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Legacy software systems, written in outdated languages like MUMPS and mainframe assembly, pose challenges in efficiency, maintenance, staffing, and security. While LLMs offer promise for modernizing these systems, their ability to understand legacy languages is largely unknown. This paper investigates the utilization of LLMs to generate documentation for legacy code using two datasets: an electronic health records (EHR) system in MUMPS and open-source applications in IBM mainframe Assembly Language Code (ALC). We propose a prompting strategy for generating line-wise code comments and a rubric to evaluate their completeness, readability, usefulness, and hallucination. Our study assesses the correlation between human evaluations and automated metrics, such as code complexity and reference-based metrics. We find that LLM-generated comments for MUMPS and ALC are generally hallucination-free, complete, readable, and useful compared to ground-truth comments, though ALC poses challenges. However, no automated metrics strongly correlate with comment quality to predict or measure LLM performance. Our findings highlight the limitations of current automated measures and the need for better evaluation metrics for LLM-generated documentation in legacy systems., Comment: Abbreviated version submitted to LLM4Code 2025 (a workshop co-located with ICSE 2025), 13 pages, 3 figures

Collisions play a crucial role in governing particle and energy transport in plasmas confined in a magnetic mirror trap. Modern gyrokinetic codes are used to model transport in magnetic mirrors, but some of these codes utilize approximate model collision operators. This study focuses on a Pastukhov-style method of images calculation of particle and energy confinement times using a Lenard-Bernstein model collision operator. Prior work on parallel particle and energy balances used a different Fokker-Planck plasma collision operator and the method needs to be extended in non-trivial ways to study the Lenard-Bernstein operator. To assess the effectiveness of our approach, we compare our results with a modern finite element solver. Our findings reveal that the particle confinement time scales like $a \exp(a^2)$ using the Lenard-Bernstein operator, in contrast to the more accurate scaling that the Coulomb collision operator would yield $a^2 \exp(a^2)$, where $a^2$ is approximately proportional to the ambipolar potential. We propose that codes modeling collisional losses in a magnetic mirrors utilizing the Lenard-Bernstein or Dougherty collision operator scale their collision frequency of any electrostatically confined species. This study illuminates the intricate role the collision operator plays in the Pastukhov-style method of images calculation of collisional confinement., Comment: 21 pages, 4 figures, 3 tables, submitted to journal of plasma physics

Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, critical barriers to their widespread use include low signal-to-noise ratio (SNR), generally low image quality, and long scan duration. As these systems can operate in unusual environments, the level and spectral characteristics of the environmental electromagnetic inference (EMI) noise can change substantially across sites and scans, further reducing image quality. Methods for accelerating acquisition and boosting image quality are of critical importance to enable clinically actionable high-quality imaging in these systems. Despite the role that compressed sensing (CS) and artificial intelligence (AI)-based methods have had in improving image quality for high-field MRI, their adoption for low-field imaging is in its infancy, and it is unclear how robust these methods are in low SNR regimes. Here, we investigate and compare leading CS and AI-based methods for image reconstruction from subsampled data and perform a thorough analysis of their performance across a range of SNR values. We compare classical L1-wavelet CS with leading data-driven and model-driven AI methods. Experiments are performed using publicly available datasets and our own low-field and high-field experimental data. Specifically, we apply an unrolled AI network to low-field MRI, and find it outperforms competing reconstruction methods. We prospectively deploy our undersampling methods to accelerate imaging on a 6.5 mT MRI scanner. This work highlights the potential and pitfalls of advanced reconstruction techniques in low-field MRI, paving the way for broader clinical applications.

This paper presents a comparative analysis of hallucination detection systems for AI, focusing on automatic summarization and question answering tasks for Large Language Models (LLMs). We evaluate different hallucination detection systems using the diagnostic odds ratio (DOR) and cost-effectiveness metrics. Our results indicate that although advanced models can perform better they come at a much higher cost. We also demonstrate how an ideal hallucination detection system needs to maintain performance across different model sizes. Our findings highlight the importance of choosing a detection system aligned with specific application needs and resource constraints. Future research will explore hybrid systems and automated identification of underperforming components to enhance AI reliability and efficiency in detecting and mitigating hallucinations., Comment: 18 pags, 13 figures, 2 tables

