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The widespread availability of maps has enabled the development of numerous location-based applications, including navigation, ride-sharing, fitness tracking, gaming, robotics, and augmented reality. Today, the maps that power these services are predominantly controlled by a few large corporations and mostly cover outdoor spaces. As the use of these applications expands and indoor localization technologies advance, we are seeing the need for a scalable, federated location management system that can extend into private spaces. We introduce OpenFLAME (Open Federated Localization and Mapping Engine), the first federated and decentralized localization service. OpenFLAME links servers that handle localization for specific regions, providing applications with a seamless global view. Creating a federated localization system poses challenges, such as discovering the appropriate servers for a region and integrating services managed by independent providers. To address these issues and ensure scalability, we leverage Domain Name System (DNS) for service discovery and implement map abstractions to retrieve and merge locations across different maps. Our trace-driven study demonstrates that federated localization across remote servers is feasible with acceptable query latencies. To highlight the potential of the system, we developed an augmented reality navigation application for a large indoor space, showing that OpenFLAME can successfully power location-based applications.

We present a search for long-lived particles (LLPs) produced from kaon decay that decay to two muons inside the ICARUS neutrino detector. This channel would be a signal of hidden sector models that can address outstanding issues in particle physics such as the strong CP problem and the microphysical origin of dark matter. The search is performed with data collected in the Neutrinos at the Main Injector (NuMI) beam at Fermilab corresponding to $2.41\times 10^{20}$ protons-on-target. No new physics signal is observed, and we set world-leading limits on heavy QCD axions, as well as for the Higgs portal scalar among dedicated searches. Limits are also presented in a model-independent way applicable to any new physics model predicting the process $K\to \pi+S(\to\mu\mu)$, for a long-lived particle S. This result is the first search for new physics performed with the ICARUS detector at Fermilab. It paves the way for the future program of long-lived particle searches at ICARUS.

Spherical proportional counters (SPCs) are gaseous particle detectors sensitive to single ionization electrons in their target media, with large detector volumes and low background rates. The $\mbox{NEWS-G}$ collaboration employs this technology to search for low-mass dark matter, having previously performed searches with detectors at the Laboratoire Souterrain de Modane (LSM), including a recent campaign with a 135 cm diameter SPC filled with methane. While in situ calibrations of the detector response were carried out at the LSM, measurements of the mean ionization yield and fluctuations of methane gas in SPCs were performed using a 30 cm diameter detector. The results of multiple measurements taken at different operating voltages are presented. A UV laser system was used to measure the mean gas gain of the SPC, along with $\mathrm{^{37}Ar}$ and aluminum-fluorescence calibration sources. These measurements will inform the energy response model of future operating detectors., Comment: 11 pages, 8 figures

In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease understanding, treatment development, and outcome prediction. The proposed approach, sparse Group Factor Analysis (GFA) with regularised horseshoe priors, was implemented with probabilistic programming and can uncover associations (or latent factors) among multiple data modalities differentially expressed in sample subgroups. Synthetic data experiments showed the robustness of our sparse GFA by correctly inferring latent factors and model parameters. When applied to the Genetic Frontotemporal Dementia Initiative (GENFI) dataset, which comprises patients with frontotemporal dementia (FTD) with genetically defined subgroups, the sparse GFA identified latent disease factors differentially expressed across the subgroups, distinguishing between "subgroup-specific" latent factors within homogeneous groups and "subgroup common" latent factors shared across subgroups. The latent disease factors captured associations between brain structure and non-imaging variables (i.e., questionnaires assessing behaviour and disease severity) across the different genetic subgroups, offering insights into disease profiles. Importantly, two latent factors were more pronounced in the two more homogeneous FTD patient subgroups (progranulin (GRN) and microtubule-associated protein tau (MAPT) mutation), showcasing the method's ability to reveal subgroup-specific characteristics. These findings underscore the potential of sparse GFA for integrating multiple data modalities and identifying interpretable latent disease factors that can improve the characterization and stratification of patients with neurological and mental health disorders., Comment: 38 pages, 14 figures

The National Technical Assistance Center on Transition: the Collaborative (NTACT:C) Collaborative Assessment Guide for Transition Planning (CAG) is intended to help students, family members, educators, vocational rehabilitation counselors, human services and health agency staff, and other partners develop a coordinated assessment approach for transition planning and service delivery. The set of resources which make up the CAG includes sections designed for students and families. It also includes a supplement to highlight the assessment requirements of the Individuals with Disabilities Education Act (IDEA, 2004) and the Rehabilitation Act of 1973 (Rehab Act), as amended by title IV of the Workforce Innovation and Opportunity Act (WIOA) as well as a supplement of Definitions referenced throughout. Finally, it includes a supplement of sample assessment tools. The assessment process for identifying post-school goals and relevant transition services for students and youth consists of six separate but interconnected processes: (1) Determining What to Assess; (2) Determining Stakeholders; (3) Selecting Appropriate Assessments; (4) Conducting Assessments; (5) Reviewing Assessment Results; and (6) Using Assessment Data.

