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Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings., Comment: 14 pages, 6 figures, 10 tables

Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-Objective Optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithm (GA), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems using a Deep Differentiable Reinforcement Learning (DDRL) algorithm in particle accelerators. We compare DDRL algorithm with Model Free Reinforcement Learning (MFRL), GA and Bayesian Optimization (BO) for simultaneous optimization of heat load and trip rates in the Continuous Electron Beam Accelerator Facility (CEBAF). The underlying problem enforces strict constraints on both individual states and actions as well as cumulative (global) constraint for energy requirements of the beam. A physics-based surrogate model based on real data is developed. This surrogate model is differentiable and allows back-propagation of gradients. The results are evaluated in the form of a Pareto-front for two objectives. We show that the DDRL outperforms MFRL, BO, and GA on high dimensional problems.

Quantifying the global reach of planetarium dome shows presents significant challenges due to the lack of standardized viewership tracking mechanisms across diverse planetarium venues. We present an analysis of the global impact of dome shows, presenting data regarding four documentary films from a single visualization lab. Specifically, we designed and administered a viewership survey of four long-running shows that contained cinematic scientific visualizations. Reported survey data shows that between 1.2 - 2.6 million people have viewed these four films across the 68 responding planetariums (mean: 1.9 million). When we include estimates and extrapolate for the 315 planetariums that licensed these shows, we arrive at an estimate of 16.5 - 24.1 million people having seen these films (mean: 20.3 million).

We present ALMA Band 7 continuum (340 GHz) and CO J=3-2 observations for an extended sample of disks in the Upper Scorpius OB Association (Upper Sco, age ~ 10 Myr). The targets were selected from previous studies that identified new members of Upper Sco using photometry and astrometry from the Gaia mission, and the presence of a disk has been inferred from mid-infrared excess emission. The new ALMA observations are combined with previous ALMA data to define a sample of 202 Upper Sco members with disks that have spectral types between G0 and M5.5. Among these sources, 120 (59%) have been detected in the continuum with a signal-to-noise ratio >= 3, and 83 (41%) have been detected in CO J=3-2. Both the continuum and CO J=3-2 fluxes show a strong correlation with the spectral type of the central star and the type of disk inferred from the shape of the infrared spectral energy distribution, where disks around earlier type stars and full disks are more luminous than disks around later type stars and evolved and debris disks. The median dust continuum luminosity is lower for disks in Upper Sco than in younger regions, as found in previous studies, where the differences are more pronounced in later spectral types (M4-M5) than in earlier spectral types., Comment: Accepted for publication in ApJ; 53 pages, 13 figures

Efficiently retrieving and synthesizing information from large-scale multimodal collections has become a critical challenge. However, existing video retrieval datasets suffer from scope limitations, primarily focusing on matching descriptive but vague queries with small collections of professionally edited, English-centric videos. To address this gap, we introduce $\textbf{MultiVENT 2.0}$, a large-scale, multilingual event-centric video retrieval benchmark featuring a collection of more than 218,000 news videos and 3,906 queries targeting specific world events. These queries specifically target information found in the visual content, audio, embedded text, and text metadata of the videos, requiring systems leverage all these sources to succeed at the task. Preliminary results show that state-of-the-art vision-language models struggle significantly with this task, and while alternative approaches show promise, they are still insufficient to adequately address this problem. These findings underscore the need for more robust multimodal retrieval systems, as effective video retrieval is a crucial step towards multimodal content understanding and generation tasks.

The increasing volume of papers and proposals undergoing peer review emphasizes the pressing need for greater automation to effectively manage the growing scale. In this study, we present the deployment and evaluation of machine learning and optimization techniques for assigning proposals to reviewers that was developed for the Atacama Large Millimeter/submillimeter Array (ALMA) during the Cycle 10 Call for Proposals issued in 2023. By utilizing topic modeling algorithms, we identify the proposal topics and assess reviewers' expertise based on their historical ALMA proposal submissions. We then apply an adapted version of the assignment optimization algorithm from PeerReview4All (Stelmakh et al. 2021a) to maximize the alignment between proposal topics and reviewer expertise. Our evaluation shows a significant improvement in matching reviewer expertise: the median similarity score between the proposal topic and reviewer expertise increased by 51 percentage points compared to the previous cycle, and the percentage of reviewers reporting expertise in their assigned proposals rose by 20 percentage points. Furthermore, the assignment process proved highly effective in that no proposals required reassignment due to significant mismatches, resulting in a savings of 3 to 5 days of manual effort., Comment: 19 pages, 5 figures, submitted to PASP

