this way are the Hopf (inviscid Burgers) equation, the Camassa-Holm equation, the Hunter-Saxton equation and the Korteweg-De Vries equation. We then study the symmetry groups of the equations themselves and show that one can improve the behaviour of the Hopf equation by metric and topological corrections. The symmetry groups of these equations can aid the benchmarking and testing of numerical methods., Comment: 21 pages, 1 figure, 7 tables. fourth version, all comments are welcome!

Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in competitive table tennis. Table tennis is a physically demanding sport which requires human players to undergo years of training to achieve an advanced level of proficiency. In this paper, we contribute (1) a hierarchical and modular policy architecture consisting of (i) low level controllers with their detailed skill descriptors which model the agent's capabilities and help to bridge the sim-to-real gap and (ii) a high level controller that chooses the low level skills, (2) techniques for enabling zero-shot sim-to-real including an iterative approach to defining the task distribution that is grounded in the real-world and defines an automatic curriculum, and (3) real time adaptation to unseen opponents. Policy performance was assessed through 29 robot vs. human matches of which the robot won 45% (13/29). All humans were unseen players and their skill level varied from beginner to tournament level. Whilst the robot lost all matches vs. the most advanced players it won 100% matches vs. beginners and 55% matches vs. intermediate players, demonstrating solidly amateur human-level performance. Videos of the matches can be viewed at https://sites.google.com/view/competitive-robot-table-tennis, Comment: v2, 29 pages, 19 main paper, 10 references + appendix, adding an additional 9 references

Large-scale HPC simulations of plasma dynamics in fusion devices require efficient parallel I/O to avoid slowing down the simulation and to enable the post-processing of critical information. Such complex simulations lacking parallel I/O capabilities may encounter performance bottlenecks, hindering their effectiveness in data-intensive computing tasks. In this work, we focus on introducing and enhancing the efficiency of parallel I/O operations in Particle-in-Cell Monte Carlo simulations. We first evaluate the scalability of BIT1, a massively-parallel electrostatic PIC MC code, determining its initial write throughput capabilities and performance bottlenecks using an HPC I/O performance monitoring tool, Darshan. We design and develop an adaptor to the openPMD I/O interface that allows us to stream PIC particle and field information to I/O using the BP4 backend, aggressively optimized for I/O efficiency, including the highly efficient ADIOS2 interface. Next, we explore advanced optimization techniques such as data compression, aggregation, and Lustre file striping, achieving write throughput improvements while enhancing data storage efficiency. Finally, we analyze the enhanced high-throughput parallel I/O and storage capabilities achieved through the integration of openPMD with rapid metadata extraction in BP4 format. Our study demonstrates that the integration of openPMD and advanced I/O optimizations significantly enhances BIT1's I/O performance and storage capabilities, successfully introducing high throughput parallel I/O and surpassing the capabilities of traditional file I/O., Comment: Accepted by IEEE Cluster workshop 2024 (REX-IO 2024), prepared in the standardized IEEE conference format and consists of 10 pages, which includes the main text, references, and figures

We address how the interplay between the finite availability and carrying capacity of particles at different parts of a spatially extended system can control the steady state currents and density profiles in the one-dimensional current-carrying lanes connecting the different parts of the system. To study this, we set up a minimal model consisting of two particle reservoirs of the same finite carrying capacity connected by two equally sized anti-parallel asymmetric exclusion processes (TASEP). We focus on the steady-state currents and particle density profiles in the two TASEP lanes. The ensuing phases and the phase diagrams, which can be remarkably complex, are parametrized by the model parameters defining particle exchange between the TASEP lanes and the reservoirs and the filling fraction of the particles that determine the total resources available. These parameters may be tuned to make the densities of the two TASEP lanes globally uniform or piece-wise continuous in the form of a combination of a single localized domain wall and a spatially constant density or a pair of delocalized domain walls. Our model reveals that the two reservoirs can be preferentially populated or depopulated in the steady states., Comment: 18 pages, 4 figures

The computational power of High-Performance Computing (HPC) systems is constantly increasing, however, their input/output (IO) performance grows relatively slowly, and their storage capacity is also limited. This unbalance presents significant challenges for applications such as Molecular Dynamics (MD) and Computational Fluid Dynamics (CFD), which generate massive amounts of data for further visualization or analysis. At the same time, checkpointing is crucial for long runs on HPC clusters, due to limited walltimes and/or failures of system components, and typically requires the storage of large amount of data. Thus, restricted IO performance and storage capacity can lead to bottlenecks for the performance of full application workflows (as compared to computational kernels without IO). In-situ techniques, where data is further processed while still in memory rather to write it out over the I/O subsystem, can help to tackle these problems. In contrast to traditional post-processing methods, in-situ techniques can reduce or avoid the need to write or read data via the IO subsystem. They offer a promising approach for applications aiming to leverage the full power of large scale HPC systems. In-situ techniques can also be applied to hybrid computational nodes on HPC systems consisting of graphics processing units (GPUs) and central processing units (CPUs). On one node, the GPUs would have significant performance advantages over the CPUs. Therefore, current approaches for GPU-accelerated applications often focus on maximizing GPU usage, leaving CPUs underutilized. In-situ tasks using CPUs to perform data analysis or preprocess data concurrently to the running simulation, offer a possibility to improve this underutilization.

