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The rapid deployment of autonomous AI agents creates urgent challenges around authorization, accountability, and access control in digital spaces. New standards are needed to know whom AI agents act on behalf of and guide their use appropriately, protecting online spaces while unlocking the value of task delegation to autonomous agents. We introduce a novel framework for authenticated, authorized, and auditable delegation of authority to AI agents, where human users can securely delegate and restrict the permissions and scope of agents while maintaining clear chains of accountability. This framework builds on existing identification and access management protocols, extending OAuth 2.0 and OpenID Connect with agent-specific credentials and metadata, maintaining compatibility with established authentication and web infrastructure. Further, we propose a framework for translating flexible, natural language permissions into auditable access control configurations, enabling robust scoping of AI agent capabilities across diverse interaction modalities. Taken together, this practical approach facilitates immediate deployment of AI agents while addressing key security and accountability concerns, working toward ensuring agentic AI systems perform only appropriate actions and providing a tool for digital service providers to enable AI agent interactions without risking harm from scalable interaction.

In the basic recommendation paradigm, the most (predicted) relevant item is recommended to each user. This may result in some items receiving lower exposure than they "should"; to counter this, several algorithmic approaches have been developed to ensure item fairness. These approaches necessarily degrade recommendations for some users to improve outcomes for items, leading to user fairness concerns. In turn, a recent line of work has focused on developing algorithms for multi-sided fairness, to jointly optimize user fairness, item fairness, and overall recommendation quality. This induces the question: what is the tradeoff between these objectives, and what are the characteristics of (multi-objective) optimal solutions? Theoretically, we develop a model of recommendations with user and item fairness objectives and characterize the solutions of fairness-constrained optimization. We identify two phenomena: (a) when user preferences are diverse, there is "free" item and user fairness; and (b) users whose preferences are misestimated can be especially disadvantaged by item fairness constraints. Empirically, we prototype a recommendation system for preprints on arXiv and implement our framework, measuring the phenomena in practice and showing how these phenomena inform the design of markets with recommendation systems-intermediated matching., Comment: Accepted at the Thirty-Eighth Annual Conference on Neural Information Processing Systems

In this paper, we introduce fastHDMI, a Python package designed for efficient variable screening in high-dimensional datasets, particularly neuroimaging data. This work pioneers the application of three mutual information estimation methods for neuroimaging variable selection, a novel approach implemented via fastHDMI. These advancements enhance our ability to analyze the complex structures of neuroimaging datasets, providing improved tools for variable selection in high-dimensional spaces. Using the preprocessed ABIDE dataset, we evaluate the performance of these methods through extensive simulations. The tests cover a range of conditions, including linear and nonlinear associations, as well as continuous and binary outcomes. Our results highlight the superiority of the FFTKDE-based mutual information estimation for feature screening in continuous nonlinear outcomes, while binning-based methods outperform others for binary outcomes with nonlinear probability preimages. For linear simulations, both Pearson correlation and FFTKDE-based methods show comparable performance for continuous outcomes, while Pearson excels in binary outcomes with linear probability preimages. A comprehensive case study using the ABIDE dataset further demonstrates fastHDMI's practical utility, showcasing the predictive power of models built from variables selected using our screening techniques. This research affirms the computational efficiency and methodological strength of fastHDMI, significantly enriching the toolkit available for neuroimaging analysis., Comment: 31 pages, 5 figures

We present ExaDigiT, an open-source framework for developing comprehensive digital twins of liquid-cooled supercomputers. It integrates three main modules: (1) a resource allocator and power simulator, (2) a transient thermo-fluidic cooling model, and (3) an augmented reality model of the supercomputer and central energy plant. The framework enables the study of "what-if" scenarios, system optimizations, and virtual prototyping of future systems. Using Frontier as a case study, we demonstrate the framework's capabilities by replaying six months of system telemetry for systematic verification and validation. Such a comprehensive analysis of a liquid-cooled exascale supercomputer is the first of its kind. ExaDigiT elucidates complex transient cooling system dynamics, runs synthetic or real workloads, and predicts energy losses due to rectification and voltage conversion. Throughout our paper, we present lessons learned to benefit HPC practitioners developing similar digital twins. We envision the digital twin will be a key enabler for sustainable, energy-efficient supercomputing., Comment: 14 pages, 9 figures, To be published in the Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 2024

We investigate the effect of a constant static bias force on the dynamically induced shape morphing of a pre-buckled bistable beam, focusing on the beam's ability to change its vibration to be near different stable states under harmonic excitation. Our study explores four categories of oscillatory motions: switching, reverting, vacillating, and intra-well in the parameter space. We aim to achieve transitions between stable states of the pre-buckled bistable beam with minimal excitation amplitude. Our findings demonstrate the synergistic effects between dynamic excitation and static bias force, showing a broadening of the non-fractal region for switching behavior (i.e., switching from the first stable state to the second stable state) in the parameter space. This study advances the understanding of the dynamics of key structural components for multi-stable mechanical metamaterials, offering new possibilities for novel designs in adaptive applications.

