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The detailed guide's purpose is to assist educators and community leaders in designing and implementing educational programs to serve student and community needs, and to identify the goals and elements of cooperative vocational education. One chapter describes the necessary characteristics of the teacher-coordinator in such programs, and the advantages of cooperative vocational education to the community and to the students. Two other chapters suggest methods for determining employers' and students' needs for cooperative vocational education and suggest plans for meeting these needs. Three chapters deal with implementing plans for: public relations (including a suggested eight point plan and the use of media); instruction in the school (including five teaching methods and the use of youth organizations); and community laboratories (including criteria for selecting, establishing, and evaluating training stations, and guidelines for complying with appropriate laws). Another chapter analyzes essential elements of the cooperative program including staffing; facilities, equipment, and materials; and program development. A final chapter suggests ways of improving cooperative vocational education in its administrative structure, professional development of teacher-coordinators, instructional materials, and program evaluation. A 70-page appendix displays forms appropriate to the planning, implementation, and evaluation of programs. (JR)

The systematic development of horticulture curriculum for Indiana was the focus of this research project which validated a job task list for use in instructional material development. The job title catalog, A Landscape Gardener, was selected from those currently available through the Vocational-Technical Consortium of States (V-TECS) program. A purposive study as outlined in the V-TECS technical reference handbook was undertaken to validate this job title catalog for Indiana. Survey instruments were sent to job incumbent personnel in horticulture businesses and data from twenty returned surveys was tabulated and analyzed. From the selected list of 165, job incumbents selected 109 as those most commonly performed, also indicating tools commonly used and amount of time spent at various tasks. Finally the validated list of tasks contained in the job title catalog were sequenced to facilitate further work in instructional materials development. (Survey instruments and survey data are included in the appendixes.) (JH)

Asian Americans as a group are overrepresented among STEM college graduates and have the highest average college enrollment rate of any racial or ethnic category. Thus, Asian Americans are typically excluded from educational interventions directed at improving STEM education for Students of Color because they are not considered to be underrepresented minorities. However, statistics obscure the individual needs of the more than 20 ethnic subgroups that fall under the umbrella term Asian Americans. Using a participatory action research approach, this paper documents the institutional and sociocultural factors that push out HMoob (or Hmong) American college students from STEM programs at one large, predominantly White university; and the coordinate processes of gatekeeping and transactional advising that either redirect those students toward non-STEM programs or force them out of the university completely.

Large language models (LLMs) provide a compelling foundation for building generally-capable AI agents. These agents may soon be deployed at scale in the real world, representing the interests of individual humans (e.g., AI assistants) or groups of humans (e.g., AI-accelerated corporations). At present, relatively little is known about the dynamics of multiple LLM agents interacting over many generations of iterative deployment. In this paper, we examine whether a "society" of LLM agents can learn mutually beneficial social norms in the face of incentives to defect, a distinctive feature of human sociality that is arguably crucial to the success of civilization. In particular, we study the evolution of indirect reciprocity across generations of LLM agents playing a classic iterated Donor Game in which agents can observe the recent behavior of their peers. We find that the evolution of cooperation differs markedly across base models, with societies of Claude 3.5 Sonnet agents achieving significantly higher average scores than Gemini 1.5 Flash, which, in turn, outperforms GPT-4o. Further, Claude 3.5 Sonnet can make use of an additional mechanism for costly punishment to achieve yet higher scores, while Gemini 1.5 Flash and GPT-4o fail to do so. For each model class, we also observe variation in emergent behavior across random seeds, suggesting an understudied sensitive dependence on initial conditions. We suggest that our evaluation regime could inspire an inexpensive and informative new class of LLM benchmarks, focussed on the implications of LLM agent deployment for the cooperative infrastructure of society., Comment: 15 pages, 6 figures

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive, an edge-AI based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page: \url{https://ucd-dare.github.io/eidrive.github.io/}.

We assess the sensitivity of future lepton colliders to weakly coupled vector dark portals (aka ``$ Z' $ bosons'') with masses ranging from tens of GeV to a few TeV. Our analysis focuses on dark photons and $ L_{\mu} - L_{\tau} $ gauge bosons. We consider both visible and invisible decay channels. We demonstrate that both high energy $\mu$ colliders and future $ e^+e^- $ colliders, using the FCC-ee $Z$-pole and $ZH$ operation modes as a benchmark, offer significant improvements in sensitivity. We find that both colliders can enhance the sensitivity to $ L_{\mu} - L_{\tau} $ bosons (for both visible and invisible decays) and to invisibly decaying dark photons by 1--2 orders of magnitude across the relevant mass range. Furthermore, we study the impact of forward $ \mu $ detectors at the $ \mu $-collider on the sensitivity to both models., Comment: 20 pages, 8 figures

The study of large-scale structure can benefit from accurate and robust identification of the cosmic web. Having such classification can facilitate a more complete extraction of cosmological information encoded therein. Classification methods like T-web and V-web, based on the Hessian matrix, are widely used to signal-out voids, sheets, filaments, and knots. However, these techniques depend on a threshold parameter which value is chosen without physical justification, usually relying on a user visual impression, thus limiting the universality of results. In this paper we focus on the V-web method. Our aim is to find a physical motivation for deriving an universal threshold that can be applied across different cosmic scales and epochs. V-web classify the large-scale structure using the eigenvalues of the velocity shear tensor. Using a set of gravity-only simulations we introduce a normalization that incorporates the standard deviation of the velocity divergence field, isolating the beyond Gaussian evolution of cosmic web elements. In the Zeldovich's approximation, the probability presence of each cosmic web element remains constant at a threshold equal to 0. For the first time, we reveal that this behavior also holds in the non-linear regime for a normalized positive 'constant volume threshold' that depends on both the redshift and the applied smoothing scale. The conservation of volume fractions is valid for the studied redshifts between 0 and 2, regardless of cosmic variance, and is most precise for intermediate smoothing scales around 3 Mpc/h. The properties of the cosmic web derived using this approach in the V-web align with expectations from other methods, including visual impressions. We provide a general fit formula to compute the constant volume threshold for any standard cosmological simulation, regardless of its specific properties., Comment: Submitted for publication in A&A