We explore models where new light exotic states interact with the Standard Model through an asymmetric di-boson portal with one gluon and one W boson. We consider the complete set of effective operators up to dimension 6 which couple a single light exotic (LEX) state to at least one W boson and 1 gluon. Other Standard Model particles may also be present in the interactions. We discover that the W-gluon portal allows us to access a large array of exotic states in non-trivial representations of SU(3) and SU(2). We discuss new single production collider modes and phenomenological signatures for these light exotic states at LHC and e-p colliders., Comment: 29 pages, 16 figures, 9 tables

The advancement of robotic systems has revolutionized numerous industries, yet their operation often demands specialized technical knowledge, limiting accessibility for non-expert users. This paper introduces ROSA (Robot Operating System Agent), an AI-powered agent that bridges the gap between the Robot Operating System (ROS) and natural language interfaces. By leveraging state-of-the-art language models and integrating open-source frameworks, ROSA enables operators to interact with robots using natural language, translating commands into actions and interfacing with ROS through well-defined tools. ROSA's design is modular and extensible, offering seamless integration with both ROS1 and ROS2, along with safety mechanisms like parameter validation and constraint enforcement to ensure secure, reliable operations. While ROSA is originally designed for ROS, it can be extended to work with other robotics middle-wares to maximize compatibility across missions. ROSA enhances human-robot interaction by democratizing access to complex robotic systems, empowering users of all expertise levels with multi-modal capabilities such as speech integration and visual perception. Ethical considerations are thoroughly addressed, guided by foundational principles like Asimov's Three Laws of Robotics, ensuring that AI integration promotes safety, transparency, privacy, and accountability. By making robotic technology more user-friendly and accessible, ROSA not only improves operational efficiency but also sets a new standard for responsible AI use in robotics and potentially future mission operations. This paper introduces ROSA's architecture and showcases initial mock-up operations in JPL's Mars Yard, a laboratory, and a simulation using three different robots. The core ROSA library is available as open-source., Comment: Under review for IEEE Aerospace Conference, 20 pages, 20 figures

How are we able to learn about complex current events just from short snippets of video? While natural language enables straightforward ways to represent under-specified, partially observable events, visual data does not facilitate analogous methods and, consequently, introduces unique challenges in event understanding. With the growing prevalence of vision-capable AI agents, these systems must be able to model events from collections of unstructured video data. To tackle robust event modeling in multimodal settings, we introduce a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. We propose a corresponding benchmark for this task, MultiVENT-G, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. We propose a collection of LLM-driven approaches to the task of multimodal event analysis, and evaluate them on MultiVENT-G. Results illustrate the challenges that abstract event understanding poses and demonstrates promise in event-centric video-language systems., Comment: Preprint; 9 pages; 2024 EMNLP Findings

Binary black holes are the most abundant source of gravitational-wave observations. Gravitational-wave observatories in the next decade will require tremendous increases in the accuracy of numerical waveforms modeling binary black holes, compared to today's state of the art. One approach to achieving the required accuracy is using spectral-type methods that scale to many processors. Using the SpECTRE numerical-relativity code, we present the first simulations of a binary black hole inspiral, merger, and ringdown using discontinuous Galerkin methods. The efficiency of discontinuous Galerkin methods allows us to evolve the binary through ~18 orbits at reasonable computational cost. We then use SpECTRE's Cauchy Characteristic Evolution (CCE) code to extract the gravitational waves at future null infinity. The open-source nature of SpECTRE means this is the first time a spectral-type method for simulating binary black hole evolutions is available to the entire numerical-relativity community., Comment: 32 pages, 12 figures, 28 ancillary input files

Accurate estimation of project costs and durations remains a pivotal challenge in software engineering, directly impacting budgeting and resource management. Traditional estimation techniques, although widely utilized, often fall short due to their complexity and the dynamic nature of software development projects. This study introduces an innovative approach using Large Language Models (LLMs) to enhance the accuracy and usability of project cost predictions. We explore the efficacy of LLMs against traditional methods and contemporary machine learning techniques, focusing on their potential to simplify the estimation process and provide higher accuracy. Our research is structured around critical inquiries into whether LLMs can outperform existing models, the ease of their integration into current practices, outperform traditional estimation, and why traditional methods still prevail in industry settings. By applying LLMs to a range of real-world datasets and comparing their performance to both state-of-the-art and conventional methods, this study aims to demonstrate that LLMs not only yield more accurate estimates but also offer a user-friendly alternative to complex predictive models, potentially transforming project management strategies within the software industry.