Technological trends show that Radio Frequency Reinforcement Learning (RFRL) will play a prominent role in the wireless communication systems of the future. Applications of RFRL range from military communications jamming to enhancing WiFi networks. Before deploying algorithms for these purposes, they must be trained in a simulation environment to ensure adequate performance. For this reason, we previously created the RFRL Gym: a standardized, accessible tool for the development and testing of reinforcement learning (RL) algorithms in the wireless communications space. This environment leveraged the OpenAI Gym framework and featured customizable simulation scenarios within the RF spectrum. However, the RFRL Gym was limited to training a single RL agent per simulation; this is not ideal, as most real-world RF scenarios will contain multiple intelligent agents in cooperative, competitive, or mixed settings, which is a natural consequence of spectrum congestion. Therefore, through integration with Ray RLlib, multi-agent reinforcement learning (MARL) functionality for training and assessment has been added to the RFRL Gym, making it even more of a robust tool for RF spectrum simulation. This paper provides an overview of the updated RFRL Gym environment. In this work, the general framework of the tool is described relative to comparable existing resources, highlighting the significant additions and refactoring we have applied to the Gym. Afterward, results from testing various RF scenarios in the MARL environment and future additions are discussed., Comment: Accepted to IEEE CCNC 2025

Drug discovery typically consists of multiple steps, including identifying a target protein key to a disease's etiology, validating that interacting with this target could prevent symptoms or cure the disease, discovering a small molecule or biologic therapeutic to interact with it, and optimizing the candidate molecule through a complex landscape of required properties. Drug discovery related tasks often involve prediction and generation while considering multiple entities that potentially interact, which poses a challenge for typical AI models. For this purpose we present MAMMAL - Molecular Aligned Multi-Modal Architecture and Language - a method that we applied to create a versatile multi-task multi-align foundation model that learns from large-scale biological datasets (2 billion samples) across diverse modalities, including proteins, small molecules, and genes. We introduce a prompt syntax that supports a wide range of classification, regression, and generation tasks. It allows combining different modalities and entity types as inputs and/or outputs. Our model handles combinations of tokens and scalars and enables the generation of small molecules and proteins, property prediction, and transcriptomic lab test predictions. We evaluated the model on 11 diverse downstream tasks spanning different steps within a typical drug discovery pipeline, where it reaches new SOTA in 9 tasks and is comparable to SOTA in 2 tasks. This performance is achieved while using a unified architecture serving all tasks, in contrast to the original SOTA performance achieved using tailored architectures. The model code and pretrained weights are publicly available at https://github.com/BiomedSciAI/biomed-multi-alignment and https://huggingface.co/ibm/biomed.omics.bl.sm.ma-ted-458m.

The synthesis of binary nanocrystal superlattices (BNSLs) enables the targeted integration of orthogonal physical properties, like photoluminescence and magnetism, into a single superstructure, unlocking a vast design space for multifunctional materials. Yet, the formation mechanism of BNSLs remains poorly understood, restricting the use of simulation to predict the structure and properties of the superlattices. Here, we use a combination of in situ scattering and molecular simulation to elucidate the self-assembly of two common BNSLs through emulsion templating. Our self-assembly experiments reveal that no intermediate structures precede the formation of the final binary phases, indicating that their formation proceeds through classical nucleation. Using simulations, we find that, despite the formation of AlB2 and NaZn13 typically being attributed to entropy, their self-assembly is most consistent with the nanocrystals possessing short-range interparticle attraction, which we find can dramatically accelerate nucleation kinetics in BNSLs. We also find homogenous, classical nucleation in simulation, corroborating our experiments. These results establish a robust correspondence between experiment and theory, paving the way towards a priori prediction of BNSLs.

Stein variational inference (SVI) is a sample-based approximate Bayesian inference technique that generates a sample set by jointly optimizing the samples' locations to minimize an information-theoretic measure of discrepancy with the target probability distribution. SVI thus provides a fast and significantly more sample-efficient approach to Bayesian inference than traditional (random-sampling-based) alternatives. However, the optimization techniques employed in existing SVI methods struggle to address problems in which the target distribution is high-dimensional, poorly-conditioned, or non-convex, which severely limits the range of their practical applicability. In this paper, we propose a novel trust-region optimization approach for SVI that successfully addresses each of these challenges. Our method builds upon prior work in SVI by leveraging conditional independences in the target distribution (to achieve high-dimensional scaling) and second-order information (to address poor conditioning), while additionally providing an effective adaptive step control procedure, which is essential for ensuring convergence on challenging non-convex optimization problems. Experimental results show our method achieves superior numerical performance, both in convergence rate and sample accuracy, and scales better in high-dimensional distributions, than previous SVI techniques.

