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The study compared the relationship of the contract plan of student evaluation and a traditional type of evaluation, for changes in knowledge and attitudes during a college course in ecological relationships. The Syracuse Environmental Awareness Test was used to measure both cognitive and affective changes. In the experimental group the contract plan of evaluation was utilized. Subjects were evaluated on the number of course objectives which were completed satisfactorily. In the control group, a traditional type of student evaluation was used. The students were evaluated on the scores they received on three examinations. Although there was no statistically significant difference between the two groups when compared, there were several factors which gave credence to the hypothesis that the traditional method was superior to the contract method. First, there was a significant increase in cognitive knowledge for the control group between the pretest and the posttest, while in the experimental group there was no significant increase. Second, the experimental group had a larger percentage of Health Science majors than did the control group. If it is valid to assume that Health Science majors would be more highly motivated to do well in a course in their major field of study; then the experimental group should have demonstrated greater increases in cognitive and affective behavior. (RC)

With an aim towards modeling cosmologies beyond the LCDM paradigm, we demonstrate the automatic construction of recombination history emulators while enforcing a prior of causal dynamics. These methods are particularly useful in the current era of precision cosmology, where extremely constraining datasets provide insights into a cosmological model dominated by unknown contents. Cosmic Microwave Background (CMB) data in particular provide a clean glimpse into the interaction of dark matter, baryons, and radiation in the early Universe, but interpretation of this data requires knowledge of the evolution of the ionization history of the Universe. The exploration of new physics with new CMB data will require fast and flexible calculation of this ionization history. We develop a differentiable machine learning model for recombination physics using a neural network ordinary differential equation model (Universal Differential Equations, UDEs), building towards automatic dimensionality reduction and the avoidance of manual tuning based on cosmological model., Comment: 12 pages, 3 figures, comments welcome

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data., Comment: 21 pages, 11 figures, 2 tables

Pulsar timing array observations have found evidence for an isotropic gravitational wave background with the Hellings-Downs angular correlations, expected from general relativity. This interpretation hinges on the measured shape of the angular correlations, which is predominately quadrupolar under general relativity. Here we explore a more flexible parameterization: we expand the angular correlations into a sum of Legendre polynomials and use a Bayesian analysis to constrain their coefficients with the 15-year pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). When including Legendre polynomials with multipoles $\ell \geq 2$, we only find a significant signal in the quadrupole with an amplitude consistent with general relativity and non-zero at the $\sim 95\%$ confidence level and a Bayes factor of 200. When we include multipoles $\ell \leq 1$, the Bayes factor evidence for quadrupole correlations decreases by more than an order of magnitude due to evidence for a monopolar signal at approximately 4 nHz which has also been noted in previous analyses of the NANOGrav 15-year data. Further work needs to be done in order to better characterize the properties of this monopolar signal and its effect on the evidence for quadrupolar angular correlations., Comment: 15 pages, 6 figures

We investigate the evolution of supernova remnants (SNRs) in a two-phase cloudy medium by performing a series of high-resolution (up to $\Delta x\approx0.01\,\mathrm{pc}$), 3D hydrodynamical simulations including radiative cooling and thermal conduction. We aim to reach a resolution that directly captures the shock-cloud interactions for the majority of the clouds initialized by the saturation of thermal instability. In comparison to the SNR in a uniform medium with the volume filling warm medium, the SNR expands similarly (following $\propto t^{2/5}$) but sweeps up more mass as the cold clouds contribute before shocks in the warm medium become radiative. However, the SNR in a cloudy medium continuously loses energy after shocks toward the cold clouds cool, resulting in less hot gas mass, thermal energy, and terminal momentum. Thermal conduction has little effect on the dynamics of the SNR but smooths the morphology and modifies the internal structure by increasing the density of hot gas by a factor of $\sim 3-5$. The simulation results are not fully consistent with many previous 1D models describing the SNR in a cloudy medium including a mass loading term. By direct measurement in the simulations, we find that, apart from the mass source, the energy sink is also important with a spatially flat cooling rate $\dot{e}\propto t^{-11/5}$. As an illustration, we show an example 1D model including both mass source and energy sink terms (in addition to the radiative cooling in the volume filling component) that better describes the structure of the simulated SNR., Comment: 24 pages, 14 figures, submitted to ApJ

Modeling disease progression is crucial for improving the quality and efficacy of clinical diagnosis and prognosis, but it is often hindered by a lack of longitudinal medical image monitoring for individual patients. To address this challenge, we propose the first Medical Video Generation (MVG) framework that enables controlled manipulation of disease-related image and video features, allowing precise, realistic, and personalized simulations of disease progression. Our approach begins by leveraging large language models (LLMs) to recaption prompt for disease trajectory. Next, a controllable multi-round diffusion model simulates the disease progression state for each patient, creating realistic intermediate disease state sequence. Finally, a diffusion-based video transition generation model interpolates disease progression between these states. We validate our framework across three medical imaging domains: chest X-ray, fundus photography, and skin image. Our results demonstrate that MVG significantly outperforms baseline models in generating coherent and clinically plausible disease trajectories. Two user studies by veteran physicians, provide further validation and insights into the clinical utility of the generated sequences. MVG has the potential to assist healthcare providers in modeling disease trajectories, interpolating missing medical image data, and enhancing medical education through realistic, dynamic visualizations of disease progression., Comment: Tech Report. The appendix will release soon. arXiv admin note: text overlap with arXiv:2309.11745