Generating high magnetic fields requires materials with not only high electric conductivity, but also good strength properties in order to withstand the necessarily strong Lorentz forces. A number of bi-metal composites, most notably Cu/Nb, are considered to be good candidates for this purpose. Here, we generalize our previous work on Cu/Nb in order to predict, from theory, the dependence of electric conductivity on the microstructure and volume fraction of the less conductive component for a number of other bi-metal composites. Together with information on strength properties (taken from previous literature), the conductivity information we provide in this work can help to identify new promising candidate materials (such as Cu/Nb, Cu/Ag, Cu/W, ...) for magnet applications with the highest achievable field strengths., Comment: 11 pages, 4 figures; v2+v3 minor revision

Over the last two decades, spatial light modulators (SLMs) have revolutionised our ability to shape optical fields. They grant independent dynamic control over thousands of degrees-of-freedom within a single light beam. In this work we test a new type of SLM, known as a phase-only light modulator (PLM), that blends the high efficiency of liquid crystal SLMs with the fast switching rates of binary digital micro-mirror devices (DMDs). A PLM has a 2D mega-pixel array of micro-mirrors. The vertical height of each micro-mirror can be independently adjusted with 4-bit precision. Here we provide a concise tutorial on the operation and calibration of a PLM. We demonstrate arbitrary pattern projection, aberration correction, and control of light transport through complex media. We show high-speed wavefront shaping through a multimode optical fibre -- scanning over 2000 points at 1.44 kHz. We make available our custom high-speed PLM control software library developed in C++. As PLMs are based upon micro-electromechanical system (MEMS) technology, they are polarisation agnostic, and possess fundamental switching rate limitations equivalent to that of DMDs -- with operation at up to 10 kHz anticipated in the near future. We expect PLMs will find high-speed light shaping applications across a range of fields including adaptive optics, microscopy, optogenetics and quantum optics., Comment: 15 pages, 5 figures

The physical origin of the large cavities observed in transition disks is to date still unclear. Different physical mechanisms (e.g., a companion, dead zones, enhanced grain growth) produce disk cavities of different depth, and the expected spatial distribution of gas and solids in each mechanism is not the same. In this work, we analyze the multi-wavelength interferometric visibilities of dust continuum observations obtained with ALMA and VLA for six transition disks: CQTau, UXTau A, LkCa15, RXJ1615, SR24S, and DMTau, and calculate brightness radial profiles, where diverse emission morphology is revealed at different wavelengths. The multi-wavelength data is used to model the spectral energy distribution and compute constraints on the radial profile of the dust surface density, maximum grain size, and dust temperature in each disk. They are compared with the observational signatures expected from various physical mechanisms responsible for disk cavities. The observational signatures suggest that the cavities observed in the disks around UXTau A, LkCa15, and RXJ1615 could potentially originate from a dust trap created by a companion. Conversely, in the disks around CQTau, SR24S, DMTau, the origin of the cavity remains unclear, although it is compatible with a pressure bump and grain growth within the cavity.

We present galaxy MACS0416-Y1 at z$_{\rm{spec}} = 8.312$ as observed by the CAnadian NIRISS Unbiased Cluster Survey (CANUCS). MACS0416-Y1 has been shown to have extreme dust properties, thus, we study the physical properties and star formation histories of its resolved components. Overall, we find that MACS0416-Y1 is undergoing a star formation burst in three resolved clumps. The central clump is less massive compared to the other clumps and possibly formed in the merging process of the two larger clumps. Although the star formation history indicates an ongoing star formation burst, this gas-rich galaxy shows comparable star formation efficiency to cosmic noon galaxies. Using NIRSpec prism spectroscopy, we measure metallicity, $12 +\log\rm{(O/H)} = 7.76\pm0.03$ , ionisation parameter, $\log U = -2.48\pm0.03$, and electron temperature $\rm{T}_e = 18000\pm 4000 K $. The emission line ratios of the galaxy indicate an evolved Interstellar medium (ISM) similar to $z\sim2$ star-forming galaxies. Further, we find possible presence of ionisation from an active galactic nuclei (AGN) using emission line diagnostics, however, we do not detect broad line component in H$\beta$ emission line. As this gas-rich galaxy is undergoing a major merger, we hypothesise that the high dust temperature in MACS0416-Y1 is caused by the star formation burst or a possible narrow-line AGN., Comment: Submitted to ApJL