Given recent increases in ocean water levels brought on by climate change, this investigation decomposed changes in coastal water levels into its fundamental components to predict maximum water levels for a given coastal location. The study focused on Virginia Key, Florida, in the United States, located near the coast of Miami. Hourly mean lower low water (MLLW) levels were obtained from the National Data Buoy Center from January 28, 1994, through December 31, 2023. In the temporal dimension, Kolmogorov-Zurbenko filters were used to extract long-term trends, annual and daily tides, and higher frequency harmonics, while in the spectral dimension, Kolmogorov-Zurbenko periodograms with DiRienzo-Zurbenko algorithm smoothing were used to confirm known tidal frequencies and periods. A linear model predicted that the long-term trend in water level will rise 2.02 feet from January 1994 to December 2050, while a quadratic model predicted a rise of 5.91 during the same period. In addition, the combined crests of annual tides, daily tides, and higher frequency harmonics increase water levels up to 2.16 feet, yielding a combined total of 4.18 feet as a lower bound and a combined total of 8.09 feet as an upper bound. These findings provide a foundation for more accurate prediction of coastal flooding during severe weather events and provide an impetus for policy choices with respect to residential communities, businesses, and wildlife habitats. Further, using Kolmogorov-Zurbenko analytic methods to study coastal sites throughout the world could draw a more comprehensive picture of the impact climate change is having on coastal waters globally., Comment: 23 pages, 7 figures, this article draws from arXiv:2007.03031v3 and arXiv:2412.07735v1

Background: This protocol introduces an ultrasound (US) configuration for whole-body 3D dynamic contrast-enhanced ultrasound (DCE-US) imaging in preclinical applications. The set-up relies on a clinical abdominal matrix US probe to enable mice imaging beyond current preclinical systems that are generally unable to capture whole-body volumetric and dynamic imaging. We demonstrate via this protocol the feasibility of volumetric contrast-enhanced and molecular 3D imaging in the entire lower body of mouse, as well as the capability of imaging multiple lesions in the same animal simultaneously with a single contrast bolus injection. Methods: We modified a silicone cup with a 101.6 mm inner diameter and 6.4 mm wall thickness, to a height of 76.2 mm and cut out a rectangular side window (12.7 mm x 15.9 mm) at the lower part of the cup. Mice were positioned with their head and the two front legs outside of the cup while the rest of the body remained inside the cup and submerged under water; the edges around the mouse's body in contact with the cup's wall were sealed with Vaseline to prevent water leakage, and the cup was filled with warm water to maintain the mouse's body heat. Imaging was conducted using a portable clinical ultrasound system (EPIQ 7 Philips) with an abdominal matrix-array transducer (PM mode, X6-1) inserted into the top of the cup, and positioned to visualize the entire of the mouse's abdomen. The mice were imaged before and after the injection of P-selectin-targeted microbubbles. Results: Here, in nude mice with two adjacent tumors, we demonstrate that a commercially available clinical matrix transducer can be utilized to achieve whole-body 3D DCE-US and molecular US imaging, as well as capture independent qualitative and quantitative information from several lesions or organs at different locations in the animal, through a single bolus injection., Comment: 16 pages, 4 figures

We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside the three CALSPEC standards, from 912 \r{A} to 32 $\mu$m. The framework is the first of its kind to jointly infer photometric zeropoints and WD parameters ($\log g$, $T_{\text{eff}}$, $A_V$, $R_V$) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic HST/WFC3 UVIS and IR fluxes, HST/STIS UV spectroscopy and ground-based optical spectroscopy to sub-percent precision. Photometric residuals for the sample are the lowest yet yielding $<0.004$ mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables efficient sampling via Hamiltonian Monte Carlo, critical for exploring the high-dimensional posterior space. The hierarchical nature of the model enables population analysis of intrinsic WD and dust parameters. Inferred SEDs from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory's Legacy Survey of Space and Time, and the Nancy Grace Roman Space Telescope., Comment: 32 pages, 24 figures, 5 tables, submitted to MNRAS

This investigation establishes the theoretical and practical limits of signal strength estimate precision for Kolmogorov-Zurbenko periodograms with dynamic smoothing and compares them to those of standard log-periodograms with static smoothing. Previous research has established the sensitivity, accuracy, resolution, and robustness of Kolmogorov-Zurbenko periodograms with dynamic smoothing in estimating signal frequencies. However, the precision with which they estimate signal strength has never been evaluated. To this point, the width of the confidence interval for a signal strength estimate can serve as a criterion for assessing the precision of such estimates: the narrower the confidence interval, the more precise the estimate. The statistical background for confidence intervals of periodograms is presented, followed by candidate functions to compute and plot them when using Kolmogorov-Zurbenko periodograms with dynamic smoothing. Given an identified signal frequency, a static smoothing window and its smoothing window width can be selected such that its confidence interval is narrower and, thus, its signal strength estimate more precise, than that of dynamic smoothing windows, all while maintaining a level of frequency resolution as good as or better than that of a dynamic smoothing window. These findings suggest the need for a two-step protocol in spectral analysis: computation of a Kolmogorov-Zurbenko periodogram with dynamic smoothing to detect, identify, and separate signal frequencies, followed by computation of a Kolmogorov-Zurbenko periodogram with static smoothing to precisely estimate signal strength and compute its confidence intervals., Comment: 32 pages, 8 figures, this article draws from arXiv:2007.03031v3

Total variation (TV) is a widely used function for regularizing imaging inverse problems that is particularly appropriate for images whose underlying structure is piecewise constant. TV regularized optimization problems are typically solved using proximal methods, but the way in which they are applied is constrained by the absence of a closed-form expression for the proximal operator of the TV function. A closed-form approximation of the TV proximal operator has previously been proposed, but its accuracy was not theoretically explored in detail. We address this gap by making several new theoretical contributions, proving that the approximation leads to a proximal operator of some convex function, that it always decreases the TV function, and that its error can be fully characterized and controlled with its scaling parameter. We experimentally validate our theoretical results on image denoising and sparse-view computed tomography (CT) image reconstruction.