Over the Nullarbor Plain in South Australia, the Desert Fireball Network detected a fireball on the night of 1 June 2019 (7:30 pm local time), and six weeks later recovered a single meteorite (42 g) named Arpu Kuilpu. This meteorite was then distributed to a consortium of collaborating institutions to be measured and analyzed by a number of methodologies including: SEM-EDS, EPMA, ICP-MS, gamma-ray spectrometry, ideal gas pycnometry, magnetic susceptibility measurement, {\mu}CT, optical microscopy, and accelerator and noble gas mass spectrometry techniques. These analyses revealed that Arpu Kuilpu is an unbrecciated H5 ordinary chondrite, with minimal weathering (W0-1) and minimal shock (S2). The olivine and pyroxene mineral compositions (in mol%) are Fa: 19.2 +- 0.2, and Fs: 16.8 +- 0.2, further supporting the H5 type and class. The measured oxygen isotopes are also consistent with an H chondrite ({\delta}17O = 2.904 +- 0.177; {\delta}18O = 4.163 +- 0.336; {\Delta}17O = 0.740 +- 0.002). Ideal gas pycnometry measured bulk and grain densities of 3.66 +- 0.02 and 3.77 +- 0.02 g cm-3, respectively, yielding a porosity of 3.0 % +- 0.7. The magnetic susceptibility of this meteorite is log X = 5.16 +- 0.08. The most recent impact-related heating event experienced by Arpu Kuilpu was measured by 40Ar/39Ar chronology to be 4467 +- 16 Ma, while the cosmic ray exposure age is estimated to be between 6-8 Ma. The noble gas isotopes, radionuclides, and fireball observations all indicate that Arpu Kuilpu's meteoroid was quite small (maximum radius of 10 cm, though more likely between 1-5 cm). Although this meteorite is a rather ordinary ordinary chondrite, its prior orbit resembled that of a Jupiter Family Comet (JFC) further lending support to the assertion that many cm- to m-sized objects on JFC orbits are asteroidal rather than cometary in origin.

Quasi-periodic Eruptions (QPEs) are luminous bursts of soft X-rays from the nuclei of galaxies, repeating on timescales of hours to weeks. The mechanism behind these rare systems is uncertain, but most theories involve accretion disks around supermassive black holes (SMBHs), undergoing instabilities or interacting with a stellar object in a close orbit. It has been suggested that this disk could be created when the SMBH disrupts a passing star, implying that many QPEs should be preceded by observable tidal disruption events (TDEs). Two known QPE sources show long-term decays in quiescent luminosity consistent with TDEs, and two observed TDEs have exhibited X-ray flares consistent with individual eruptions. TDEs and QPEs also occur preferentially in similar galaxies. However, no confirmed repeating QPEs have been associated with a spectroscopically confirmed TDE or an optical TDE observed at peak brightness. Here we report the detection of nine X-ray QPEs with a mean recurrence time of approximately 48 hours from AT2019qiz, a nearby and extensively studied optically-selected TDE. We detect and model the X-ray, ultraviolet and optical emission from the accretion disk, and show that an orbiting body colliding with this disk provides a plausible explanation for the QPEs.

Statistical tracking filters depend on accurate target measurements and uncertainty estimates for good tracking performance. In this work, we propose novel machine learning models for target detection and uncertainty estimation in range-Doppler map (RDM) images for Ground Moving Target Indicator (GMTI) radars. We show that by using the outputs of these models, we can significantly improve the performance of a multiple hypothesis tracker for complex multi-target air-to-ground tracking scenarios.