How embedded, actively accreting low-mass protostars accrete their mass is still greatly debated. Observations are now piecing together the puzzle of embedded protostellar accretion, in particular with new facilities in the near-infrared. However, high-resolution theoretical models are still lacking, with a stark paucity of detailed simulations of these early phases. Here we present high-resolution non-ideal magneto-hydrodynamic simulations of a Solar mass protostar accreting at rates exceeding 10$^{-6} M_{\odot}$ yr$^{-1}$. We show the results of the accretion flow for four different protostellar magnetic fields, 10 G, 500 G, 1 kG, and 2 kG, combined with a disk magnetic field. For weaker (10 G and 500 G) protostar magnetic fields, accretion occurs via a turbulent boundary layer mode, with disk material impacting across the protostellar surface. In the 500 G model, the presence of a magnetically dominated outflow focuses the accretion towards the equator, slightly enhancing and ordering the accretion. For kG magnetic fields, the disk becomes truncated due to the protostellar dipole and exhibits magnetospheric accretion, with the 2 kG model having accretion bursts induced by the interchange instability. We present bolometric light curves for the models and find that they reproduce observations of Class I protostars from YSOVAR, with high bursts followed by an exponential decay possibly being a signature of instability-driven accretion. Finally, we present the filling fractions of accretion and find that 90\% of the mass is accreted in a surface area fraction of 10-20\%. These simulations will be extended in future work for a broader parameter space, with their high resolution and high temporal spacing able to explore a wide range of interesting protostellar physics., Comment: Accepted to A&A

Arrays of coupled superconducting qubits are analog quantum simulators able to emulate a wide range of tight-binding models in parameter regimes that are difficult to access or adjust in natural materials. In this work, we use a superconducting qubit array to emulate a tight-binding model on the rhombic lattice, which features flat bands. Enabled by broad adjustability of the dispersion of the energy bands and of on-site disorder, we examine regimes where flat-band localization and Anderson localization compete. We observe disorder-induced localization for dispersive bands and disorder-induced delocalization for flat bands. Remarkably, we find a sudden transition between the two regimes and, in its vicinity, the semblance of quantum critical scaling., Comment: 8 pages, 5 figures, and Supplementary Information

We present a comprehensive survey on the use of annotations in information visualizations, highlighting their crucial role in improving audience understanding and engagement with visual data. Our investigation encompasses empirical studies on annotations, showcasing their impact on user engagement, interaction, comprehension, and memorability across various contexts. We also study the existing tools and techniques for creating annotations and their diverse applications, enhancing the understanding of both practical and theoretical aspects of annotations in data visualization. Additionally, we identify existing research gaps and propose potential future research directions, making our survey a valuable resource for researchers, visualization designers, and practitioners by providing a thorough understanding of the application of annotations in visualization.

This paper presents an overview of the Burer-Monteiro method (BM), a technique that has been applied to solve robot perception problems to certifiable optimality in real-time. BM is often used to solve semidefinite programming relaxations, which can be used to perform global optimization for non-convex perception problems. Specifically, BM leverages the low-rank structure of typical semidefinite programs to dramatically reduce the computational cost of performing optimization. This paper discusses BM in certifiable perception, with three main objectives: (i) to consolidate information from the literature into a unified presentation, (ii) to elucidate the role of the linear independence constraint qualification (LICQ), a concept not yet well-covered in certifiable perception literature, and (iii) to share practical considerations that are discussed among practitioners but not thoroughly covered in the literature. Our general aim is to offer a practical primer for applying BM towards certifiable perception., Comment: Accepted to 2024 Robotics: Science and Systems (RSS) Safe Autonomy Workshop

The planted random subgraph detection conjecture of Abram et al. (TCC 2023) asserts the pseudorandomness of a pair of graphs $(H, G)$, where $G$ is an Erdos-Renyi random graph on $n$ vertices, and $H$ is a random induced subgraph of $G$ on $k$ vertices. Assuming the hardness of distinguishing these two distributions (with two leaked vertices), Abram et al. construct communication-efficient, computationally secure (1) 2-party private simultaneous messages (PSM) and (2) secret sharing for forbidden graph structures. We prove the low-degree hardness of detecting planted random subgraphs all the way up to $k\leq n^{1 - \Omega(1)}$. This improves over Abram et al.'s analysis for $k \leq n^{1/2 - \Omega(1)}$. The hardness extends to $r$-uniform hypergraphs for constant $r$. Our analysis is tight in the distinguisher's degree, its advantage, and in the number of leaked vertices. Extending the constructions of Abram et al, we apply the conjecture towards (1) communication-optimal multiparty PSM protocols for random functions and (2) bit secret sharing with share size $(1 + \epsilon)\log n$ for any $\epsilon > 0$ in which arbitrary minimal coalitions of up to $r$ parties can reconstruct and secrecy holds against all unqualified subsets of up to $\ell = o(\epsilon \log n)^{1/(r-1)}$ parties.