Intermediate mass black holes (10^2 < M_BH< 10^5 Msun) are an open question in our understanding of black hole evolution and growth. They have long been linked to dense star cluster environments thanks to cluster dynamics, but there are a limited number of secure detections. We leverage existing X-ray observations from Chandra X-ray Observatory and optical catalogs from Hubble Space Telescope with new radio observations from the Karl G. Jansky Very Large Array to search for any evidence of accreting black holes in young massive clusters in the nearby galaxy M51. We find that of 43 bright ($L_X > 10^{38}$ erg/s) X-ray point sources in M51, 24 had probable matches to objects including possible associated star clusters in the HST Legacy Extragalactic UV Survey catalog, seven of which were classified as contaminants (background galaxies or foreground stars). We explore the optical properties of the remaining 17 sources, including cluster age and mass estimates, and search for radio counterparts in the 8-12 GHz band. The lack of radio counterparts to X-ray sources we know to be associated with young massive clusters in M51 suggests that we do not significantly detect hard-state IMBHs ~ 10^4 Msun or above. However, more sensitive radio facilities like the Square Kilometre Array and next generation Very Large Array may be able to provide evidence for IMBHs with masses down to ~ 10^3 Msun., Comment: 18 pages, 7 figures, accepted to ApJ

The new era of galaxy evolution studies hearkened in by JWST has led to the discovery of z > 5 galaxies exhibiting excess nitrogen with log(N/O)~1 dex or more than expected from log(N/O) vs 12+log(O/H) trends in the local Universe. A variety of novel enrichment pathways have been presented to explain the apparent nitrogen excess, invoking a wide range of processes from very massive stars to stripped binaries to fine-tuned star-formation histories. However, understanding the excitation mechanism responsible for the observed nebular emission is necessary to accurately infer chemical abundances. As of yet, the ionization sources of these galaxies have not been thoroughly explored, with radiative shocks left out of the picture. We present a suite of homogeneous excitation models for star-forming galaxies, active galactic nuclei, and radiative shocks, with which we explore possible explanations for the apparent nitrogen excess. We propose new BPT-style diagnostics to classify galaxies at z > 5, finding that, when combined with O iii] 1660,66 and He ii 1640, N iii] 1747-54 / C iii] 1907,09 best selects shock-dominated galaxies while N iv] 1483,86 / C iii] 1907,09 best distinguishes between active black holes and star forming galaxies. From our diagnostics, we find that slow/intermediate radiative shocks (v = 75-150 km/s) are most consistent with observed UV emission line flux ratios in nitrogen-bright galaxies. Accounting for the effects of shocks can bring nitrogen estimates into better agreement with abundance patterns observed in the local Universe and may be attributable to Wolf Rayet populations actively enriching these galaxies with nitrogen and possibly driving winds responsible for these shocks., Comment: submitted to MNRAS, 13 pages, 5 figures

The Atacama Cosmology Telescope Data Release 6 (ACT DR6) power spectrum is expected to provide state-of-the-art cosmological constraints, with an associated need for precise error modeling. In this paper we design, and evaluate the performance of, an analytic covariance matrix prescription for the DR6 power spectrum that sufficiently accounts for the complicated ACT map properties. We use recent advances in the literature to handle sharp features in the signal and noise power spectra, and account for the effect of map-level anisotropies on the covariance matrix. In including inhomogeneous survey depth information, the resulting covariance matrix prescription is structurally similar to that used in the $\textit{Planck}$ Cosmic Microwave Background (CMB) analysis. We quantify the performance of our prescription using comparisons to Monte Carlo simulations, finding better than $3\%$ agreement. This represents an improvement from a simpler, pre-existing prescription, which differs from simulations by $\sim16\%$. We develop a new method to correct the analytic covariance matrix using simulations, after which both prescriptions achieve better than $1\%$ agreement. This correction method outperforms a commonly used alternative, where the analytic correlation matrix is assumed to be accurate when correcting the covariance. Beyond its use for ACT, this framework should be applicable for future high resolution CMB experiments including the Simons Observatory (SO)., Comment: 22 pages (+11 appendix), 10 figures (+5 appendix), submitted to JCAP

Utilizing machine learning techniques has always required choosing hyperparameters. This is true whether one uses a classical technique such as a KNN or very modern neural networks such as Deep Learning. Though in many applications, hyperparameters are chosen by hand, automated methods have become increasingly more common. These automated methods have become collectively known as automated machine learning, or AutoML. Several automated selection algorithms have shown similar or improved performance over state-of-the-art methods. This breakthrough has led to the development of cloud-based services like Google AutoML, which is based on Deep Learning and is widely considered to be the industry leader in AutoML services. Extreme Learning Machines (ELMs) use a fundamentally different type of neural architecture, producing better results at a significantly discounted computational cost. We benchmark the Extreme AutoML technology against Google's AutoML using several popular classification data sets from the University of California at Irvine's (UCI) repository, and several other data sets, observing significant advantages for Extreme AutoML in accuracy, Jaccard Indices, the variance of Jaccard Indices across classes (i.e. class variance) and training times., Comment: 8 pages, 7 figures

Advances in artificial intelligence (AI) present significant risks and opportunities, requiring improved governance to mitigate societal harms and promote equitable benefits. Current incentive structures and regulatory delays may hinder responsible AI development and deployment, particularly in light of the transformative potential of large language models (LLMs). To address these challenges, we propose developing the following three contributions: (1) a large multimodal text and economic-timeseries foundation model that integrates economic and natural language policy data for enhanced forecasting and decision-making, (2) algorithmic mechanisms for eliciting diverse and representative perspectives, enabling the creation of data-driven public policy recommendations, and (3) an AI-driven web platform for supporting transparent, inclusive, and data-driven policymaking.