In this paper, we discuss the need for an integrated software stack that unites artificial intelligence (AI) and modeling and simulation (ModSim) tools to advance scientific discovery. The authors advocate for a unified AI/ModSim software ecosystem that ensures compatibility across a wide range of software on diverse high-performance computing systems, promoting ease of deployment, version management, and binary distribution. Key challenges highlighted include balancing the distinct needs of AI and ModSim, especially in terms of software build practices, dependency management, and compatibility. The document underscores the importance of continuous integration, community-driven stewardship, and collaboration with the Department of Energy (DOE) to develop a portable and cohesive scientific software ecosystem. Recommendations focus on supporting standardized environments through initiatives like the Extreme-scale Scientific Software Stack (E4S) and Spack to foster interdisciplinary innovation and facilitate new scientific advancements., Comment: 5 pages

Aggregating large-scale radiotherapy planning and delivery data is crucial for advancing radiation oncology research and improving clinical practice, yet challenges persist due to the diversity of treatment planning systems (TPS), record and verify (R&V) systems, and complex data formats lacking standardized retrieval methods. We developed a robust software framework that automates the collection and integration of multi-institutional radiotherapy data from diverse TPS and R&V systems. By utilizing the unidirectional references of DICOM objects, our framework reconstructs complete patient datasets starting from Radiotherapy Treatment Records (RTRECORDs), managing tasks such as data queries, transfers, verification, and logging. It effectively maps DICOM linkages between RTRECORDs, RTPLANs, RTDOSEs, RTSTRUCTs, planning images, registrations, and associated diagnostic images, incorporating custom modules for data conversion and comprehensive error handling. Implemented across multiple institutions using various systems$-$ including ARIA, Eclipse, MOSAIQ, RayStation, MIM, Pinnacle$-$ the framework successfully collected data from two clinics over an 11-year period, aggregating data from 6,022 patients and 13,871 treatment plans with a success rate of 99.76% and an average processing time of approximately 18 minutes per patient. Ongoing efforts are extending data collection to clinics lacking DICOM Query/Retrieve capabilities, demonstrating the framework's adaptability to various clinical environments. This efficient automation of comprehensive data collection overcomes significant technical barriers, facilitating the creation of large-scale datasets that can accelerate advancements in radiation oncology., Comment: 11 pages, 5 figures

Free electrons in the interstellar medium refract and diffract radio waves along multiple paths, resulting in angular and temporal broadening of radio pulses that limits pulsar timing precision. We determine multifrequency, multi-epoch scattering times for the large dispersion measure millisecond pulsar J1903+0327 by developing a three component model for the emitted pulse shape that is convolved with a best fit pulse broadening function (PBF) identified from a family of thin-screen and extended-media PBFs. We show that the scattering time, $\tau$, at a fiducial frequency of 1500 MHz changes by approximately 10% over a 5.5yr span with a characteristic timescale of approximately 100 days. We also constrain the spectral index and inner scale of the wavenumber spectrum of electron density variations along this line of sight. We find that the scaling law for $\tau$ vs. radio frequency is strongly affected by any mismatch between the true and assumed PBF or between the true and assumed intrinsic pulse shape. We show using simulations that refraction is a plausible cause of the epoch dependence of $\tau$, manifesting as changes in the PBF shape and $1/e$ time scale. Finally, we discuss the implications of our scattering results on pulsar timing including time of arrival delays and dispersion measure misestimation., Comment: 16 pages, 14 figures, submitted to ApJ

The detection of a stochastic gravitational wave background by pulsar timing arrays suggests the presence of a supermassive black hole binary population. Although the observed spectrum generally aligns with predictions from orbital evolution driven by gravitational wave emission in circular orbits, there is a discernible preference for a turnover at the lowest observed frequencies. This turnover could indicate a significant hardening phase, transitioning from early environmental influences to later stages predominantly influenced by gravitational wave emission. In the vicinity of these binaries, the ejection of stars or dark matter particles through gravitational three-body slingshots efficiently extracts orbital energy, leading to a low-frequency turnover in the spectrum. By analyzing the NANOGrav 15-year data, we assess how the gravitational wave spectrum depends on the initial inner galactic profile prior to disruption by binary ejections, accounting for a range of initial binary eccentricities. Our findings suggest a parsec-scale galactic center density around $10^6\,M_\odot/\textrm{pc}^3$ across most of the parameter space, offering insights into the environmental effects on black hole evolution and combined matter density near galaxy centers., Comment: 15 pages, 5 figures

Galaxies obey a set of strict dynamical laws, which imply a close coupling between the visible matter (stars and gas) and the observed dynamics (set by dark matter in the standard cosmological context). Here we review recent results from weak gravitational lensing, which allows studying the empirical laws of galaxy dynamics out to exceedingly large radii in both late-type galaxies (LTGs) and early-type galaxies (ETGs). We focus on three laws: (1) the circular velocity curves of both LTGs and ETGs remain indefinitely flat out to several hundreds of kpc; (2) the same baryonic Tully-Fisher relation is followed by LTGs and ETGs; (3) the same radial acceleration relation (RAR) is followed by LTGs and ETGs. Combining galaxy data with Solar System data, the RAR covers about 16 orders of magnitude in the Newtonian baryonic acceleration. Remarkably, these empirical facts were predicted a priori by MOND., Comment: Invited review for the IAU General Assembly 2024, Focus Meeting 9 (6 pages, 3 figures)

In the latest of a series of papers (arXiv:2410.21850), it has been claimed that neutrinos cannot oscillate as a consequence of their mass mixing. I clarify that this arises from the unrealistic assumption that neutrinos will always be in nearly exact eigenstates of energy, and ignoring the spatial dependence of the neutrino wave function. Taking into account the latter, neutrinos will be observed to oscillate in precisely the expected manner., Comment: 1 page, comment on arXiv:2410.21850