Adapting the step size locally in the no-U-turn sampler (NUTS) is challenging because the step-size and path-length tuning parameters are interdependent. The determination of an optimal path length requires a predefined step size, while the ideal step size must account for errors along the selected path. Ensuring reversibility further complicates this tuning problem. In this paper, we present a method for locally adapting the step size in NUTS that is an instance of the Gibbs self-tuning (GIST) framework. Our approach guarantees reversibility with an acceptance probability that depends exclusively on the conditional distribution of the step size. We validate our step-size-adaptive NUTS method on Neal's funnel density and a high-dimensional normal distribution, demonstrating its effectiveness in challenging scenarios., Comment: for companion code, see https://github.com/bob-carpenter/adaptive-hmc

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

NASA's return to the Moon presents unparalleled opportunities to advance high-impact scientific capabilities. At the cutting edge of these possibilities are extremely high-resolution interferometric observations at visible and ultraviolet wavelengths. Such technology can resolve the surfaces of stars, explore the inner accretion disks of nascent stars and black holes, and eventually enable us to observe surface features and weather patterns on nearby exoplanets. We have been awarded Phase 1 support from NASA's Innovative Advanced Concepts (NIAC) program to explore the feasibility of constructing a high-resolution, long-baseline UV/optical imaging interferometer on the lunar surface, in conjunction with the Artemis Program. A 1996 study comparing interferometers on the Moon versus free-flyers in space concluded that, without pre-existing lunar infrastructure, free-flyers were preferable. However, with the advent of the Artemis Program, it is now crucial to revisit the potential of building lunar interferometers. Our objective is to conduct a study with the same level of rigor applied to large baseline, free-flying interferometers during the 2003-2005 NASA Vision Missions Studies. This preparation is essential for timely and effective utilization of the forthcoming lunar infrastructure. In this paper, we highlight the groundbreaking potential of a lunar surface-based interferometer. This concept study will be a huge step forward to larger arrays on both the moon and free-flying in space, over a wide variety of wavelengths and science topics. Our Phase 1 study began in April 2024, and here we present a concise overview of our vision and the progress made so far., Comment: 6 pages, 3 figures, SPIE Astronomical Telescopes + Instrumentation 2024 (Invited Paper)

MoonLITE (Lunar InTerferometry Explorer) is an Astrophysics Pioneers proposal to develop, build, fly, and operate the first separated-aperture optical interferometer in space, delivering sub-mas science results. MoonLITE will leverage the Pioneers opportunity for utilizing NASA's Commercial Lunar Payload Services (CLPS) to deliver an optical interferometer to the lunar surface, enabling unprecedented discovery power by combining high spatial resolution from optical interferometry with deep sensitivity from the stability of the lunar surface. Following landing, the CLPS-provided rover will deploy the pre-loaded MoonLITE outboard optical telescope 100 meters from the lander's inboard telescope, establishing a two-element interferometric observatory with a single deployment. MoonLITE will observe targets as faint as 17th magnitude in the visible, exceeding ground-based interferometric sensitivity by many magnitudes, and surpassing space-based optical systems resolution by a factor of 50 times. The capabilities of MoonLITE open a unique discovery space that includes direct size measurements of the smallest, coolest stars and substellar brown dwarfs; searches for close-in stellar companions orbiting exoplanet-hosting stars that could confound our understanding and characterization of the frequency of Earth-like planets; direct size measurements of young stellar objects and characterization of the terrestrial planet-forming regions of these young stars; measurements of the inner regions and binary fraction of active galactic nuclei; and a probe of the very nature of spacetime foam itself. A portion of the observing time will also be made available to the broader community via a guest observer program. MoonLITE takes advantage of the CLPS opportunity and delivers an unprecedented combination of sensitivity and angular resolution at the remarkably affordable cost point of Pioneers., Comment: 13 pages, 8 figures, SPIE Astronomical Telescopes + Instrumentation 2024