Countless science and engineering applications in multi-objective optimization (MOO) necessitate that decision-makers (DMs) select a Pareto-optimal solution which aligns with their preferences. Evaluating individual solutions is often expensive, necessitating cost-sensitive optimization techniques. Due to competing objectives, the space of trade-offs is also expansive -- thus, examining the full Pareto frontier may prove overwhelming to a DM. Such real-world settings generally have loosely-defined and context-specific desirable regions for each objective function that can aid in constraining the search over the Pareto frontier. We introduce a novel conceptual framework that operationalizes these priors using soft-hard functions, SHFs, which allow for the DM to intuitively impose soft and hard bounds on each objective -- which has been lacking in previous MOO frameworks. Leveraging a novel minimax formulation for Pareto frontier sampling, we propose a two-step process for obtaining a compact set of Pareto-optimal points which respect the user-defined soft and hard bounds: (1) densely sample the Pareto frontier using Bayesian optimization, and (2) sparsify the selected set to surface to the user, using robust submodular function optimization. We prove that (2) obtains the optimal compact Pareto-optimal set of points from (1). We further show that many practical problems fit within the SHF framework and provide extensive empirical validation on diverse domains, including brachytherapy, engineering design, and large language model personalization. Specifically, for brachytherapy, our approach returns a compact set of points with over 3% greater SHF-defined utility than the next best approach. Among the other diverse experiments, our approach consistently leads in utility, allowing the DM to reach >99% of their maximum possible desired utility within validation of 5 points.

In the evolving landscape of machine learning (ML), Federated Learning (FL) presents a paradigm shift towards decentralized model training while preserving user data privacy. This paper introduces the concept of ``privacy drift", an innovative framework that parallels the well-known phenomenon of concept drift. While concept drift addresses the variability in model accuracy over time due to changes in the data, privacy drift encapsulates the variation in the leakage of private information as models undergo incremental training. By defining and examining privacy drift, this study aims to unveil the nuanced relationship between the evolution of model performance and the integrity of data privacy. Through rigorous experimentation, we investigate the dynamics of privacy drift in FL systems, focusing on how model updates and data distribution shifts influence the susceptibility of models to privacy attacks, such as membership inference attacks (MIA). Our results highlight a complex interplay between model accuracy and privacy safeguards, revealing that enhancements in model performance can lead to increased privacy risks. We provide empirical evidence from experiments on customized datasets derived from CIFAR-100 (Canadian Institute for Advanced Research, 100 classes), showcasing the impact of data and concept drift on privacy. This work lays the groundwork for future research on privacy-aware machine learning, aiming to achieve a delicate balance between model accuracy and data privacy in decentralized environments., Comment: 6 pages, 7 figures, Accepted in IEEE ICNC 25

This technical report introduces the EXAONE 3.5 instruction-tuned language models, developed and released by LG AI Research. The EXAONE 3.5 language models are offered in three configurations: 32B, 7.8B, and 2.4B. These models feature several standout capabilities: 1) exceptional instruction following capabilities in real-world scenarios, achieving the highest scores across seven benchmarks, 2) outstanding long-context comprehension, attaining the top performance in four benchmarks, and 3) competitive results compared to state-of-the-art open models of similar sizes across nine general benchmarks. The EXAONE 3.5 language models are open to anyone for research purposes and can be downloaded from https://huggingface.co/LGAI-EXAONE. For commercial use, please reach out to the official contact point of LG AI Research: contact_us@lgresearch.ai., Comment: arXiv admin note: text overlap with arXiv:2408.03541

The concern that Artificial Intelligence (AI) and Machine Learning (ML) are entering a "reproducibility crisis" has spurred significant research in the past few years. Yet with each paper, it is often unclear what someone means by "reproducibility". Our work attempts to clarify the scope of "reproducibility" as displayed by the community at large. In doing so, we propose to refine the research to eight general topic areas. In this light, we see that each of these areas contains many works that do not advertise themselves as being about "reproducibility", in part because they go back decades before the matter came to broader attention., Comment: To appear in AAAI 2025, Senior Member Presentation Track

We present the first circularly polarized Floquet engineering time-resolved Resonant Inelastic X-ray Scattering (tr-RIXS) experiment in H$_3$LiIr$_2$O$_6$, an iridium-based Kitaev system. Our calculations and experimental results are consistent with the modification of the low energy magnetic excitations in H$_3$LiIr$_2$O$_6$ only during illumination by the laser pulse, consistent with the Floquet engineering of the exchange interactions. However, the penetration length mismatch between the X-ray probe and laser pump and the intrinsic complexity of Kitaev magnets prevented us from unequivocally extracting towards which ground H$_3$LiIr$_2$O$_6$ was driven. We outline possible solutions to these challenges for Floquet stabilization and observation of the Kitaev Quantum Spin Liquid limit by RIXS.

Estimating the redshifts of distant galaxies is critical for determining their intrinsic properties, as well as for using them as cosmological probes. Measuring redshifts spectroscopically is accurate, but expensive in terms of telescope time, hence it has become common to measure `photometric' redshifts, which are fits to photometry taken in a number of filters using templates of galaxy spectral energy distributions (SEDs). However, most photometric methods rely on optical and near-infrared (NIR) photometry, neglecting longer wavelength data in the far-infrared (FIR) and millimeter. Since the ultimate goal of future surveys is to obtain redshift estimates for all galaxies, it is important to improve photometric redshift algorithms for cases where optical/NIR fits fail to produce reliable results. For specific subsets of galaxies, in particular dusty star-forming galaxies (DSFGs), it can be particularly hard to obtain good optical photometry and thus reliable photometric redshift estimates, while these same galaxies are often bright at longer wavelengths. Here we describe a new method for independently incorporating FIR-to-millimeter photometry to the outputs of standard optical/NIR SED-fitting codes to help improve redshift estimation, in particular of DSFGs. We test our method with the H-ATLAS catalog, which contains FIR photometry from Herschel-SPIRE cross-matched to optical and NIR observations, and show that our approach reduces the number of catastrophic outliers by a factor of three compared to standard optical and NIR SED-fitting routines alone., Comment: Submitted to ApJ