The first generation of ELT instruments includes an optical-infrared high-resolution spectrograph, indicated as ELT-HIRES and recently christened ANDES (ArmazoNes high Dispersion Echelle Spectrograph). ANDES consists of three fibre-fed spectrographs ([U]BV, RIZ, YJH) providing a spectral resolution of $\sim$100,000 with a minimum simultaneous wavelength coverage of 0.4-1.8 $\mu$m with the goal of extending it to 0.35-2.4 $\mu$m with the addition of a U arm to the BV spectrograph and a separate K band spectrograph. It operates both in seeing- and diffraction-limited conditions and the fibre feeding allows several, interchangeable observing modes including a single conjugated adaptive optics module and a small diffraction-limited integral field unit in the NIR. Modularity and fibre-feeding allow ANDES to be placed partly on the ELT Nasmyth platform and partly in the Coud\'e room. ANDES has a wide range of groundbreaking science cases spanning nearly all areas of research in astrophysics and even fundamental physics. Among the top science cases, there are the detection of biosignatures from exoplanet atmospheres, finding the fingerprints of the first generation of stars, tests on the stability of Nature's fundamental couplings, and the direct detection of the cosmic acceleration. The ANDES project is carried forward by a large international consortium, composed of 35 Institutes from 13 countries, forming a team of almost 300 scientists and engineers which include the majority of the scientific and technical expertise in the field that can be found in ESO member states., Comment: SPIE astronomical telescope and instrumentation 2024, in press

High-eccentricity gas giant planets serve as unique laboratories for studying the thermal and chemical properties of H/He-dominated atmospheres. One of the most extreme cases is HD 80606b -- a hot Jupiter orbiting a sun-like star with an eccentricity of $0.93$ -- which experiences an increase in incident flux of nearly three orders of magnitude as the star-planet separation decreases from $0.88\,{\rm au}$ at apoastron to $0.03\,{\rm au}$ at periastron. We observed the planet's periastron passage using \emph{JWST}'s NIRSpec/G395H instrument ($2.8-5.2\,{\rm \mu m}$) during a $21\,{\rm hr}$ window centered on the eclipse. We find that, as the planet passes through periastron, its emission spectrum transitions from a featureless blackbody to one in which CO and CH$_4$ absorption features are visible. We obtain significant detections of CH$_4$ during post-periapse phases at $3.7-4.8\sigma$ depending on the phase. Following periapse, CO and H$_2$O are also detected at $3.4\sigma$ and $3.1\sigma$, respectively. Furthermore, we rule out the presence of a strong temperature inversion near the IR photosphere -- predicted by GCMs to form temporarily during periapse passage -- based on the lack of obvious emission features throughout the observing window. Our study demonstrates the feasibility of studying hot Jupiter atmospheres using partial phase curves obtained with NIRSpec/G395H., Comment: 25 pages, 19 figures, 2 tables. Submitted to ApJ

The NEWS-G direct detection experiment uses spherical proportional counters to search for light dark matter candidates. New results from a 10 day physics run with a $135\,\mathrm{cm}$ in diameter spherical proportional counter at the Laboratoire Souterrain de Modane are reported. The target consists of $114\,\mathrm{g}$ of methane, providing sensitivity to dark matter spin-dependent coupling to protons. New constraints are presented in the mass range $0.17$ to $1.2\,\mathrm{GeV/c^2}$, with a 90% confidence level cross-section upper limit of $30.9\,\mathrm{pb}$ for a mass of $0.76\,\mathrm{GeV/c^2}$., Comment: 7 pages, 3 figures

Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of the posterior over data, $\mathbf{x}\sim p^{\rm post}(\mathbf{x})\propto p(\mathbf{x})r(\mathbf{x})$, in a model that consists of a diffusion generative model prior $p(\mathbf{x})$ and a black-box constraint or likelihood function $r(\mathbf{x})$. We state and prove the asymptotic correctness of a data-free learning objective, relative trajectory balance, for training a diffusion model that samples from this posterior, a problem that existing methods solve only approximately or in restricted cases. Relative trajectory balance arises from the generative flow network perspective on diffusion models, which allows the use of deep reinforcement learning techniques to improve mode coverage. Experiments illustrate the broad potential of unbiased inference of arbitrary posteriors under diffusion priors: in vision (classifier guidance), language (infilling under a discrete diffusion LLM), and multimodal data (text-to-image generation). Beyond generative modeling, we apply relative trajectory balance to the problem of continuous control with a score-based behavior prior, achieving state-of-the-art results on benchmarks in offline reinforcement learning., Comment: Code: https://github.com/GFNOrg/diffusion-finetuning