The accuracy of recommender systems influences their trust and decision-making when using them. Providing additional information, such as visualizations, offers context that would otherwise be lacking. However, the role of visualizations in influencing trust and decisions with recommender systems is under-explored. To bridge this gap, we conducted a two-part human-subject experiment to investigate the impact of scatterplots on recommender system decisions. Our first study focuses on high-level decisions, such as selecting which recommender system to use. The second study focuses on low-level decisions, such as agreeing or disagreeing with a specific recommendation. Our results show scatterplots accompanied by higher levels of accuracy influence decisions and that participants tended to trust the recommendations more when scatterplots were accompanied by descriptive accuracy (e.g., \textit{high}, \textit{medium}, or \textit{low}) instead of numeric accuracy (e.g., \textit{90\%}). Furthermore, we observed scatterplots often assisted participants in validating their decisions. Based on the results, we believe that scatterplots and visualizations, in general, can aid in making informed decisions, validating decisions, and building trust in recommendation systems.

We explore the dynamics of qubit-state purity in the presence of transverse noise that is anisotropically distributed in the Bloch-sphere XY plane. We perform Ramsey experiments with noise injected along a fixed laboratory-frame axis and observe oscillations in the purity at twice the qubit frequency arising from the intrinsic qubit Larmor precession. We probe the oscillation dependence on the noise anisotropy, orientation, and power spectral density, using a low-frequency fluxonium qubit. Our results elucidate the impact of transverse noise anisotropy on qubit decoherence and may be useful to disentangle charge and flux noise in superconducting quantum circuits.

Context: Mutual neutralization between cations and anions play an important role in determining the charge-balance in certain astrophysical environments. However, empirical data for such reactions involving complex molecular species has been lacking due to challenges in performing experimental studies, leaving the astronomical community to rely on decades old models with large uncertainties for describing these processes in the interstellar medium. Aims: To investigate the mutual neutralization (MN) reaction, C$_{60}^+$ + C$_{60}^-$ $\rightarrow$ C$_{60}^*$ + C$_{60}$, for collisions at interstellar-like conditions. Methods: The mutual neutralization reaction between C$_{60}^+$ and C$_{60}^-$ at collision energies of 100\,meV was studied using the Double ElectroStatic Ion Ring ExpEriment, DESIREE, and its merged-beam capabilities. To aid in the interpretation of the experimental results, semi-classical modeling based on the Landau-Zener approach was performed for the studied reaction. Results: We experimentally identify a narrow range of kinetic energies for the neutral reaction products. Modeling was used to calculate the quantum state-selective reaction probabilities, absolute cross sections, and rate coefficients of these MN reactions, using the experimental results as a benchmark. The MN cross sections are compared with model results for electron attachment to C$_{60}$ and electron recombination with C$_{60}^+$. Conclusions: The present results show that it is crucial to take mutual polarization effects, the finite sizes, and the final quantum states of both molecular ions into account for reliable predictions of MN rates expected to strongly influence the charge-balance and chemistry in, e.g., dense molecular clouds., Comment: 9 pages, 4 figures

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using virtual instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.

We propose a method to realize microwave-activated CZ gates between two remote spin qubits in quantum dots using an offset-charge-sensitive transmon coupler. The qubits are longitudinally coupled to the coupler, so that the transition frequency of the coupler depends on the logical qubit states; a capacitive network model using first-quantized charge operators is developed to illustrate this. Driving the coupler transition then implements a conditional phase shift on the qubits. Two pulsing schemes are investigated: a rapid, off-resonant pulse with constant amplitude, and a pulse with envelope engineering that incorporates dynamical decoupling to mitigate charge noise. We develop non-Markovian time-domain simulations to accurately model gate performance in the presence of $1/f^\beta$ charge noise. Simulation results indicate that a CZ gate fidelity exceeding 90% is possible with realistic parameters and noise models., Comment: 19 pages, 8 figures