The blue loop stage of intermediate mass stars has been called a "magnifying glass", where even seemingly small effects in prior stages of evolution, as well as assumptions about stellar composition, rotation, and convection, produce discernible changes. As such, blue loops, and especially the existence and properties of Cepheids, can serve as a laboratory where feebly connected Beyond Standard Model particles such as axions can be gainfully studied. We undertake a careful study of the effects of these putative particles on the blue loop, paying close attention to the evolution of the core potential and the hydrogen profile. Our simulations, performed with MESA, place bounds on the axion-photon coupling using the galactic Cepheid S Mus, with dynamically-determined mass of $6 M_\odot$, as a benchmark. The effects of varying convective overshoot on the core potential and hydrogen profile, and the ensuing changes in the axion constraints, are carefully studied. Along the way, we explore the "mirror principle" induced by the hydrogen burning shell and contrast our results with those existing in the literature. Less conservative (but more stringent) bounds on the axion-photon coupling are given for a $9 M_\odot$ model, which is the heaviest that can be simulated if overshoot is incorporated, and tentative projections are given for a $12 M_\odot$ model, which is approximately the heaviest tail of the mass distribution of galactic Cepheids determined by pulsation models using Gaia DR2. Our main message is that the reliable simulation and observation (ideally, through dynamical mass determination) of massive Cepheids constitutes an important frontier in axion searches, challenges in modeling uncertainties in the microphysics of the blue loop stage notwithstanding., Comment: 28+3 pages, 20 figures

We present Argos, a simple approach for adding verifiability to fully homomorphic encryption (FHE) schemes using trusted hardware. Traditional approaches to verifiable FHE require expensive cryptographic proofs, which incur an overhead of up to seven orders of magnitude on top of FHE, making them impractical. With Argos, we show that trusted hardware can be securely used to provide verifiability for FHE computations, with minimal overhead relative to the baseline FHE computation. An important contribution of Argos is showing that the major security pitfall associated with trusted hardware, microarchitectural side channels, can be completely mitigated by excluding any secrets from the CPU and the memory hierarchy. This is made possible by focusing on building a platform that only enforces program and data integrity and not confidentiality (which is sufficient for verifiable FHE, since all data remain encrypted at all times). All secrets related to the attestation mechanism are kept in a separate coprocessor (e.g., a TPM) inaccessible to any software-based attacker. Relying on a discrete TPM typically incurs significant performance overhead, which is why (insecure) software-based TPMs are used in practice. As a second contribution, we show that for FHE applications, the attestation protocol can be adapted to only incur a fixed cost. Argos requires no dedicated hardware extensions and is supported on commodity processors from 2008 onward. Our prototype implementation introduces 6% overhead to the FHE evaluation, and 8% for more complex protocols. In particular, we show that Argos can be adapted for real-world applications of FHE, such as PIR and PSI. By demonstrating how to combine cryptography with trusted hardware, Argos paves the way for widespread deployment of FHE-based protocols beyond the semi-honest setting, without the overhead of cryptographic proofs.

We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the host star reaches the main sequence in 1 Gyr and that planet c lies close to the inner boundary of the habitable zone. We find the magma ocean stage can last up to 50 Myr, remove 8 Earth-oceans of water, and leave up to 2000 bars of oxygen in the atmosphere. However, if the planet forms with a hydrogen envelope as small as 0.1 Earth-masses, no water will be lost during the star's pre-main sequence phase from thermal escape processes. We find that the planets are unlikely to be in a 3:1 mean motion resonance and that both planets tidally circularize within 0.5 Gyr when tidal dissipation is held constant. However, if tidal dissipation is a function of mantle temperature and rheology, then we find that planet c's orbit may require more than 7 Gyr to circularize, during which time tidal heating may reach hundreds of terawatts. We thus conclude that the habitability of planet c depends most strongly on the initial volatile content and internal properties, but no data yet preclude the viability of an active biosphere on the planet., Comment: 16 pages, 9 figures, accepted to PSJ

In the pursuit of fusion power, achieving tritium self-sufficiency stands as a pivotal challenge. Tritium breeding within molten salts is a critical aspect of next-generation fusion reactors, yet experimental measurements of \gls{tbr} have remained elusive. Here we present the results of the \gls{baby} experiment, which represents a pioneering effort in tritium research by utilizing high-energy (\SI{14}{\mega\electronvolt}) neutron irradiation of molten salts, a departure from conventional low-energy neutron approaches. Using a small-scale (\SI{100}{\milli\litre}) molten salt tritium breeding setup, we not only simulated, but also directly measured a \gls{tbr}. This innovative approach provides crucial experimental validation, offering insights unattainable through simulation alone. Moreover, our findings reveal a surprising outcome: tritium was predominantly collected as HT, contrary to the expected TF. This underscores the complexity of tritium behavior in molten salts, highlighting the need for further investigation. This work lays the foundation for a more sophisticated experimental setup, including increasing the volume of the breeder, enhancing neutron detection, and refining tritium collection systems. Such improvements are crucial for advancing our understanding of fusion reactor feasibility and paving the way for future experiments.

The rise of intelligent autonomous systems, especially in robotics and autonomous agents, has created a critical need for robust communication middleware that can ensure real-time processing of extensive sensor data. Current robotics middleware like Robot Operating System (ROS) 2 faces challenges with nondeterminism and high communication latency when dealing with large data across multiple subscribers on a multi-core compute platform. To address these issues, we present High-Performance Robotic Middleware (HPRM), built on top of the deterministic coordination language Lingua Franca (LF). HPRM employs optimizations including an in-memory object store for efficient zero-copy transfer of large payloads, adaptive serialization to minimize serialization overhead, and an eager protocol with real-time sockets to reduce handshake latency. Benchmarks show HPRM achieves up to 173x lower latency than ROS2 when broadcasting large messages to multiple nodes. We then demonstrate the benefits of HPRM by integrating it with the CARLA simulator and running reinforcement learning agents along with object detection workloads. In the CARLA autonomous driving application, HPRM attains 91.1% lower latency than ROS2. The deterministic coordination semantics of HPRM, combined with its optimized IPC mechanisms, enable efficient and predictable real-time communication for intelligent autonomous systems., Comment: 7 pages

The downstream use cases, benefits, and risks of AI systems depend significantly on the access afforded to the system, and to whom. However, the downstream implications of different access styles are not well understood, making it difficult for decision-makers to govern model access responsibly. Consequently, we spotlight Model Access Governance, an emerging field focused on helping organisations and governments make responsible, evidence-based access decisions. We outline the motivation for developing this field by highlighting the risks of misgoverning model access, the limitations of existing research on the topic, and the opportunity for impact. We then make four sets of recommendations, aimed at helping AI evaluation organisations, frontier AI companies, governments and international bodies build consensus around empirically-driven access governance.