Observers and practitioners of artificial intelligence (AI) have proposed an FDA-style licensing regime for the most advanced AI models, or 'frontier' models. In this paper, we explore the applicability of approval regulation -- that is, regulation of a product that combines experimental minima with government licensure conditioned partially or fully upon that experimentation -- to the regulation of frontier AI. There are a number of reasons to believe that approval regulation, simplistically applied, would be inapposite for frontier AI risks. Domains of weak fit include the difficulty of defining the regulated product, the presence of Knightian uncertainty or deep ambiguity about harms from AI, the potentially transmissible nature of risks, and distributed activities among actors involved in the AI lifecycle. We conclude by highlighting the role of policy learning and experimentation in regulatory development, describing how learning from other forms of AI regulation and improvements in evaluation and testing methods can help to overcome some of the challenges we identify., Comment: Accepted to Seventh AAAI/ACM Conference on AI, Ethics, and Society (2024)

We present a study connecting the physical properties of protostellar envelopes to the morphology of the envelope-scale magnetic field. We use the ALMA polarization observations of 55 young prtostars at 0.87 mm on $\sim400-3000$ au scales from the {\em B}-field Orion Protostellar Survey (BOPS) to infer the envelope-scale magnetic field and both dust and gas emission on comparable scales to measure the envelope properties. We find that the protostellar envelopes with compact polarized dust emission tend to have lower envelope masses, than the sources with more extended envelopes. We also find that protostars showing hourglass-field morphologies tend to have lower velocity dispersions in their envelopes, whereas systems with spiral-field morphologies have higher velocity dispersion. Combining with the disk properties taken from the Orion VLA/ALMA Nascent Disk and Multiplicity (VANDAM) survey, we connect envelope properties to fragmentation. Our results suggest that envelope mass may not correlate with fragmentation, whereas turbulence appears to promote fragmentation. On the other hand, we find that fragmentation is suppressed in systems with pinched magnetic fields, suggesting that the magnetic field play a role on providing additional support against gravitational collapse, and the formation of an hourglass-like field may coincide with enhanced magnetic braking that removes angular momentum and hinders the formation of embedded disks. Nevertheless, significant misalignment between magnetic field and outflow axes tends to reduce magnetic braking, leading to the formation of larger disks., Comment: 18 pages, 6 figures, submitted to ApJ

Detecting planet signatures in protoplanetary disks is fundamental to understanding how and where planets form. In this work, we report dust and gas observational hints of planet formation in the disk around 2MASS-J16120668-301027, as part of the ALMA Large Program "AGE-PRO: ALMA survey of Gas Evolution in Protoplanetary disks". The disk was imaged with the Atacama Large Millimeter/submillimeter Array (ALMA) at Band 6 (1.3 mm) in dust continuum emission and four molecular lines: $^{12}$CO(J=2-1), $^{13}$CO(J=2-1), C$^{18}$O(J=2-1), and H$_2$CO(J=3$_{(3,0)}$-2$_{(2,0)}$). Resolved observations of the dust continuum emission (angular resolution of $\sim 150$ mas, 20 au) show a ring-like structure with a peak at $0.57 ^{\prime \prime}$ (75 au), a deep gap with a minimum at 0.24$^{\prime \prime}$ (31 au), an inner disk, a bridge connecting the inner disk and the outer ring, along with a spiral arm structure, and a tentative detection (to $3\sigma$) of a compact emission at the center of the disk gap, with an estimated dust mass of $\sim 2.7-12.9$ Lunar masses. We also detected a kinematic kink (not coincident with any dust substructure) through several $^{12}$CO channel maps (angular resolution $\sim$ 200 mas, 30 au), located at a radius of $\sim 0.875^{\prime \prime}$ (115.6 au). After modeling the $^{12}$CO velocity rotation around the protostar, we identified a tentative rotating-like structure at the kink location with a geometry similar to that of the disk. We discuss potential explanations for the dust and gas substructures observed in the disk, and their potential connection to signatures of planet formation., Comment: 24 pages, 15 figures