Let $S$ a be locally compact space with a countable base. Let $\cal Y$ be a transient symmetric Borel right process with state space $S$ and continuous strictly positive $p$--potential densities $u^p(x,y)$. Local and uniform moduli of continuity are obtained for the local times of both fully and partially rebirthed versions of $\cal Y$. A fully rebirthed version of $\cal Y$ is an extension of $\cal Y$ so that instead of terminating at the end of its lifetime it is immediately ``reborn'' with a probability measure $\mu $, on $S$. I.e., the process goes to the set $B\subset S$ with probability $\mu(B) $, after which it continues to evolve the way $\YY$ did, being reborn with probability $\mu $ each time it dies. This rebirthed version of $\cal Y$ is a recurrent Borel right process with state space $S$ and $p$-potential densities of form, \[ u^p(x,y)+h(x,y),\qquad x,y\in S,\,\, p>0, \] where $h(x,y)$ is not symmetric. The local times of the rebirthed process are given in terms of the local times of $\cal Y$ and isomorphism theorems in the spirit of Dynkin, Eisenbam and Kaspi are obtained that relate these local times to generalized chi--square processes formed by Gaussian processes with covariances $u^{q}(x,y)$ for different values of $q$. These isomorphisms allow one to obtain exact local and uniform moduli of continuity for the local times of the rebirthed process. Several explicit examples are given in which $\cal Y$ is either a modified L\'evy process or a diffusion. Analogous results are obtained for partially rebirthed versions of $\cal Y$. This is obtained by starting $\cal Y$ in $S$ and when it dies returning it to $S$ with a sub-probability measure $\Xi$. (With probability $1-|\Xi |$ it is sent to a disjoint state space $S'$, where it remains.)

Consider a stationary Poisson process $\eta$ in a $d$-dimensional hyperbolic space of constant curvature $-\varkappa$ and let the points of $\eta$ together with a fixed origin $o$ be the vertices of a graph. Connect each point $x\in\eta$ with its radial nearest neighbour, which is the hyperbolically nearest vertex to $x$ that is closer to $o$ than $x$. This construction gives rise to the hyperbolic radial spanning tree, whose geometric properties are in the focus of this paper. In particular, the degree of the origin is studied. For increasing balls around $o$ as observation windows, expectation and variance asymptotics as well as a quantitative central limit theorem for a class of edge-length functionals are derived. The results are contrasted with those for the Euclidean radial spanning tree.

We study $D=11$ supergravity solutions which are dual to one-dimensional superconformal defects in $d=3$ SCFTs. We consider defects in ABJM theory with monodromy for $U(1)^4\subset SO(8)$ global symmetry, as well as in $\mathcal{N}=2$ mABJM SCFT, which arises from the RG flow of a mass deformation of ABJM theory, with monodromy for $U(1)^3\subset SU(3)\times U(1)$ global symmetry. We show that the defects of the two SCFTs are connected by a line of bulk marginal mass deformations and argue that they are also related by bulk RG flow. In all cases we allow for the possibility of conical singularities at the location of the defect. Various physical observables of the defects are computed including the defects conformal weight and the partition function, as well as associated supersymmetric Renyi entropies., Comment: 71 pages, 6 figures

The investigation of exoplanetary habitability is integral to advancing our knowledge of extraterrestrial life potential and detailing the environmental conditions of distant worlds. In this analysis, we explore the properties of exoplanets situated with respect to circumstellar habitable zones by implementing a sophisticated filtering methodology on data from the NASA Exoplanet Archive. This research encompasses a thorough examination of 5,595 confirmed exoplanets listed in the Archive as of March 10th, 2024, systematically evaluated according to their calculated surface temperatures and stellar classifications of their host stars, taking into account the biases implicit in the methodologies used for their discovery. Our findings elucidate distinctive patterns in exoplanetary attributes, which are significantly shaped by the spectral classifications and mass of the host stars. The insights garnered from our study not only enhance the existing models for managing burgeoning exoplanetary datasets, but also lay foundational groundwork for future explorations into the dynamic relationships between exoplanets and their stellar environments.