We investigate how advanced large language models (LLMs), specifically GPT-4, process corporate disclosures to forecast earnings. Using earnings press releases issued around GPT-4's knowledge cutoff date, we address two questions: (1) Do GPT-generated earnings forecasts outperform analysts in accuracy? (2) How is GPT's performance related to its processing of textual and quantitative information? Our findings suggest that GPT forecasts are significantly less accurate than those of analysts. This underperformance can be traced to GPT's distinct textual and quantitative approaches: its textual processing follows a consistent, generalized pattern across firms, highlighting its strengths in language tasks. In contrast, its quantitative processing capabilities vary significantly across firms, revealing limitations tied to the uneven availability of domain-specific training data. Additionally, there is some evidence that GPT's forecast accuracy diminishes beyond its knowledge cutoff, underscoring the need to evaluate LLMs under hindsight-free conditions. Overall, this study provides a novel exploration of the "black box" of GPT-4's information processing, offering insights into LLMs' potential and challenges in financial applications.

Evaluations of Large Language Models (LLMs) on knowledge-intensive tasks and factual accuracy often focus on high-resource languages primarily because datasets for low-resource languages (LRLs) are scarce. In this paper, we present Uhura -- a new benchmark that focuses on two tasks in six typologically-diverse African languages, created via human translation of existing English benchmarks. The first dataset, Uhura-ARC-Easy, is composed of multiple-choice science questions. The second, Uhura-TruthfulQA, is a safety benchmark testing the truthfulness of models on topics including health, law, finance, and politics. We highlight the challenges creating benchmarks with highly technical content for LRLs and outline mitigation strategies. Our evaluation reveals a significant performance gap between proprietary models such as GPT-4o and o1-preview, and Claude models, and open-source models like Meta's LLaMA and Google's Gemma. Additionally, all models perform better in English than in African languages. These results indicate that LMs struggle with answering scientific questions and are more prone to generating false claims in low-resource African languages. Our findings underscore the necessity for continuous improvement of multilingual LM capabilities in LRL settings to ensure safe and reliable use in real-world contexts. We open-source the Uhura Benchmark and Uhura Platform to foster further research and development in NLP for LRLs., Comment: working paper

We propose a simple approximation of the noncommutative integral in noncommutative geometry for the Connes--Van Suijlekom paradigm of spectrally truncated spectral triples. A close connection between this approximation and the field of quantum ergodicity and work by Widom in particular immediately provides a Szeg\H{o} limit formula for noncommutative geometry. Finally, we propose a definition for the ergodicity of geodesic flow for compact spectral triples. This definition is known in quantum ergodicity as uniqueness of the vacuum state for $C^*$-dynamical systems, and for spectral triples where local Weyl laws hold this implies that the Dirac operator of the spectral triple is quantum ergodic. This brings to light a close connection between quantum ergodicity and Connes' integral formula., Comment: 26 pages, no figures

Planning safe and efficient trajectories through signal-free intersections presents significant challenges for autonomous vehicles (AVs), particularly in dynamic, multi-task environments with unpredictable interactions and an increased possibility of conflicts. This study aims to address these challenges by developing a robust, adaptive framework to ensure safety in such complex scenarios. Existing approaches often struggle to provide reliable safety mechanisms in dynamic and learn multi-task behaviors from demonstrations in signal-free intersections. This study proposes a safety-critical planning method that integrates Dynamic High-Order Control Barrier Functions (DHOCBF) with a diffusion-based model, called Dynamic Safety-Critical Diffuser (DSC-Diffuser), offering a robust solution for adaptive, safe, and multi-task driving in signal-free intersections. Our approach incorporates a goal-oriented, task-guided diffusion model, enabling the model to learn multiple driving tasks simultaneously from real-world data. To further ensure driving safety in dynamic environments, the proposed DHOCBF framework dynamically adjusts to account for the movements of surrounding vehicles, offering enhanced adaptability compared to traditional control barrier functions. Validity evaluations of DHOCBF, conducted through numerical simulations, demonstrate its robustness in adapting to variations in obstacle velocities, sizes, uncertainties, and locations, effectively maintaining driving safety across a wide range of complex and uncertain scenarios. Performance evaluations across various scenes confirm that DSC-Diffuser provides realistic, stable, and generalizable policies, equipping it with the flexibility to adapt to diverse driving tasks., Comment: 7 figures, 3 tables, 12 pages

The eigenvalues and eigenvectors of nonnormal matrices can be unstable under perturbations of their entries. This renders an obstacle to the analysis of numerical algorithms for non-Hermitian eigenvalue problems. A recent technique to handle this issue is pseudospectral shattering [BGVKS23], showing that adding a random perturbation to any matrix has a regularizing effect on the stability of the eigenvalues and eigenvectors. Prior work has analyzed the regularizing effect of dense Gaussian perturbations, where independent noise is added to every entry of a given matrix [BVKS20, BGVKS23, BKMS21, JSS21]. We show that the same effect can be achieved by adding a sparse random perturbation. In particular, we show that given any $n\times n$ matrix $M$ of polynomially bounded norm: (a) perturbing $O(n\log^2(n))$ random entries of $M$ by adding i.i.d. complex Gaussians yields $\log\kappa_V(A)=O(\text{poly}\log(n))$ and $\log (1/\eta(A))=O(\text{poly}\log(n))$ with high probability; (b) perturbing $O(n^{1+\alpha})$ random entries of $M$ for any constant $\alpha>0$ yields $\log\kappa_V(A)=O_\alpha(\log(n))$ and $\log(1/\eta(A))=O_\alpha(\log(n))$ with high probability. Here, $\kappa_V(A)$ denotes the condition number of the eigenvectors of the perturbed matrix $A$ and $\eta(A)$ denotes its minimum eigenvalue gap. A key mechanism of the proof is to reduce the study of $\kappa_V(A)$ to control of the pseudospectral area and minimum eigenvalue gap of $A$, which are further reduced to estimates on the least two singular values of shifts of $A$. We obtain the required least singular value estimates via a streamlining of an argument of Tao and Vu [TV07] specialized to the case of sparse complex Gaussian perturbations.