High-Performance Computing (HPC) systems provide input/output (IO) performance growing relatively slowly compared to peak computational performance and have limited storage capacity. Computational Fluid Dynamics (CFD) applications aiming to leverage the full power of Exascale HPC systems, such as the solver Nek5000, will generate massive data for further processing. These data need to be efficiently stored via the IO subsystem. However, limited IO performance and storage capacity may result in performance, and thus scientific discovery, bottlenecks. In comparison to traditional post-processing methods, in-situ techniques can reduce or avoid writing and reading the data through the IO subsystem, promising to be a solution to these problems. In this paper, we study the performance and resource usage of three in-situ use cases: data compression, image generation, and uncertainty quantification. We furthermore analyze three approaches when these in-situ tasks and the simulation are executed synchronously, asynchronously, or in a hybrid manner. In-situ compression can be used to reduce the IO time and storage requirements while maintaining data accuracy. Furthermore, in-situ visualization and analysis can save Terabytes of data from being routed through the IO subsystem to storage. However, the overall efficiency is crucially dependent on the characteristics of both, the in-situ task and the simulation. In some cases, the overhead introduced by the in-situ tasks can be substantial. Therefore, it is essential to choose the proper in-situ approach, synchronous, asynchronous, or hybrid, to minimize overhead and maximize the benefits of concurrent execution.

We report on the observation and study of the magneto-ratchet effect in a graphene-based two-dimensional metamaterial formed by a graphite gate that is placed below a graphene monolayer and patterned with an array of triangular antidots. We demonstrate that terahertz/gigahertz excitation of the metamaterial leads to sign-alternating magneto-oscillations with an amplitude that exceeds the ratchet current at zero magnetic field by orders of magnitude. The oscillations are shown to be related to the Shubnikov-de Haas effect. In addition to the giant ratchet current oscillations we detect resonant ratchet currents caused by the cyclotron and electron spin resonances. The results are well described by the developed theory considering the magneto-ratchet effect caused by the interplay of the near-field radiation and the nonuniform periodic electrostatic potential of the metamaterial controlled by the gate voltages., Comment: 10 pages, 6 figures

In this article we consider a large family of nonlinear nonlocal equations involving gradient nonlinearity and provide a unified approach, based on the Ishii-Lions type technique, to establish Liouville property of the solutions. We also answer an open problem raised by [19]. Some applications to regularity issues are also studied., Comment: 28 pages

We consider the problem of finding spatial configurations of multiple objects in images, e.g., a mobile inspection robot is tasked to localize abandoned tools on the floor. We define the spatial configuration of objects by first-order logic in terms of relations and attributes. A neuro-symbolic program matches the logic formulas to probabilistic object proposals for the given image, provided by language-vision models by querying them for the symbols. This work is the first to combine neuro-symbolic programming (reasoning) and language-vision models (learning) to find spatial configurations of objects in images in an open world setting. We show the effectiveness by finding abandoned tools on floors and leaking pipes. We find that most prediction errors are due to biases in the language-vision model., Comment: 12 pages

We have developed an image-based convolutional neural network (CNN) that is applicable for quantitative time-resolved measurements of the fragmentation behavior of opaque brittle materials using ultra-high speed optical imaging. This model extends previous work on the U-net model, where we trained binary, 3 and 5 class models using supervised learning on experimentally measured dynamic fracture experiments on various opaque structural ceramic materials that were adhered on transparent polymer (polycarbonate or acrylic) backing materials. Full details of the experimental investigations are outside the scope of this manuscript but briefly, several different ceramics were loaded using spatially and time-varying mechanical loads to induce inelastic deformation and fracture processes that were recorded at frequencies as high as 5 MHz using high speed optical imaging. These experiments provided a rich and diverse dataset that includes many of the common fracture modes found in static and dynamic fracture including cone cracking, median cracking, comminution, and combined complex failure modes that involve effectively simultaneous activation and propagation of multiple fragmentation modes. While the training data presented here was obtained from dynamic fragmentation experiments, this study is applicable to static loading of these materials as the crack speeds typically higher a kilometer per second in these materials are on the order of 1-10 km/s regardless of the loading rate. We believe the methodologies presented here will be useful in quantifying the failure processes in structural materials for protection applications and can be used for direct validation of engineering models used in design.