This work presents a novel framework for numerically simulating the depressurization of tanks and pipelines containing carbon dioxide (CO2). The framework focuses on efficient solution strategies for the coupled system of fluid flow equations and thermodynamic constraints. A key contribution lies in proposing a new set of equations for phase equilibrium calculations which simplifies the traditional vapor-liquid equilibrium (VLE) calculations for two-phase CO2 mixtures. The first major novelty resides in the reduction of the conventional four-equation VLE system to a single equation, enabling efficient solution using a non-linear solver. This significantly reduces computational cost compared to traditional methods. Furthermore, a second novelty is introduced by deriving an ordinary differential equation (ODE) directly from the UV-Flash equation. This ODE can be integrated alongside the governing fluid flow equations, offering a computationally efficient approach for simulating depressurization processes., Comment: 22 pages, 15 figures, submitted to computers and fluids

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.

Version control systems are integral to software development, with GitHub emerging as a popular online platform due to its comprehensive project management tools, including issue tracking and pull requests. However, GitHub lacks a direct link between issues and commits, making it difficult for developers to understand how specific issues are resolved. Although GitHub's Insights page provides some visualization for repository data, the representation of issues and commits related data in a textual format hampers quick evaluation of issue management. This paper presents a prototype web application that generates visualizations to offer insights into issue timelines and reveals different factors related to issues. It focuses on the lifecycle of issues and depicts vital information to enhance users' understanding of development patterns in their projects. We demonstrate the effectiveness of our approach through case studies involving three open-source GitHub repositories. Furthermore, we conducted a user evaluation to validate the efficacy of our prototype in conveying crucial repository information more efficiently and rapidly.

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. In this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets., Comment: 9 pages paper + 2 page references, 8 figures, IEEE VIS 2024 paper to be published as a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG)

Generative design (GD) methods aim to automatically generate a wide variety of designs that satisfy functional or aesthetic design requirements. However, research to date generally lacks considerations of manufacturability of the generated designs. To this end, we propose a novel GD approach by using deep neural networks to encode design for manufacturing (DFM) rules, thereby modifying part designs to make them manufacturable by a given manufacturing process. Specifically, a three-step approach is proposed: first, an instance segmentation method, Mask R-CNN, is used to decompose a part design into subregions. Second, a conditional generative adversarial neural network (cGAN), Pix2Pix, transforms unmanufacturable decomposed subregions into manufacturable subregions. The transformed subregions of designs are subsequently reintegrated into a unified manufacturable design. These three steps, Mask-RCNN, Pix2Pix, and reintegration, form the basis of the proposed Manufacturable conditional GAN (McGAN) framework. Experimental results show that McGAN can transform existing unmanufacturable designs to generate their corresponding manufacturable counterparts automatically that realize the specified manufacturing rules in an efficient and robust manner. The effectiveness of McGAN is demonstrated through two-dimensional design case studies of an injection molding process.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high spatial frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM) utilizing a surface layer of Nitrogen Vacancy (NV) centers in diamond. Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower SNR data, reducing the data collection time by a factor of about 400 and permitting reconstructions of weaker and three-dimensional current sources., Comment: 17 pages, 10 figures. Includes Supplemental Information

We have measured the spontaneous and photo-induced decays of anionic gold clusters, Au$_N^-$, with sizes ranging from $N = 2$ to 13, and 15. After production in a sputter ion source, the size-selected clusters were stored in the cryogenic electrostatic ion-beam storage ring DESIREE and their neutralization decays were measured for storage times between 0.1 and 100 s. The dimer was observed to decay by electron emission in parallel to neutral atom emission at long times, analogously to the behavior of copper and silver dimers, implying a breakdown of the Born-Oppenheimer approximation. Radiative cooling is observed for all cluster sizes except for the dimer. The decay rates of clusters $N=3,6,8-13,15$ show only a single radiative cooling time. For $N=6-13$ the cooling times have a strong odd-even oscillation with an amplitude that decrease with cluster size, and with the even $N$ having the fast cooling. We compare our results with previous measurements of radiative cooling rates of the corresponding cationic gold clusters, Au$_N^+$, which also show an odd-even effect with a similar oscillation amplitude but at orders of magnitude shorter time scales, and out of phase with the anions., Comment: 12 pages, 12 figures