Time-of-arrival (TOA) measurements of pulses from pulsars are conventionally made by a template matching algorithm that compares a profile constructed by averaging a finite number of pulses to a long-term average pulse shape. However, the shapes of pulses can and do vary, leading to errors in TOA estimation. All pulsars show stochastic variations in shape, amplitude, and phase between successive pulses that only partially average out in averages of finitely many pulses. This jitter phenomenon will only become more problematic for timing precision as more sensitive telescopes are built. We describe techniques for characterizing jitter (and other shape variations) and demonstrate them with data from the Vela pulsar, PSR B0833$-$45. These include partial sum analyses; auto-and cross correlations between templates and profiles and between multifrequency arrival times; and principal component analysis. We then quantify how pulse shape changes affect TOA estimates using both analytical and simulation methods on pulse shapes of varying complexity (multiple components). These methods can provide the means for improving arrival time precision for many applications, including gravitational wave astronomy using pulsar timing arrays., Comment: 23 pages, 13 figures. Submitted to ApJ

We propose the \textit{linear barycentric coding model (LBCM)} that utilizes the linear optimal transport (LOT) metric for analysis and synthesis of probability measures. We provide a closed-form solution to the variational problem characterizing the probability measures in the LBCM and establish equivalence of the LBCM to the set of Wasserstein-2 barycenters in the special case of compatible measures. Computational methods for synthesizing and analyzing measures in the LBCM are developed with finite sample guarantees. One of our main theoretical contributions is to identify an LBCM, expressed in terms of a simple family, which is sufficient to express all probability measures on the interval $[0,1]$. We show that a natural analogous construction of an LBCM in $\mathbb{R}^2$ fails, and we leave it as an open problem to identify the proper extension in more than one dimension. We conclude by demonstrating the utility of LBCM for covariance estimation and data imputation., Comment: 37 pages, 6 figures

Astrophysical observations suggest that most of the matter in the cosmos consists of a new form that has not been observed on Earth. The nature and origin of this mysterious dark matter are among the most pressing questions in fundamental science. In this review we summarize the current state of dark matter research from two perspectives. First, we provide an overview of the leading theoretical proposals for dark matter. And second, we describe how these proposals have driven a broad and diverse global search program for dark matter involving direct laboratory searches and astrophysical observations. This review is based on a Green Paper on dark matter prepared as part of the 2020 Astroparticle Community Planning initiative undertaken by the Canadian Subatomic Physics community but has been significantly updated to reflect recent advances., Comment: 40 pages, 15 figures, invited review article accepted for publication in the Canadian Journal of Physics for a special issue on "Particle Astrophysics in Canada," figures from other works reproduced with permission

The R package trajmsm provides functions designed to simplify the estimation of the parameters of a model combining latent class growth analysis (LCGA), a trajectory analysis technique, and marginal structural models (MSMs) called LCGA-MSM. LCGA summarizes similar patterns of change over time into a few distinct categories called trajectory groups, which are then included as "treatments" in the MSM. MSMs are a class of causal models that correctly handle treatment-confounder feedback. The parameters of LCGA-MSMs can be consistently estimated using different estimators, such as inverse probability weighting (IPW), g-computation, and pooled longitudinal targeted maximum likelihood estimation (pooled LTMLE). These three estimators of the parameters of LCGA-MSMs are currently implemented in our package. In the context of a time-dependent outcome, we previously proposed a combination of LCGA and history-restricted MSMs (LCGA-HRMSMs). Our package provides additional functions to estimate the parameters of such models. Version 0.1.3 of the package is currently available on CRAN., Comment: 19 pages, 13 tables, 3 figures

Ballots are the core records of elections. Electronic records of actual ballots cast (cast vote records) are available to the public in some jurisdictions. However, they have been released in a variety of formats and have not been independently evaluated. Here we introduce a database of cast vote records from the 2020 U.S. general election. We downloaded publicly available unstandardized cast vote records, standardized them into a multi-state database, and extensively compared their totals to certified election results. Our release includes vote records for President, Governor, U.S. Senate and House, and state upper and lower chambers -- covering 42.7 million voters in 20 states who voted for more than 2,204 candidates. This database serves as a uniquely granular administrative dataset for studying voting behavior and election administration. Using this data, we show that in battleground states, 1.9 percent of solid Republicans (as defined by their congressional and state legislative voting) in our database split their ticket for Joe Biden, while 1.2 percent of solid Democrats split their ticket for Donald Trump., Comment: 26 pages and appendix

Local primordial non-Gaussianity (LPNG) couples long-wavelength cosmological fluctuations to the short-wavelength behavior of galaxies. This coupling is encoded in bias parameters including $b_{\phi}$ and $b_{\delta\phi}$ at linear and quadratic order in the large-scale biasing framework. We perform the first field-level measurement of $b_{\phi}$ and $b_{\delta\phi}$ using Lagrangian bias and non-linear displacements from N-body simulations. We compare our field level measurements with universality predictions and separate universe results, finding qualitative consistency, but disagreement in detail. We also quantify the information on $f_{\mathrm{NL}}^{(\mathrm{loc})}$ available in the field given various assumptions on knowledge of $b_{\phi}$ at fixed initial conditions. We find that it is not possible to precisely constrain $f_{\mathrm{NL}}^{(\mathrm{loc})}$ when marginalizing over $b_{\phi} f_{\mathrm{NL}}^{(\mathrm{loc})}$ even at the field level, observing a 2-3X degradation in constraints between a linear and quadratic biasing model on perturbative field-level mocks, suggesting that a $b_{\phi}$ prior is necessary to meaningfully constrain $f_{\mathrm{NL}}^{(\mathrm{loc})}$ at the field level even in this idealized scenario. For simulated dark matter halos, the pure $f_{\mathrm{NL}}^{(\mathrm{loc})}$ constraints from both linear and quadratic field-level models appear biased when marginalizing over bias parameters including $b_{\phi}$ and $b_{\delta\phi}$ due largely to the $f_{\mathrm{NL}}^{(\mathrm{loc})} - b_\phi$ degeneracy. Our results are an important consistency test of the large-scale bias framework for LPNG and highlight the importance of physically motivated priors on LPNG bias parameters for future surveys., Comment: 26 pages, 10 figures, prepared for submission to JCAP