It is widely accepted that cosmic rays are a plausible cause of DRAM errors in high-performance computing (HPC) systems, and various studies suggest that they could explain some aspects of the observed DRAM error behavior. However, this phenomenon is insufficiently studied in production environments. We analyze the correlations between cosmic rays and DRAM errors on two HPC clusters: a production supercomputer with server-class DDR3-1600 and a prototype with LPDDR3-1600 and no hardware error correction. Our error logs cover 2000 billion MB-hours for the MareNostrum 3 supercomputer and 135 million MB-hours for the Mont-Blanc prototype. Our analysis combines quantitative analysis, formal statistical methods and machine learning. We detect no indications that cosmic rays have any influence on the DRAM errors. To understand whether the findings are specific to systems under study, located at 100 meters above the sea level, the analysis should be repeated on other HPC clusters, especially the ones located on higher altitudes. Also, analysis can (and should) be applied to revisit and extend numerous previous studies which use cosmic rays as a hypothetical explanation for some aspects of the observed DRAM error behaviors., Comment: Accepted for publication in SBAC-PAD'24

Scaling to larger systems, with current levels of reliability, requires cost-effective methods to mitigate hardware failures. One of the main causes of hardware failure is an uncorrected error in memory, which terminates the current job and wastes all computation since the last checkpoint. This paper presents the first adaptive method for triggering uncorrected error mitigation. It uses a prediction approach that considers the likelihood of an uncorrected error and its current potential cost. The method is based on reinforcement learning, and the only user-defined parameters are the mitigation cost and whether the job can be restarted from a mitigation point. We evaluate our method using classical machine learning metrics together with a cost-benefit analysis, which compares the cost of mitigation actions with the benefits from mitigating some of the errors. On two years of production logs from the MareNostrum supercomputer, our method reduces lost compute time by 54% compared with no mitigation and is just 6% below the optimal Oracle method. All source code is open source., Comment: Published in HPDC'24

The generality and robustness of inference algorithms is critical to the success of widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl. When designing a new general-purpose inference algorithm, whether it involves Monte Carlo sampling or variational approximation, the fundamental problem arises in evaluating its accuracy and efficiency across a range of representative target models. To solve this problem, we propose posteriordb, a database of models and data sets defining target densities along with reference Monte Carlo draws. We further provide a guide to the best practices in using posteriordb for model evaluation and comparison. To provide a wide range of realistic target densities, posteriordb currently comprises 120 representative models and has been instrumental in developing several general inference algorithms.

Spatially resolved studies are key to understanding when, where, and how stars form within galaxies. Using slitless grism spectra and broadband imaging from the CAnadian NIRISS Unbiased Cluster Survey (CANUCS) we study the spatially resolved properties of a strongly lensed ($\mu$ = 5.4$\pm$1.8) z = 0.8718 galaxy pair consisting of a blue face-on galaxy (10.2 $\pm$ 0.2 log($M/M_\odot$)) with multiple star-forming clumps and a dusty red edge-on galaxy (9.9 $\pm$ 0.3 log($M/M_\odot$)). We produce accurate H$\alpha$ maps from JWST/NIRISS grism data using a new methodology that accurately models spatially varying continuum and emission line strengths. With spatially resolved indicators, we probe star formation on timescales of $\sim$ 10 Myr (NIRISS H$\alpha$ emission line maps) and $\sim$ 100 Myr (UV imaging and broadband SED fits). Taking the ratio of the H$\alpha$ to UV flux ($\eta$), we measure spatially resolved star formation burstiness. We find that in the face-on galaxy both H$\alpha$ and broadband star formation rates (SFRs) drop at large galactocentric radii by a factor of $\sim$ 4.7 and 3.8 respectively, while SFR over the last $\sim$ 100 Myrs has increased by a factor of 1.6. Additionally, of the 20 clumps identified in the galaxy pair we find that 7 are experiencing bursty star formation, while 10 clumps are quenching and 3 are in equilibrium (either being in a state of steady star formation or post-burst). Our analysis reveals that the blue face-on galaxy disk is predominantly in a quenching or equilibrium phase. However, the most intense quenching within the galaxy is seen in the quenching clumps. This pilot study demonstrates what JWST/NIRISS data can reveal about spatially varying star formation in galaxies at Cosmic Noon., Comment: MNRAS Accepted, 15 pages, 12 figures

Dental disease is still one of the most common chronic diseases in the United States. While dental disease is preventable through healthy oral self-care behaviors (OSCB), this basic behavior is not consistently practiced. We have developed Oralytics, an online, reinforcement learning (RL) algorithm that optimizes the delivery of personalized intervention prompts to improve OSCB. In this paper, we offer a full overview of algorithm design decisions made using prior data, domain expertise, and experiments in a simulation test bed. The finalized RL algorithm was deployed in the Oralytics clinical trial, conducted from fall 2023 to summer 2024.