Positronium and muonium, as purely leptonic atoms without internal structure, provide ideal systems for high-precision tests of quantum electrodynamics (QED) and measurements of fundamental constants. However, the high velocities of these lightweight atoms complicate precision spectroscopy, particularly in the 1S-2S transition, due to transit time broadening and second-order Doppler shifts. To overcome these challenges, we propose a novel method combining two-photon Ramsey spectroscopy with a technique to correct the second-order Doppler shifts on an atom-by-atom basis. Additionally, this approach suppresses systematic effects of the AC Stark shift to a negligible level compared to the target precision. Simulations predict that for both positronium and muonium, this method could improve the measurement precision of the 1S-2S transition by more than two orders of magnitude compared to the current state of the art. This approach opens up new avenues for rigorous bound-state QED tests and searches for physics beyond the Standard Model., Comment: 15 pages, 9 figures, 2 tables prepared for the topical issue in EPJD of the PSAS 2024 conference

Gravity currents are a ubiquitous density driven flow occurring in both the natural environment and in industry. They include: seafloor turbidity currents, primary vectors of sediment, nutrient and pollutant transport; cold fronts; and hazardous gas spills. However, while the energetics are critical for their evolution and particle suspension, they are included in system scale models only crudely, so we cannot yet predict and explain the dynamics and run-out of such real-world flows. Herein, a novel depth-averaged framework is developed to capture the evolution of volume, concentration, momentum, and turbulent kinetic energy from direct integrals of the full governing equations. For the first time, we show the connection between the vertical profiles, the evolution of the depth-averaged flow, and the energetics. The viscous dissipation of mean-flow energy near the bed makes a leading order contribution, and an energetic approach to entrainment captures detrainment of fluid through particle settling. These observations allow a reconsideration of particle suspension, advancing over 50-years of research. We find that the new formulation can describe the full evolution of a shallow dilute current, with the accuracy depending primarily on closures for the profiles and source terms. Critically, this enables accurate and computationally efficient hazard risk analysis and earth surface modelling.

We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (more than 3.7M images) of multi-view RGB/monochrome image streams showing 19 subjects interacting with 33 diverse rigid objects, multi-modal signals such as eye gaze or scene point clouds, as well as comprehensive ground-truth annotations including 3D poses of objects, hands, and cameras, and 3D models of hands and objects. In addition to simple pick-up/observe/put-down actions, HOT3D contains scenarios resembling typical actions in a kitchen, office, and living room environment. The dataset is recorded by two head-mounted devices from Meta: Project Aria, a research prototype of light-weight AR/AI glasses, and Quest 3, a production VR headset sold in millions of units. Ground-truth poses were obtained by a professional motion-capture system using small optical markers attached to hands and objects. Hand annotations are provided in the UmeTrack and MANO formats and objects are represented by 3D meshes with PBR materials obtained by an in-house scanner. In our experiments, we demonstrate the effectiveness of multi-view egocentric data for three popular tasks: 3D hand tracking, 6DoF object pose estimation, and 3D lifting of unknown in-hand objects. The evaluated multi-view methods, whose benchmarking is uniquely enabled by HOT3D, significantly outperform their single-view counterparts., Comment: arXiv admin note: substantial text overlap with arXiv:2406.09598

Undoped and Cr doped \b{eta}-Ga2O3 (100) single crystals are grown by optical floating zone method. The full width at half maximum of rocking curve is found to be 106 arcsec for undoped Ga2O3 crystals whereas the 100 and 200 ppm of Cr doped Ga2O3 crystals display multiple rocking curves with large peak widths indicating the presence of structural defects. Raman measurements reveal broadening in the vibrational mode of ~ 350 cm-1 with a shoulder peak indicating the Cr3+ dopants preferentially substitute for Ga3+ at the octahedral sites. Further, the Cr doped Ga2O3 crystals display strong optical absorption bands about 420 and 597 nm in the UV-Vis spectroscopy. Moreover, the observation of sharp characteristic photoluminescence emission lines at 690 and 697 nm also confirms the Cr substitution in the doped crystals. The indentation hardness increases nearly linear from 13.0 to 17.9 GPa whilst the indentation modulus decreases from 224.9 to 202.4 GPa upon Cr doping of 200 ppm in \b{eta}-Ga2O3. The structural defects caused by the Cr doping interrupt the movement of indentation induced dislocations that results in the increase of hardness of the Cr doped \b{eta}-Ga2O3 (100) single crystals., Comment: 14 pages, 6 figures

A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious actor's intent. In this work, we ask if there is a way for anti-virus developers to similarly re-purpose existing work to improve their malware detection capability. We show that this is plausible via YARA rules, which use human-written signatures to detect specific malware families, functionalities, or other markers of interest. By extracting sub-signatures from publicly available YARA rules, we assembled a set of features that can more effectively discriminate malicious samples from benign ones. Our experiments demonstrate that these features add value beyond traditional features on the EMBER 2018 dataset. Manual analysis of the added sub-signatures shows a power-law behavior in a combination of features that are specific and unique, as well as features that occur often. A prior expectation may be that the features would be limited in being overly specific to unique malware families. This behavior is observed, and is apparently useful in practice. In addition, we also find sub-signatures that are dual-purpose (e.g., detecting virtual machine environments) or broadly generic (e.g., DLL imports)., Comment: To appear in BigData'24 CyberHunt 2024

We investigate the internal and external mechanisms that regulate and quench star formation (SF) in galaxies at $z \sim 0.3$ using MAGPI observations and the EAGLE, Magneticum, and IllustrisTNG cosmological simulations. Using SimSpin to generate mock observations of simulated galaxies, we match detection/resolution limits in star formation rates and stellar mass, along with MAGPI observational details including the average point spread function and pixel scale. While we find a good agreement in the slope of the global star-forming main sequence (SFMS) between MAGPI observations and all three simulations, the slope of the resolved SFMS does not agree within 1 $-$ 2$\sigma$. Furthermore, in radial SF trends, good agreement between observations and simulations exists only for galaxies far below the SFMS, where we capture evidence for inside-out quenching. The simulations overall agree with each other between $\sim1.5-4 \ R_{\rm e}$ but show varying central suppression within $R \sim 1.5 \ R_{\rm e}$ for galaxies on and below the SFMS, attributable to different AGN feedback prescriptions. All three simulations show similar dependencies of SF radial trends with environment. Central galaxies are subject to both internal and external mechanisms, showing increased SF suppression in the centre with increasing halo mass, indicating AGN feedback. Satellite galaxies display increasing suppression in the outskirts as halo mass increases, indicative of environmental processes. These results demonstrate the power of spatially resolved studies of galaxies; while global properties align, radial profiles reveal discrepancies between observations and simulations and their underlying physics., Comment: 20 pages, 10 figures, submitted to MNRAS