Predicting future human behavior is an increasingly popular topic in computer vision, driven by the interest in applications such as autonomous vehicles, digital assistants and human-robot interactions. The literature on behavior prediction spans various tasks, including action anticipation, activity forecasting, intent prediction, goal prediction, and so on. Our survey aims to tie together this fragmented literature, covering recent technical innovations as well as the development of new large-scale datasets for model training and evaluation. We also summarize the widely-used metrics for different tasks and provide a comprehensive performance comparison of existing approaches on eleven action anticipation datasets. This survey serves as not only a reference for contemporary methodologies in action anticipation, but also a guideline for future research direction of this evolving landscape., Comment: 30 pages, 9 figures, 12 tables

Reduced transmon qubit $T_1$ coherence times have been linked to the amorphous oxide layers formed by thin film capacitors during processing. Because Ta or Ta capped Nb capacitors exhibit overall superior qubit performance to those fabricated with Nb capacitors, it has been hypothesized that the amorphous, non-stoichiometric Ta$_2$O$_{5-x}$ oxide is less lossy than its Nb$_2$O$_{5-x}$ counterpart. The origins of what makes amorphous Ta$_2$O$_{5-x}$ less susceptible to accepted decoherence channels is unknown. Here we establish the microscopic features of amorphous Nb$_2$O$_{5-x}$ and Ta$_2$O$_{5-x}$ using a combination of \textit{ab initio} molecular dynamics and density functional theory calculations. Our simulations establish that oxygen deficiency is less likely to occur in amorphous Ta$_2$O$_{5-x}$ than in Nb$_2$O$_{5-x}$ for $0\le x \le 0.25$ and that at a given level of oxygen deficiency the formation of metal Ta-Ta bonds is enhanced. These bonds, which are accommodated by structural flaws in the amorphous network, capture electrons better than in amorphous Nb$_2$O$_{5-x}$. These thermochemical differences quench or highly suppress magnetic moments in amorphous Ta$_2$O$_{5-x}$ and eliminate a potential source of quasiparticles and magnetic flux noise. Our calculations also show that hyperfine couplings between Nb nuclei and local magnetic moments in Nb$_2$O$_{5-x}$ could form "two-level systems" (TLS) or "two-level fluctuators" (TLF) with energy splittings of 100-1000 MHz or higher. This reveals a new TLS mechanism in amorphous Nb$_2$O$_{5-x}$ oxide layers that is unlikely in Ta$_2$O$_{5-x}$. Our work provides fundamental understanding of the materials chemistry and limitations imposed by native oxides of superconducting qubits, which can be used to guide materials selection and processing., Comment: 8 pages, 4 figures

Fragmentation in a gravitationally unstable accretion disk can be an important pathway for forming stellar/planetary companions. To characterize quantitatively the condition for and outcome of fragmentation under realistic thermodynamics, we perform global 3D simulations of gravitationally unstable disks at various cooling rates and cooling types, including the first global simulations of gravitational instability that employ full radiation transport. We find that fragmentation is a stochastic process, with the fragment generation rate per disk area $p_{\rm frag}$ showing an exponential dependence on the parameter $\beta\equiv\Omega_K t_{cool}$, where $\Omega_K$ is the Keplerian rotation frequency and $t_{cool}$ is the average cooling timescale. Compared to a prescribed constant $\beta$, radiative cooling in the optically thin/thick regime makes $p_{\rm frag}$ decrease slower/faster in $\beta$; the critical $\beta$ corresponding to $\sim 1$ fragment per orbit is $\approx$3, 5, 2 for constant $\beta$, optically thin, and optically thick cooling, respectively. The distribution function of the initial fragment mass is remarkably insensitive to disk thermodynamics. Regardless of cooling rate and optical depth, the typical initial fragment mass is $m_{frag} \approx 40 M_{tot}h^3$, with $M_{tot}$ being the total (star+disk) mass and $h=H/R$ being the disk aspect ratio. Applying this result to typical Class 0/I protostellar disks, we find $m_{frag}\sim 20 M_J$, suggesting that fragmentation more likely forms brown dwarfs. Given the finite width of the $m_{frag}$ distribution, forming massive planets is also possible., Comment: 21 pages, 11 figures, accepted for publication in ApJ

LLM-as-a-Judge is a widely used method for evaluating the performance of Large Language Models (LLMs) across various tasks. We address the challenge of quantifying the uncertainty of LLM-as-a-Judge evaluations. While uncertainty quantification has been well-studied in other domains, applying it effectively to LLMs poses unique challenges due to their complex decision-making capabilities and computational demands. In this paper, we introduce a novel method for quantifying uncertainty designed to enhance the trustworthiness of LLM-as-a-Judge evaluations. The method quantifies uncertainty by analyzing the relationships between generated assessments and possible ratings. By cross-evaluating these relationships and constructing a confusion matrix based on token probabilities, the method derives labels of high or low uncertainty. We evaluate our method across multiple benchmarks, demonstrating a strong correlation between the accuracy of LLM evaluations and the derived uncertainty scores. Our findings suggest that this method can significantly improve the reliability and consistency of LLM-as-a-Judge evaluations.