Rings and gaps are among the most widely observed forms of substructure in protoplanetary disks. A gap-ring pair may be formed when a planet carves a gap in the disk, which produces a local pressure maximum following the gap that traps inwardly drifting dust grains and appears as a bright ring due to the enhanced dust density. A dust-trapping ring would provide a promising environment for solid growth and possibly planetesimal production via the streaming instability. We present evidence of dust trapping in the bright ring of the planet-hosting disk Elias 2-24, from the analysis of 1.3 mm and 3 mm ALMA observations at high spatial resolution (0.029 arcsec, 4.0 au). We leverage the high spatial resolution to demonstrate that larger grains are more efficiently trapped and place constraints on the local turbulence ($8 \times 10^{-4} < \alpha_\mathrm{turb} < 0.03$) and the gas-to-dust ratio ($\Sigma_g / \Sigma_d < 30$) in the ring. Using a scattering-included marginal probability analysis we measure a total dust disk mass of $M_\mathrm{dust} = 13.8^{+0.7}_{-0.5} \times 10^{-4} \ M_\odot$. We also show that at the orbital radius of the proposed perturber, the gap is cleared of material down to a flux contrast of 10$^{-3}$ of the peak flux in the disk., Comment: 18 pages, 12 figures, accepted to ApJ

Predicting drug efficacy and safety in vivo requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations. However, current molecular representation learning methods do not provide a comprehensive view of cell states under these perturbations and struggle to remove noise, hindering model generalization. We introduce the Information Alignment (InfoAlign) approach to learn molecular representations through the information bottleneck method in cells. We integrate molecules and cellular response data as nodes into a context graph, connecting them with weighted edges based on chemical, biological, and computational criteria. For each molecule in a training batch, InfoAlign optimizes the encoder's latent representation with a minimality objective to discard redundant structural information. A sufficiency objective decodes the representation to align with different feature spaces from the molecule's neighborhood in the context graph. We demonstrate that the proposed sufficiency objective for alignment is tighter than existing encoder-based contrastive methods. Empirically, we validate representations from InfoAlign in two downstream applications: molecular property prediction against up to 27 baseline methods across four datasets, plus zero-shot molecule-morphology matching., Comment: 20 pages, 5 tables, 7 figures

Strong gravitational lensing in galaxy clusters has become an essential tool in astrophysics, allowing us to directly probe the dark matter distribution and study magnified background sources. The precision and reliability of strong lensing models rely heavily on the number and quality of multiple images of background sources with spectroscopic redshifts. We present an updated strong lensing model of the galaxy cluster MACS J0416.1-2403 with the largest sample of multiple images with spectroscopic redshifts in a galaxy cluster field to date. Furthermore, we aim to demonstrate the effectiveness of JWST particularly its NIRISS camera, for strong lensing studies. We use the JWST 's NIRCam imaging and NIRSpec and NIRISS spectroscopy from the CAnadian NIRISS Unbiased Cluster Survey (CANUCS). The cluster mass model is constrained using Lenstool software. Our new dataset, used for constraining the lens model, comprises 303 secure multiple images from 111 background sources and includes systems with previously known MUSE redshift and systems for which we obtained spectroscopic redshift for the first time using NIRISS and NIRSpec spectroscopy. The total number of secure spectroscopic systems is >20% higher than in the previous strong lensing studies of this cluster. The derived strong lensing model can reproduce multiple images with the root-mean-square distance of 0.53''. We also provide a full catalogue with 415 multiple images, including less reliable candidates. We furthermore demonstrate the effectiveness of JWST particularly NIRISS, for strong lensing studies. As NIRISS F115W, F150W, and F200W grism spectroscopy captures at least two of the [OII] {\lambda}3727, [OIII] {\lambda}{\lambda}4959, 5007, and H{\alpha} lines at 1