In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can take a considerable amount of scan time. Under-sampling k-space reduces the acquisition time, but results in aliased, or "folded," images. GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) is a parallel imaging technique that yields full images from subsampled arrays of k-space. GRAPPA uses localized interpolation weights, which are estimated per-scan and fixed over time, to fill in the missing spatial frequencies of the subsampled k-space. Hence, we propose a Bayesian approach to GRAPPA (BGRAPPA) where space measurement uncertainty are assessed from the a priori calibration k-space arrays. The prior information is utilized to estimate the missing spatial frequency values from the posterior distribution and reconstruct into full field-of-view images. Our BGRAPPA technique successfully reconstructed both a simulated and experimental single slice image with less artifacts, reduced noise leading to an increased signal-to-noise ratio (SNR), and stronger power of task detection., Comment: 19 pages, 15 figures (23 pages and 19 figures including the supplementary material)

M dwarf stars are the most promising hosts for detection and characterization of small and potentially habitable planets, and provide leverage relative to solar-type stars to test models of planet formation and evolution. Using \emph{Gaia} astrometry, adaptive optics imaging, and calibrated gyrochronologic relations to estimate stellar properties and filter binaries we refined the radii of 117 Kepler Objects of Interest (confirmed or candidate planets) transiting 74 single late K- and early M-type stars, and assigned stellar rotation-based ages to 113 of these. We constructed the radius distribution of 115 small ($<4R_{\oplus}$) planets and assessed its evolution. As for solar-type stars, the inferred distribution contains distinct populations of "super-Earths" (at $\approx$1.3$_{\oplus}$) and "sub-Neptunes" (at $\approx$2.2$R_{\oplus}$) separated by a gap or "valley" at $\approx$1.7$_{\oplus}$ that has a period dependence that is significantly weaker (power law index of -0.03$^{+0.01}_{-0.03}$) than for solar-type stars. Sub-Neptunes are largely absent at short periods ($<$2 days) and high irradiance, a feature analogous to the "Neptune desert" observed around solar-type stars. The relative number of sub-Neptunes to super-Earths declines between the younger and older halves of the sample (median age 3.86 Gyr), although the formal significance is low ($p = 0.08$) because of the small sample size. The decline in sub-Neptunes appears to be more pronounced on wider orbits and low stellar irradiance. This is not due to detection bias and suggests a role for H$_2$O as steam in inflating the radii of sub-Neptunes and/or regulating the escape of H/He from them., Comment: Accepted to MNRAS on 2024 September 12. Received 2024 September 7; in original form 2023 August 14. Table 1 available in machine readable form as ancillary file

Based on explicitly gauge invariant interpolating operators we compute complete next-leading order QED-corrections for leptonic decays. These are sizeable since the helicity-suppression in V-A interactions allows for structure-dependent collinear logs. We have explicitly checked that these logs are absent for helicity-unsuppressed Yukawa-type transitions. Based on $B \to \gamma$ form factors we present the rates for $B^- \to (\mu^-,\tau^-) \bar \nu (\gamma)$ in differential and integrated form as a function of the photon energy cut-off $E_\gamma^{\text{cut}}$. The effect of the virtual structure-dependent corrections are approximately $+5\%$ and $+3\%$ for the $\mu$- and $\tau$-channel respectively. The structure dependence of the real radiation exceeds that of the virtual one for $E_\gamma^{\text{cut}}|_{\mu} > 0.18(3)$ GeV and is subdominant for the tau channel even when fully inclusive., Comment: 31 pp, 12 figs, ancillary Mathematica notebook explaining how to combine real and virtual contributions

Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However, agents replayed from offline data are not reactive and hard to intuitively control. Existing approaches address these challenges by proposing methods that rely on heuristics or generative models of real-world data but these approaches either lack realism or necessitate costly iterative sampling procedures to control the generated behaviours. In this work, we take an alternative approach and propose CtRL-Sim, a method that leverages return-conditioned offline reinforcement learning (RL) to efficiently generate reactive and controllable traffic agents. Specifically, we process real-world driving data through a physics-enhanced Nocturne simulator to generate a diverse offline RL dataset, annotated with various rewards. With this dataset, we train a return-conditioned multi-agent behaviour model that allows for fine-grained manipulation of agent behaviours by modifying the desired returns for the various reward components. This capability enables the generation of a wide range of driving behaviours beyond the scope of the initial dataset, including adversarial behaviours. We show that CtRL-Sim can generate realistic safety-critical scenarios while providing fine-grained control over agent behaviours., Comment: CoRL 2024

Simulation is an invaluable tool for radio-frequency system designers that enables rapid prototyping of various algorithms for imaging, target detection, classification, and tracking. However, simulating realistic radar scans is a challenging task that requires an accurate model of the scene, radio frequency material properties, and a corresponding radar synthesis function. Rather than specifying these models explicitly, we propose DART - Doppler Aided Radar Tomography, a Neural Radiance Field-inspired method which uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. We then evaluate DART by constructing a custom data collection platform and collecting a novel radar dataset together with accurate position and instantaneous velocity measurements from lidar-based localization. In comparison to state-of-the-art baselines, DART synthesizes superior radar range-Doppler images from novel views across all datasets and additionally can be used to generate high quality tomographic images., Comment: To appear in CVPR 2024; see https://wiselabcmu.github.io/dart/ for our project site