Light passing near a black hole can follow multiple paths from an emission source to an observer due to strong gravitational lensing. Photons following different paths take different amounts of time to reach the observer, which produces an echo signature in the image. The characteristic echo delay is determined primarily by the mass of the black hole, but it is also influenced by the black hole spin and inclination to the observer. In the Kerr geometry, echo images are demagnified, rotated, and sheared copies of the direct image and lie within a restricted region of the image. Echo images have exponentially suppressed flux, and temporal correlations within the flow make it challenging to directly detect light echoes from the total light curve. In this paper, we propose a novel method to search for light echoes by correlating the total light curve with the interferometric signal at high spatial frequencies, which is a proxy for indirect emission. We explore the viability of our method using numerical general relativistic magnetohydrodynamic simulations of a near-face-on accretion system scaled to M87-like parameters. We demonstrate that our method can be used to directly infer the echo delay period in simulated data. An echo detection would be clear evidence that we have captured photons that have circled the black hole, and a high-fidelity echo measurement would provide an independent measure of fundamental black hole parameters. Our results suggest that detecting echoes may be achievable through interferometric observations with a modest space-based very long baseline interferometry mission., Comment: 21 pages, 15 figures, accepted for publication in ApJL

It was recently suggested (arxiv:2410.06604) that the small value of the dark energy of the universe could be explained in terms of the scale of neutrino masses through a simple quantum field theoretic mechanism. I clarify that the quantity computed is only a threshold correction to the vacuum energy at the low $m_\nu$ scale, and therefore cannot explain the magnitude of the vacuum energy., Comment: 1 paragraph; comment on arxiv:2410.06604; v2: added references

Dust from core-collapse supernovae (CCSNe), specifically Type IIP SNe, has been suggested to be a significant source of the dust observed in high-redshift galaxies. CCSNe eject large amounts of newly formed heavy elements, which can condense into dust grains in the cooling ejecta. However, infrared (IR) observations of typical CCSNe generally measure dust masses that are too small to account for the dust production needed at high redshifts. Type IIn SNe, classified by their dense circumstellar medium (CSM), are also known to exhibit strong IR emission from warm dust, but the dust origin and heating mechanism have generally remained unconstrained because of limited observational capabilities in the mid-IR. Here, we present a JWST/MIRI Medium Resolution Spectrograph (MRS) spectrum of the Type IIn SN 2005ip nearly 17 years post-explosion. The Type IIn SN 2005ip is one of the longest-lasting and most well-studied SNe observed to date. Combined with a Spitzer mid-IR spectrum of SN 2005ip obtained in 2008, this data set provides a rare 15-year baseline, allowing for a unique investigation of the evolution of dust. The JWST spectrum shows a new high-mass dust component ($\gtrsim0.08$ M$_{\odot}$) that is not present in the earlier Spitzer spectrum. Our analysis shows dust likely formed over the past 15 years in the cold, dense shell (CDS), between the forward and reverse shocks. There is also a smaller mass of carbonaceous dust ($\gtrsim0.005$ M$_{\odot}$) in the ejecta. These observations provide new insights into the role of SN dust production, particularly within the CDS, and its potential contribution to the rapid dust enrichment of the early Universe.

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

It was recently claimed (arxiv:2407.00954) that neutrino oscillations through conventional mass mixing are forbidden by energy conservation, and that they in fact arise from virtual $Z$ boson exchange. I explain why both claims, and therefore a repetition of these claims in arxiv:2410.03133, are incorrect., Comment: 2 pages, comment on arxiv:2407.00954 and arxiv:2410.03133

Evaluation of large language model (LLM) outputs requires users to make critical judgments about the best outputs across various configurations. This process is costly and takes time given the large amounts of data. LLMs are increasingly used as evaluators to filter training data, evaluate model performance or assist human evaluators with detailed assessments. To support this process, effective front-end tools are critical for evaluation. Two common approaches for using LLMs as evaluators are direct assessment and pairwise comparison. In our study with machine learning practitioners (n=15), each completing 6 tasks yielding 131 evaluations, we explore how task-related factors and assessment strategies influence criteria refinement and user perceptions. Findings show that users performed more evaluations with direct assessment by making criteria task-specific, modifying judgments, and changing the evaluator model. We conclude with recommendations for how systems can better support interactions in LLM-assisted evaluations.

We describe AthenaK: a new implementation of the Athena++ block-based adaptive mesh refinement (AMR) framework using the Kokkos programming model. Finite volume methods for Newtonian, special relativistic (SR), and general relativistic (GR) hydrodynamics and magnetohydrodynamics (MHD), and GR-radiation hydrodynamics and MHD, as well as a module for evolving Lagrangian tracer or charged test particles (e.g., cosmic rays) are implemented using the framework. In two companion papers we describe (1) a new solver for the Einstein equations based on the Z4c formalism and (2) a GRMHD solver in dynamical spacetimes also implemented using the framework, enabling new applications in numerical relativity. By adopting Kokkos, the code can be run on virtually any hardware, including CPUs, GPUs from multiple vendors, and emerging ARM processors. AthenaK shows excellent performance and weak scaling, achieving over one billion cell updates per second for hydrodynamics in three-dimensions on a single NVIDIA Grace Hopper processor and with a typical parallel efficiency of 80% on 65536 AMD GPUs on the OLCF Frontier system. Such performance portability enables AthenaK to leverage modern exascale computing systems for challenging applications in astrophysical fluid dynamics, numerical relativity, and multimessenger astrophysics., Comment: Submitted to ApJS

Antiferromagnets that display very small stray magnetic field are ideal for spintronic applications. Of particular interest are non-collinear, chiral antiferromagnets of the type Mn3X (X=Sn, Ge), which display a large magnetotransport response that is correlated with their antiferromagnetic ordering. The ability to read out and manipulate this ordering is crucial for their integration into spintronic devices. These materials exhibit a tiny unbalanced magnetic moment such that a large external magnetic field can, in principle, be used to set the material into a single antiferromagnetic domain. However, in thin films of Mn3Sn, we find that such fields induce only a partial magnetic ordering. By detecting two orthogonal in-plane components of the magnetic order vector, we find that the non-switchable fraction has a unidirectional anisotropy. This also enables us to visualize switching along multiple easy axes in Mn3Sn. Studying the switching at the nanoscale allows us to correlate the pining behavior to crystal grain boundaries in the Mn3Sn nanowire structures.