Kidney cancer is a global health concern, and accurate assessment of patient frailty is crucial for optimizing surgical outcomes. This paper introduces AI Age Discrepancy, a novel metric derived from machine learning analysis of preoperative abdominal CT scans, as a potential indicator of frailty and postoperative risk in kidney cancer patients. This retrospective study of 599 patients from the 2023 Kidney Tumor Segmentation (KiTS) challenge dataset found that a higher AI Age Discrepancy is significantly associated with longer hospital stays and lower overall survival rates, independent of established factors. This suggests that AI Age Discrepancy may provide valuable insights into patient frailty and could thus inform clinical decision-making in kidney cancer treatment., Comment: 10 pages, 3 figures, 2 tables

Reflected or scattered light produce artefacts in astronomical observations that can negatively impact the scientific study. Hence, automated detection of these artefacts is highly beneficial, especially with the increasing amounts of data gathered. Machine learning methods are well-suited to this problem, but currently there is a lack of annotated data to train such approaches to detect artefacts in astronomical observations. In this work, we present a dataset of images from the XMM-Newton space telescope Optical Monitoring camera showing different types of artefacts. We hand-annotated a sample of 1000 images with artefacts which we use to train automated ML methods. We further demonstrate techniques tailored for accurate detection and masking of artefacts using instance segmentation. We adopt a hybrid approach, combining knowledge from both convolutional neural networks (CNNs) and transformer-based models and use their advantages in segmentation. The presented method and dataset will advance artefact detection in astronomical observations by providing a reproducible baseline. All code and data are made available (https://github.com/ESA-Datalabs/XAMI-model and https://github.com/ESA-Datalabs/XAMI-dataset)., Comment: Accepted for oral presentation at SPAICE 2024

Qubit decoherence unavoidably degrades the fidelity of quantum logic gates. Accordingly, realizing gates that are as fast as possible is a guiding principle for qubit control, necessitating protocols for mitigating error channels that become significant as gate time is decreased. One such error channel arises from the counter-rotating component of strong, linearly polarized drives. This error channel is particularly important when gate times approach the qubit Larmor period and represents the dominant source of infidelity for sufficiently fast single-qubit gates with low-frequency qubits such as fluxonium. In this work, we develop and demonstrate two complementary protocols for mitigating this error channel. The first protocol realizes circularly polarized driving in circuit quantum electrodynamics (QED) through simultaneous charge and flux control. The second protocol -- commensurate pulses -- leverages the coherent and periodic nature of counter-rotating fields to regularize their contributions to gates, enabling single-qubit gate fidelities reliably exceeding $99.997\%$. This protocol is platform independent and requires no additional calibration overhead. This work establishes straightforward strategies for mitigating counter-rotating effects from strong drives in circuit QED and other platforms, which we expect to be helpful in the effort to realize high-fidelity control for fault-tolerant quantum computing.

The TEMPO (Transiting Exosatellites, Moons, and Planets in Orion) Survey is a proposed 30-day observational campaign using the Nancy Grace Roman Space Telescope. By providing deep, high-resolution, short-cadence infrared photometry of a dynamic star-forming region, TEMPO will investigate the demographics of exosatellites orbiting free-floating planets and brown dwarfs -- a largely unexplored discovery space. Here, we present the simulated detection yields of three populations: extrasolar moon analogs orbiting free-floating planets, exosatellites orbiting brown dwarfs, and exoplanets orbiting young stars. Additionally, we outline a comprehensive range of anticipated scientific outcomes accompanying such a survey. These science drivers include: obtaining observational constraints to test prevailing theories of moon, planet, and star formation; directly detecting widely separated exoplanets orbiting young stars; investigating the variability of young stars and brown dwarfs; constraining the low-mass end of the stellar initial mass function; constructing the distribution of dust in the Orion Nebula and mapping evolution in the near-infrared extinction law; mapping emission features that trace the shocked gas in the region; constructing a dynamical map of Orion members using proper motions; and searching for extragalactic sources and transients via deep extragalactic observations reaching a limiting magnitude of $m_{AB}=29.7$\,mag (F146 filter)., Comment: 15 pages, 6 figures, submitted to OJAp

We prove sharp weighted estimates for the non-tangential maximal function of singular integrals mapping functions from $\mathbf{R}^n$ to the half-space in $\mathbf{R}^{1+n}$ above $\mathbf{R}^n$. The proof is based on pointwise sparse domination of the adjoint singular integrals that map functions from the half-space back to the boundary. It is proved that these map $L_1$ functions in the half-space to weak $L_1$ functions on the boundary. From this a non-standard sparse domination of the singular integrals is established, where averages have been replaced by Carleson averages., Comment: Minor corrections