Traditional archival practices for describing electronic theses and dissertations (ETDs) rely on broad, high-level metadata schemes that fail to capture the depth, complexity, and interdisciplinary nature of these long scholarly works. The lack of detailed, chapter-level content descriptions impedes researchers' ability to locate specific sections or themes, thereby reducing discoverability and overall accessibility. By providing chapter-level metadata information, we improve the effectiveness of ETDs as research resources. This makes it easier for scholars to navigate them efficiently and extract valuable insights. The absence of such metadata further obstructs interdisciplinary research by obscuring connections across fields, hindering new academic discoveries and collaboration. In this paper, we propose a machine learning and AI-driven solution to automatically categorize ETD chapters. This solution is intended to improve discoverability and promote understanding of chapters. Our approach enriches traditional archival practices by providing context-rich descriptions that facilitate targeted navigation and improved access. We aim to support interdisciplinary research and make ETDs more accessible. By providing chapter-level classification labels and using them to index in our developed prototype system, we make content in ETD chapters more discoverable and usable for a diverse range of scholarly needs. Implementing this AI-enhanced approach allows archives to serve researchers better, enabling efficient access to relevant information and supporting deeper engagement with ETDs. This will increase the impact of ETDs as research tools, foster interdisciplinary exploration, and reinforce the role of archives in scholarly communication within the data-intensive academic landscape.

As research institutions increasingly commit to supporting the United Nations' Sustainable Development Goals (SDGs), there is a pressing need to accurately assess their research output against these goals. Current approaches, primarily reliant on keyword-based Boolean search queries, conflate incidental keyword matches with genuine contributions, reducing retrieval precision and complicating benchmarking efforts. This study investigates the application of autoregressive Large Language Models (LLMs) as evaluation agents to identify relevant scholarly contributions to SDG targets in scholarly publications. Using a dataset of academic abstracts retrieved via SDG-specific keyword queries, we demonstrate that small, locally-hosted LLMs can differentiate semantically relevant contributions to SDG targets from documents retrieved due to incidental keyword matches, addressing the limitations of traditional methods. By leveraging the contextual understanding of LLMs, this approach provides a scalable framework for improving SDG-related research metrics and informing institutional reporting.

Recent techniques for speech deepfake detection often rely on pre-trained self-supervised models. These systems, initially developed for Automatic Speech Recognition (ASR), have proved their ability to offer a meaningful representation of speech signals, which can benefit various tasks, including deepfake detection. In this context, pre-trained models serve as feature extractors and are used to extract embeddings from input speech, which are then fed to a binary speech deepfake detector. The remarkable accuracy achieved through this approach underscores a potential relationship between ASR and speech deepfake detection. However, this connection is not yet entirely clear, and we do not know whether improved performance in ASR corresponds to higher speech deepfake detection capabilities. In this paper, we address this question through a systematic analysis. We consider two different pre-trained self-supervised ASR models, Whisper and Wav2Vec 2.0, and adapt them for the speech deepfake detection task. These models have been released in multiple versions, with increasing number of parameters and enhanced ASR performance. We investigate whether performance improvements in ASR correlate with improvements in speech deepfake detection. Our results provide insights into the relationship between these two tasks and offer valuable guidance for the development of more effective speech deepfake detectors., Comment: Published at Asilomar Conference on Signals, Systems, and Computers 2024

Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpretability and ease of use have led to their adoption for predictive tasks as well, including in clinical settings. However, designing checklists can be challenging, often requiring expert knowledge and manual rule design based on available data. Recent work has attempted to address this issue by using machine learning to automatically generate predictive checklists from data, although these approaches have been limited to Boolean data. We propose a novel method for learning predictive checklists from diverse data modalities, such as images and time series. Our approach relies on probabilistic logic programming, a learning paradigm that enables matching the discrete nature of checklist with continuous-valued data. We propose a regularization technique to tradeoff between the information captured in discrete concepts of continuous data and permit a tunable level of interpretability for the learned checklist concepts. We demonstrate that our method outperforms various explainable machine learning techniques on prediction tasks involving image sequences, time series, and clinical notes., Comment: 36 pages

Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-and middle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a potentially scalable pathway to enhance healthcare access and reduce costs. However, their effectiveness in the Global South, especially across the African continent, remains to be established. In this work, we introduce AfriMed-QA, the first large scale Pan-African English multi-specialty medical Question-Answering (QA) dataset, 15,000 questions (open and closed-ended) sourced from over 60 medical schools across 16 countries, covering 32 medical specialties. We further evaluate 30 LLMs across multiple axes including correctness and demographic bias. Our findings show significant performance variation across specialties and geographies, MCQ performance clearly lags USMLE (MedQA). We find that biomedical LLMs underperform general models and smaller edge-friendly LLMs struggle to achieve a passing score. Interestingly, human evaluations show a consistent consumer preference for LLM answers and explanations when compared with clinician answers.

Research on the projective measurement of gravitons increasingly supports Dysons conclusions that the detection of single gravitons is not physically possible. It is therefore prudent to consider alternative signatures of non-classicality in gravitational wave detections to determine if gravity is quantized. Coincident multiple detector operations make it possible to consider the bipartite measurement-induced entanglement, in the detection process, as a signature of non-classicality. By developing a model of measurement-induced entanglement, based on a fixed number of gravitons for the bipartite system, we demonstrate that the entanglement entropy is on the order of a few percent of the mean number of gravitons interacting with the detectors. The bipartite measurement-induced entanglement is part of the detection process, which avoids the challenges associated with developing signatures of production-induced entanglement, due to the extremely low gravitational wave detector efficiencies. The calculation of normalized measurement-induced entanglement entropy demonstrates the potential of developing physically meaningful signatures of non-classicality based on bipartite detections of gravitational radiation. This result is in stark contrast to the discouraging calculations based on single-point detections., Comment: 12 pages and 2 figures