We report the fabrication and characterisation of a multi-core anti-resonant hollow core fibre with low inter-core coupling. The optical losses were 0.03 and 0.08 dB/m at 620 and 1000 nm respectively, while the novel structure provides new insights into hollow core fibre design and fabrication.

It was recently claimed (arXiv:2405.05410 [gr-qc]) that evidence for a stochastic gravitational wave background observed by pulsar timing arrays can be attributed instead to random perturbations of the Sun's motion by transiting asteroids. I show that that this would lead to a large dipole component accompanying a much smaller quadrupolar perturbation of pulsar timing signals, which would not be confused with a gravitational wave signal. Such an anomalous dipole would have been detected and identified as a spurious background by the PTA collaborations, if it existed., Comment: 3 pages, 1 figure. Comment on arXiv:2405.05410 [gr-qc], accepted for publication in RNAAS

Turbulence is a prevalent phenomenon in space and astrophysical plasmas, often characterized by stochastic fluctuations. While laboratory experiments and numerical simulations have revealed chaotic behavior, in-situ observations of turbulent plasmas in natural environments have predominantly shown highly stochastic signatures. Here, we present unprecedented in-situ evidence of chaotic fluctuations in the turbulent solar wind plasma downstream of the Earth's bow shock. By analyzing the relative location of magnetic-field fluctuations on the permutation entropy-complexity plane (C-H plane), we demonstrate that turbulence in the magnetosheath plasma exhibits characteristics of chaotic fluctuations rather than stochastic behavior, diverging from the expected traits of well-developed turbulence. This finding challenges established notions of plasma turbulence and reveals the need for caution when using the magnetosheath as a laboratory for studying plasma turbulence., Comment: Accepted for publication in Physics of Plasmas

A new method is presented to generate atomic structures that reproduce the essential characteristics of arbitrary material systems, phases, or ensembles. Previous methods allow one to reproduce the essential characteristics (e.g. chemical disorder) of a large random alloy within a small crystal structure. The ability to generate small representations of random alloys, with the restriction to crystal systems, results from using the fixed-lattice cluster correlations to describe structural characteristics. A more general description of the structural characteristics of atomic systems is obtained using complete sets of atomic environment descriptors. These are used within for generating representative atomic structures without restriction to fixed lattices. A general data-driven approach is provided utilizing the atomic cluster expansion(ACE) basis. The N-body ACE descriptors are a complete set of atomic environment descriptors that span both chemical and spatial degrees of freedom and are used within for describing atomic structures. The generalized representative structure(GRS) method presented within generates small atomic structures that reproduce ACE descriptor distributions corresponding to arbitrary structural and chemical complexity. It is shown that systematically improvable representations of crystalline systems on fixed parent lattices, amorphous materials, liquids, and ensembles of atomic structures may be produced efficiently through optimization algorithms. We highlight reduced representations of atomistic machine-learning training datasets that contain similar amounts of information and small 40-72 atom representations of liquid phases. The ability to use GRS methodology as a driver for informed novel structure generation is also demonstrated. The advantages over other data-driven methods and state-of-the-art methods restricted to high-symmetry systems are highlighted.

Generic constraints on dark photons are generally presented assuming they have Stueckelberg masses. These constraints are strengthened if instead the mass is due to the Higgs mechanism and the dark Higgs is light. First, we show that under reasonable assumptions on the origin of kinetic mixing $\epsilon$ and perturbativity, the strengthened constraints on $\epsilon$ cannot be arbitrarily relaxed by making the Higgs heavy. Second, we demonstrate a simple mechanism for generating dark photon dark matter after inflation, where fluctuations of a dark Higgs by stochastic misalignment can produce stable dark photons through $h\to A'A'$ decay. Third, we point out new lower bounds on $\epsilon$ in the case where the dark photon mediates thermal freeze-out of light dark matter by $s$-channel exchange, taking account of generic expectations for the size of $\epsilon$, and astrophysical upper bounds on the self-interaction cross section., Comment: Added bound on dark photons from cosmic string formation, clarifications and citations

It was recently claimed (arXiv:2409.09081v1 [astro-ph.HE]) that accretion of ordinary matter on black holes of mass $(6\times 10^{14}-4\times10^{19})\,$g would be inhibited by quantum mechanical effects, namely the de Broglie wavelength of the electron being larger than the Schwarzschild radius. However the conclusion is based on considering accretion of a single atom over the age of the Universe. There is no suppression of the accretion rate per atom on such black holes., Comment: 1 page. Comment on arXiv:2409.09081