Drug-target interaction (DTI) prediction is crucial for identifying new therapeutics and detecting mechanisms of action. While structure-based methods accurately model physical interactions between a drug and its protein target, cell-based assays such as Cell Painting can better capture complex DTI interactions. This paper introduces MOTIVE, a Morphological cOmpound Target Interaction Graph dataset comprising Cell Painting features for 11,000 genes and 3,600 compounds, along with their relationships extracted from seven publicly available databases. We provide random, cold-source (new drugs), and cold-target (new genes) data splits to enable rigorous evaluation under realistic use cases. Our benchmark results show that graph neural networks that use Cell Painting features consistently outperform those that learn from graph structure alone, feature-based models, and topological heuristics. MOTIVE accelerates both graph ML research and drug discovery by promoting the development of more reliable DTI prediction models. MOTIVE resources are available at https://github.com/carpenter-singh-lab/motive.

Decay energy spectrometry (DES) is a novel radiometric technique for high-precision analysis of nuclear materials. DES employs the unique thermal detection physics of cryogenic microcalorimeters with ultra-high energy resolution and 100$\%$ detection efficiency to accomplish high precision decay energy measurements. Low-activity nuclear samples of 1 Bq or less, and without chemical separation, are used to provide elemental and isotopic compositions in a single measurement. Isotopic ratio precisions of 1 ppm - 1,000 ppm (isotope dependent), which is close to that of the mass spectrometry, have been demonstrated in 12-hour DES measurements of ~5 Bq samples of certified reference materials of uranium (U) and plutonium (Pu). DES has very different systematic biases and uncertainties, as well as different sensitivities to nuclides, compared to mass-spectrometry techniques. Therefore, the accuracy and confidence of nuclear material assays can be improved by combining this new technique with existing mass-spectrometry techniques. Commercial-level DES techniques and equipment are being developed for the implementation of DES at the Nuclear Material Laboratory (NML) of International Atomic Energy Agency (IAEA) to provide complementary measurements to the existing technologies. The paper describes details of DES measurement methods, as well as DES precision and accuracy to U and Pu standard sources to discuss its capability in analysis of nuclear safeguards samples., Comment: This was submitted to 2022 IAEA symposium on nuclear safeguards (https://www.iaea.org/events/sg-2022), and posted at https://media.superevent.com/documents/20221027/668fdac0ee8d895ec6bcf293b1c42e6a/id-145.pdf

With the increasing prevalence of probabilistic programming languages, Hamiltonian Monte Carlo (HMC) has become the mainstay of applied Bayesian inference. However HMC still struggles to sample from densities with multiscale geometry: a large step size is needed to efficiently explore low curvature regions while a small step size is needed to accurately explore high curvature regions. We introduce the delayed rejection generalized HMC (DR-G-HMC) sampler that overcomes this challenge by employing dynamic step size selection, inspired by differential equation solvers. In a single sampling iteration, DR-G-HMC sequentially makes proposals with geometrically decreasing step sizes if necessary. This simulates Hamiltonian dynamics with increasing fidelity that, in high curvature regions, generates proposals with a higher chance of acceptance. DR-G-HMC also makes generalized HMC competitive by decreasing the number of rejections which otherwise cause inefficient backtracking and prevents directed movement. We present experiments to demonstrate that DR-G-HMC (1) correctly samples from multiscale densities, (2) makes generalized HMC methods competitive with the state of the art No-U-Turn sampler, and (3) is robust to tuning parameters., Comment: 9 pages, 5 figures

In applied statistics and machine learning, the "gold standards" used for training are often biased and almost always noisy. Dawid and Skene's justifiably popular crowdsourcing model adjusts for rater (coder, annotator) sensitivity and specificity, but fails to capture distributional properties of rating data gathered for training, which in turn biases training. In this study, we introduce a general purpose measurement-error model with which we can infer consensus categories by adding item-level effects for difficulty, discriminativeness, and guessability. We further show how to constrain the bimodal posterior of these models to avoid (or if necessary, allow) adversarial raters. We validate our model's goodness of fit with posterior predictive checks, the Bayesian analogue of $\chi^2$ tests. Dawid and Skene's model is rejected by goodness of fit tests, whereas our new model, which adjusts for item heterogeneity, is not rejected. We illustrate our new model with two well-studied data sets, binary rating data for caries in dental X-rays and implication in natural language.