The development of novel materials in recent years has been accelerated greatly by the use of computational modelling techniques aimed at elucidating the complex physics controlling microstructure formation in materials, the properties of which control material function. One such technique is the phase field method, a field theoretic approach that couples various thermophysical fields to microscopic order parameter fields that track the phases of microstructure. Phase field models are framed as multiple, non-linear, partial differential equations, which are extremely challenging to compute efficiently. Recent years have seen an explosion of computational algorithms aimed at enhancing the efficiency of phase field simulations. One such technique, adaptive mesh refinement (AMR), dynamically adapts numerical meshes to be highly refined around steep spatial gradients of the PDE fields and coarser where the fields are smooth. This reduces the number of computations per time step significantly, thus reducing the total time of computation. What AMR doesn't do is allow for adaptive time stepping. This work combines AMR with a neural network algorithm that uses a U-Net with a Convolutional Long-Short Term Memory (CLSTM) base to accelerate phase field simulations. Our neural network algorithm is described in detail and tested in on simulations of directional solidification of a dilute binary alloy, a paradigm that is highly practical for its relevance to the solidification of alloys., Comment: 20 pages (2 pages of those being references), 10 figures, pre-print

Lattice walks are used to model various physical phenomena. In particular, walks within Weyl chambers connect directly to representation theory via the Littelmann path model. We derive asymptotics for centrally weighted lattice walks within the Weyl chamber corresponding to $A_2$ by using tools from analytic combinatorics in several variables (ACSV). We find universality classes depending on the weights of the walks, in line with prior results on the weighted Gouyou-Beauchamps model. Along the way, we identify a type of singularity within a multivariate rational generating function that is not yet covered by the theory of ACSV. We conjecture asymptotics for this type of singularity., Comment: To appear in 2024 Analysis of Algorithms proceedings in the Leibniz International Proceedings in Informatics

We derive asymptotic formulae for the coefficients of bivariate generating functions with algebraic and logarithmic factors. Logarithms appear when encoding cycles of combinatorial objects, and also implicitly when objects can be broken into indecomposable parts. Asymptotics are quickly computable and can verify combinatorial properties of sequences and assist in randomly generating objects. While multiple approaches for algebraic asymptotics have recently emerged, we find that the contour manipulation approach can be extended to these D-finite generating functions., Comment: To appear in the 2024 FPSAC proceedings in "S\'eminaire Lotharingien de Combinatoire"

For a Lorentzian homogeneous space, we study how algebraic conditions on the isotropy group affect the geometry and curvature of the homogeneous space. More specifically, we prove that a Lorentzian locally homogeneous space is locally isometric to a plane wave if it admits an Ambrose--Singer connection with indecomposable, non-irreducible holonomy. This generalises several existing results that require a certain algebraic type of the torsion of the Ambrose--Singer connection and moreover is in analogy to the fact that a Lorentzian homogeneous space with irreducible isotropy has constant sectional curvature. In addition, we prove results about Lorentzian connections with parallel torsion and for 2-symmetric connections., Comment: 32 pages, comments welcome. In v3 we added some results about connections with parallel torsion and about 2-symmetric connections

On 24 September 2023, the NASA OSIRIS-REx mission dropped a capsule to Earth containing approximately 120 g of pristine carbonaceous regolith from Bennu. We describe the delivery and initial allocation of this asteroid sample and introduce its bulk physical, chemical, and mineralogical properties from early analyses. The regolith is very dark overall, with higher-reflectance inclusions and particles interspersed. Particle sizes range from sub-micron dust to a stone about 3.5 cm long. Millimeter-scale and larger stones typically have hummocky or angular morphologies. A subset of the stones appears mottled by brighter material that occurs as veins and crusts. Hummocky stones have the lowest densities and mottled stones have the highest. Remote sensing of the surface of Bennu detected hydrated phyllosilicates, magnetite, organic compounds, carbonates, and scarce anhydrous silicates, all of which the sample confirms. We also find sulfides, presolar grains, and, less expectedly, Na-rich phosphates, as well as other trace phases. The sample composition and mineralogy indicate substantial aqueous alteration and resemble those of Ryugu and the most chemically primitive, low-petrologic-type carbonaceous chondrites. Nevertheless, we find distinct hydrogen, nitrogen, and oxygen isotopic compositions, and some of the material we analyzed is enriched in fluid-mobile elements. Our findings underscore the value of sample return, especially for low-density material that may not readily survive atmospheric entry, and lay the groundwork for more comprehensive analyses., Comment: 73 pages, 22 figures

As an organic semiconductor and a prototypical acceptor molecule in organic photovoltaics, C$_{60}$ has broad relevance to the world of organic thin film electronics. Although highly uniform C$_{60}$ thin films are necessary to conduct spectroscopic analysis of the electronic structure of these C$_{60}$-based materials, reported C$_{60}$ films show a relatively low degree of order beyond a monolayer. Here, we develop a generalizable two-stage growth technique that consistently produces single-domain C$_{60}$ films of controllable thicknesses, using Au(111) as an epitaxially well-matched substrate. We characterize the films using low-energy electron diffraction, low-energy electron microscopy, scanning tunneling microscopy, and angle-resolved photoemission spectroscopy (ARPES). We report highly oriented epitaxial film growth of C$_{60}$/Au(111) from 1 monolayer (ML) up to 20 ML films. The high-quality of the C$_{60}$ thin films enables the direct observation of the electronic dispersion of the HOMO and HOMO-1 bands via ARPES without need for small spot sizes. Our results indicate a path for the growth of organic films on metallic substrates with long-range ordering., Comment: 16 pages, 7 figures