We introduce an extension to the AthenaK code for general-relativistic magnetohydrodynamics (GRMHD) in dynamical spacetimes using a 3+1 conservative Eulerian formulation. Like the fixed-spacetime GRMHD solver, we use standard finite-volume methods to evolve the fluid and a constrained transport scheme to preserve the divergence-free constraint for the magnetic field. We also utilize a first-order flux correction (FOFC) scheme to reduce the need for an artificial atmosphere and optionally enforce a maximum principle to improve robustness. We demonstrate the accuracy of AthenaK using a set of standard tests in flat and curved spacetimes. Using a SANE accretion disk around a Kerr black hole, we compare the new solver to the existing solver for stationary spacetimes using the so-called "HARM-like" formulation. We find that both formulations converge to similar results. We also include the first published binary neutron star (BNS) mergers performed on graphical processing units (GPUs). Thanks to the FOFC scheme, our BNS mergers maintain a relative error of $\mathcal{O}(10^{-11})$ or better in baryon mass conservation up to collapse. Finally, we perform scaling tests of AthenaK on OLCF Frontier, where we show excellent weak scaling of $\geq 80\%$ efficiency up to 32768 GPUs and $74\%$ up to 65536 GPUs for a GRMHD problem in dynamical spacetimes with six levels of mesh refinement. AthenaK achieves an order-of-magnitude speedup using GPUs compared to CPUs, demonstrating that it is suitable for performing numerical relativity problems on modern exascale resources., Comment: 26 pages, 25 figures; to be submitted to ApJS soon

In this paper, we investigate the impact of hallucinations and cognitive forcing functions in human-AI collaborative text generation tasks, focusing on the use of Large Language Models (LLMs) to assist in generating high-quality conversational data. LLMs require data for fine-tuning, a crucial step in enhancing their performance. In the context of conversational customer support, the data takes the form of a conversation between a human customer and an agent and can be generated with an AI assistant. In our inquiry, involving 11 users who each completed 8 tasks, resulting in a total of 88 tasks, we found that the presence of hallucinations negatively impacts the quality of data. We also find that, although the cognitive forcing function does not always mitigate the detrimental effects of hallucinations on data quality, the presence of cognitive forcing functions and hallucinations together impacts data quality and influences how users leverage the AI responses presented to them. Our analysis of user behavior reveals distinct patterns of reliance on AI-generated responses, highlighting the importance of managing hallucinations in AI-generated content within conversational AI contexts.

Point tracking is a fundamental problem in computer vision with numerous applications in AR and robotics. A common failure mode in long-term point tracking occurs when the predicted point leaves the object it belongs to and lands on the background or another object. We identify this as the failure to correctly capture objectness properties in learning to track. To address this limitation of prior work, we propose a novel objectness regularization approach that guides points to be aware of object priors by forcing them to stay inside the the boundaries of object instances. By capturing objectness cues at training time, we avoid the need to compute object masks during testing. In addition, we leverage contextual attention to enhance the feature representation for capturing objectness at the feature level more effectively. As a result, our approach achieves state-of-the-art performance on three point tracking benchmarks, and we further validate the effectiveness of our components via ablation studies. The source code is available at: https://github.com/RehgLab/tracking_objectness, Comment: ECCV 2024 ILR Workshop

Social interactions form the foundation of human societies. Artificial intelligence has made significant progress in certain areas, but enabling machines to seamlessly understand social interactions remains an open challenge. It is important to address this gap by endowing machines with social capabilities. We identify three key capabilities needed for effective social understanding: 1) understanding multimodal social cues, 2) understanding multi-party dynamics, and 3) understanding beliefs. Building upon these foundations, we classify and review existing machine learning works on social understanding from the perspectives of verbal, non-verbal, and multimodal social cues. The verbal branch focuses on understanding linguistic signals such as speaker intent, dialogue sentiment, and commonsense reasoning. The non-verbal branch addresses techniques for perceiving social meaning from visual behaviors such as body gestures, gaze patterns, and facial expressions. The multimodal branch covers approaches that integrate verbal and non-verbal multimodal cues to holistically interpret social interactions such as recognizing emotions, conversational dynamics, and social situations. By reviewing the scope and limitations of current approaches and benchmarks, we aim to clarify the development trajectory and illuminate the path towards more comprehensive intelligence for social understanding. We hope this survey will spur further research interest and insights into this area.

Recently, nanofluidics experiments have been used to characterize the behavior of single DNA molecules confined to narrow slits etched with arrays of nanopits. Analysis of the experimental data relies on analytical estimates of the underlying free-energy landscape. In this study we use computer simulations to explicitly calculate the free energy and test the approximations employed in such analytical models. Specifically, Monte Carlo simulations were used to study a polymer confined to complex geometry consisting of a nanoslit with two square nanopits embedded in one of the surfaces. The two-dimensional Weighted Histogram Analysis Method (WHAM2D) is used to calculate the free energy, $F$, as a function of the sum ($\lambda_1$) and the difference ($\lambda_2$) of the length of the polymer contour contained in the two nanopits. We find the variation of the free-energy function with respect to confinement dimensions to be comparable to the analytical predictions that employ a simplistic theoretical model. However, there are some noteworthy quantitative discrepancies, particularly between the predicted and observed variation of $F$ with respect to $\lambda_1$. Our study provides a useful lesson on the limitations of using simplistic analytical expressions for polymer free-energy landscapes to interpret results for experiments of DNA confined to a complex geometry and points to the value of carrying out accurate numerical calculations of the free energy instead., Comment: 14 pages, 9 figures

The ability of a brain or a neural network to efficiently learn depends crucially on both the task structure and the learning rule. Previous works have analyzed the dynamical equations describing learning in the relatively simplified context of the perceptron under assumptions of a student-teacher framework or a linearized output. While these assumptions have facilitated theoretical understanding, they have precluded a detailed understanding of the roles of the nonlinearity and input-data distribution in determining the learning dynamics, limiting the applicability of the theories to real biological or artificial neural networks. Here, we use a stochastic-process approach to derive flow equations describing learning, applying this framework to the case of a nonlinear perceptron performing binary classification. We characterize the effects of the learning rule (supervised or reinforcement learning, SL/RL) and input-data distribution on the perceptron's learning curve and the forgetting curve as subsequent tasks are learned. In particular, we find that the input-data noise differently affects the learning speed under SL vs. RL, as well as determines how quickly learning of a task is overwritten by subsequent learning. Additionally, we verify our approach with real data using the MNIST dataset. This approach points a way toward analyzing learning dynamics for more-complex circuit architectures., Comment: NeurIPS 2024 camera ready version

It was recently claimed (arXiv:2408.10799) that solar system ephemeris data can exclude primordial black hole (PBH) dark matter in the mass range $10^{18}$-$10^{22}\,$g. We show that this conclusion is based on an implausible, implicit assumption; namely the uncertainty on the solar system mass within 50 au is as small as the uncertainty on the mass of the sun. Correcting for this error, we find that ephemeris data can only constrain PBH's with mass below $10^{16}\,$g, which is already excluded by constraints on their evaporation via Hawking radiation. Correcting a further error concerning the time-averaged rate of such fluctuations nullifies even this weaker constraint., Comment: 1 page. Comment on arXiv:2408.10799. Accepted for publication in RNAAS

Increasingly, experimentalists and modellers alike have come to recognise the important role of spatial structure in infection dynamics. Almost invariably, spatial computational models of viral infections -- as with in vitro experimental systems -- represent the tissue as wide and flat, which is assumed to be representative of entire affected tissue within the host. However, this assumption fails to take into account the distinctive geometry of the lung in the context of respiratory viral infections. The lung is characterised by a tubular, branching structure, and moreover is spatially heterogeneous: deeper regions of the lung are composed of far narrower airways and are associated with more severe infection. Here, we extend a typical multicellular model of viral dynamics to account for two essential features of the geometry of the lung: the tubular structure of airways, and the branching process between airway generations. We show that, with this more realistic tissue geometry, the dynamics of infection are substantially changed compared to standard computational and experimental approaches, and that the resulting model is equipped to tackle important biological phenomena that do not arise in a flat host tissue, including viral lineage dynamics in the lung, and heterogeneity in immune responses to infection in different regions of the respiratory tree. Our findings suggest aspects of viral dynamics which current in vitro systems may be insufficient to describe, and points to several features of respiratory infections which can be experimentally assessed.

Graph neural networks (GNNs) have excelled in predictive modeling for both crystals and molecules, owing to the expressiveness of graph representations. High-entropy alloys (HEAs), however, lack chemical long-range order, limiting the applicability of current graph representations. To overcome this challenge, we propose a representation of HEAs as a collection of local environment (LE) graphs. Based on this representation, we introduce the LESets machine learning model, an accurate, interpretable GNN for HEA property prediction. We demonstrate the accuracy of LESets in modeling the mechanical properties of quaternary HEAs. Through analyses and interpretation, we further extract insights into the modeling and design of HEAs. In a broader sense, LESets extends the potential applicability of GNNs to disordered materials with combinatorial complexity formed by diverse constituents and their flexible configurations.

This paper addresses the key challenge of estimating the asymptotic covariance associated with the Markov chain central limit theorem, which is essential for visualizing and terminating Markov Chain Monte Carlo (MCMC) simulations. We focus on summarizing batching, spectral, and initial sequence covariance estimation techniques. We emphasize practical recommendations for modern MCMC simulations, where positive correlation is common and leads to negatively biased covariance estimates. Our discussion is centered on computationally efficient methods that remain viable even when the number of iterations is large, offering insights into improving the reliability and accuracy of MCMC output in such scenarios., Comment: 31 pages, 10 figures

It has been proposed that the accelerated expansion of the universe can be explained by the merging of our universe with baby universes, resulting in dark energy with a phantom-like equation of state. However, the evidence in favor of it did not include the full set of cosmological observables. Here we examine the implications of this model for both early and late universe cosmology using data from Planck collaboration, DESI 2024 and other experiments. We find that the pure baby universe model gives a poor fit to current data. Extending it to include a contribution from the cosmological constant, we find two allowed regions of parameter space: one close to $\Lambda$CDM, and another with $\Lambda <0$ plus the exotic dark energy component. The two regions can be significantly favored over $\Lambda$CDM, depending on the choice of supernova datasets, and they can ameliorate the Hubble tension to the level of $2\sigma$, depending on the supernova dataset. The model with $\Lambda<0$ features an equation of state $w(a)$ with a pole singularity at early times., Comment: 7 pages, 7 figures

In the late stages of accretion leading up to the formation of planetesimals, particles grew to pebbles the size of 1-mm to tens of cm. That is the same size range that dominates the present-day comet mass loss. Meteoroids that size cause visible meteors on Earth. Here, we hypothesize that the size distribution and the physical and chemical properties of young meteoroid streams still contain information about the conditions in the solar nebula during these late stages of accretion. From observations of 47 young meteor showers, we find that freshly ejected meteoroids from long-period comets tend to have low bulk density and are distributed with equal surface area per log-mass interval (magnitude distribution index chi ~ 1.85), suggesting gentle accretion conditions. Jupiter-family comets, on the other hand, mostly produce meteoroids twice as dense and distributed with a steeper chi ~ 2.15 or even chi ~ 2.5, which implies that those pebbles grew from particles fragmenting in a collisional cascade or by catastrophic collisions, respectively. Both comet populations contain an admixture of compact materials that are sometimes sodium-poor, but Jupiter-family comets show a higher percentage (~8% on average) than long-period comet showers (~4%), and a wider range. While there are exceptions in both groups, the implication is that most long-period comets formed under gentle particle growth conditions, possibly near the 30 AU edge of the Trans Neptunian Disk, while most Jupiter family comets formed closer to the Sun where pebbles reached or passed the fragmentation barrier. This is possible if the Scattered Disk represents all objects scattered by Neptune during its migration, while the present-day outer Oort cloud formed only during and after the Sun had moved away from sibling stars., Comment: 82 pages, 14 figures, 3 tables