High-entropy materials (HEMs) have recently emerged as a significant category of materials, offering highly tunable properties. However, the scarcity of HEM data in existing density functional theory (DFT) databases, primarily due to computational expense, hinders the development of effective modeling strategies for computational materials discovery. In this study, we introduce an open DFT dataset of alloys and employ machine learning (ML) methods to investigate the material representations needed for HEM modeling. Utilizing high-throughput DFT calculations, we generate a comprehensive dataset of 84k structures, encompassing both ordered and disordered alloys across a spectrum of up to seven components and the entire compositional range. We apply descriptor-based models and graph neural networks to assess how material information is captured across diverse chemical-structural representations. We first evaluate the in-distribution performance of ML models to confirm their predictive accuracy. Subsequently, we demonstrate the capability of ML models to generalize between ordered and disordered structures, between low-order and high-order alloys, and between equimolar and non-equimolar compositions. Our findings suggest that ML models can generalize from cost-effective calculations of simpler systems to more complex scenarios. Additionally, we discuss the influence of dataset size and reveal that the information loss associated with the use of unrelaxed structures could significantly degrade the generalization performance. Overall, this research sheds light on several critical aspects of HEM modeling and offers insights for data-driven atomistic modeling of HEMs., Comment: DFT dataset available at https://doi.org/10.5281/zenodo.10854500

In this study, we asked whether Montessori schools, which tend to have high student engagement, are associated with lower average rates of chronic absenteeism and/or smaller racial disparities therein relative to non-Montessori schools. We use multilevel modeling to answer this question, following an approach proposed in Author et al., (in press) for analyzing racial disparities in count outcomes. We identified a sample of Title 1 Montessori and non-Montessori schools using propensity score matching and data from the Civil Rights Data Collection. We did not observe significant differences in average overall rates of chronic absenteeism across school types, nor in average racial disparities in the rates of chronic absenteeism between Black and White or Hispanic and White students, though Montessori schools had slightly lower average rates for White students. We discuss shortcomings in the way chronic absenteeism data is collected, and how these shortcomings do not allow researchers to answer questions about why students are chronically absent, thereby limiting intervention work. [This paper was published in "Frontiers in Education.]

Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study suggests there is less racial discipline disproportionality in Montessori schools, so we used our new approach, along with relative rate ratios and risk differences, to compare discipline disproportionality in a sample of Title 1 Montessori and non-Montessori schools identified using propensity score matching. Using the multilevel model clarified results from other measures: discipline disproportionality was similar across school settings, even though overall rates were significantly lower in the Montessori schools. [This paper was published in "Journal of Research on Educational Effectiveness.]

Chondrules probably formed during a small window of time $\sim$1-4 Ma after CAIs, when most solid matter in the asteroid belt was already in the form of km-sized planetesimals. They are unlikely, therefore, to be ``building blocks" of planets or abundant on asteroids, but more likely to be a product of energetic events common in the asteroid belt at that epoch. Laboratory experiments indicate that they could have formed when solids of primitive composition were heated to temperatures of $\sim$1600 K and then cooled for minutes to hours. A plausible heat source for this is magma, which is likely to have been abundant in the asteroid belt at that time, and only that time, due to the trapping of $^{26}$Al decay energy in planetesimal interiors. Here we propose that chondrules formed during low-speed ($\lesssim1$ km s$^{-1}$) collisions between large planetesimals when heat from their interiors was released into a stream of primitive debris from their surfaces. Heating would have been essentially instantaneous and cooling would have been on the dynamical time scale, 1/$\sqrt(G \rho) \sim 30$ minutes, where $\rho$ is the mean density of a planetesimal. Many of the heated fragments would have remained gravitationally bound to the merged object and could have suffered additional heating events as they orbited and ultimately accreted to its surface. This is a hybrid of the splash and flyby models: we propose that it was the energy released from a body's molten interior, not its mass, that was responsible for chondrule formation by heating primitive debris that emerged from the collision., Comment: 17 pages, 3 figures. Accepted for publication in Meteoritics and Planetary Science