In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in significantly accelerating charge density prediction, yet existing approaches either lack accuracy or scalability. We propose a recipe that can achieve both. In particular, we identify three key ingredients: (1) representing the charge density with atomic and virtual orbitals (spherical fields centered at atom/virtual coordinates); (2) using expressive and learnable orbital basis sets (basis function for the spherical fields); and (3) using high-capacity equivariant neural network architecture. Our method achieves state-of-the-art accuracy while being more than an order of magnitude faster than existing methods. Furthermore, our method enables flexible efficiency-accuracy trade-offs by adjusting the model/basis sizes., Comment: 15 pages

Self-supervised contrastive learning has predominantly adopted deterministic methods, which are not suited for environments characterized by uncertainty and noise. This paper introduces a new perspective on incorporating uncertainty into contrastive learning by embedding representations within a spherical space, inspired by the von Mises-Fisher distribution (vMF). We introduce an unnormalized form of vMF and leverage the concentration parameter, kappa, as a direct, interpretable measure to quantify uncertainty explicitly. This approach not only provides a probabilistic interpretation of the embedding space but also offers a method to calibrate model confidence against varying levels of data corruption and characteristics. Our empirical results demonstrate that the estimated concentration parameter correlates strongly with the degree of unforeseen data corruption encountered at test time, enables failure analysis, and enhances existing out-of-distribution detection methods., Comment: technical report

Peer review is a popular feedback mechanism in higher education that actively engages students and provides researchers with a means to assess student engagement. However, there is little empirical support for the durability of peer review, particularly when using data predictive modeling to analyze student comments. This study uses Na\"ive Bayes modeling to analyze peer review data obtained from an undergraduate visual literacy course over five years. We expand on the research of Friedman and Rosen and Beasley et al. by focusing on the Na\"ive Bayes model of students' remarks. Our findings highlight the utility of Na\"ive Bayes modeling, particularly in the analysis of student comments based on parts of speech, where nouns emerged as the prominent category. Additionally, when examining students' comments using the visual peer review rubric, the lie factor emerged as the predominant factor. Comparing Na\"ive Bayes model to Beasley's approach, we found both help instructors map directions taken in the class, but the Na\"ive Bayes model provides a more specific outline for forecasting with a more detailed framework for identifying core topics within the course, enhancing the forecasting of educational directions. Through the application of the Holdout Method and $\mathrm{k}$-fold cross-validation with continuity correction, we have validated the model's predictive accuracy, underscoring its effectiveness in offering deep insights into peer review mechanisms. Our study findings suggest that using predictive modeling to assess student comments can provide a new way to better serve the students' classroom comments on their visual peer work. This can benefit courses by inspiring changes to course content, reinforcement of course content, modification of projects, or modifications to the rubric itself., Comment: 8 pages, 6 figures, IEEE EDUCON 2024 conference

The imminent era of error-corrected quantum computing urgently demands robust methods to characterize complex quantum states, even from limited and noisy measurements. We introduce the Quantum Attention Network (QuAN), a versatile classical AI framework leveraging the power of attention mechanisms specifically tailored to address the unique challenges of learning quantum complexity. Inspired by large language models, QuAN treats measurement snapshots as tokens while respecting their permutation invariance. Combined with a novel parameter-efficient mini-set self-attention block (MSSAB), such data structure enables QuAN to access high-order moments of the bit-string distribution and preferentially attend to less noisy snapshots. We rigorously test QuAN across three distinct quantum simulation settings: driven hard-core Bose-Hubbard model, random quantum circuits, and the toric code under coherent and incoherent noise. QuAN directly learns the growth in entanglement and state complexity from experimentally obtained computational basis measurements. In particular, it learns the growth in complexity of random circuit data upon increasing depth from noisy experimental data. Taken to a regime inaccessible by existing theory, QuAN unveils the complete phase diagram for noisy toric code data as a function of both noise types. This breakthrough highlights the transformative potential of using purposefully designed AI-driven solutions to assist quantum hardware.

Tantalum films incorporated into superconducting circuits have exhibited low surface losses, resulting in long-lived qubit states. Remaining loss pathways originate in microscopic defects which manifest as two level systems (TLS) at low temperature. These defects limit performance, so careful attention to tantalum film structures is critical for optimal use in quantum devices. In this work, we investigate the growth of tantalum using magnetron sputtering on sapphire, Si, and photoresist substrates. In the case of sapphire, we present procedures for growth of fully-oriented films with alpha-Ta [1 1 1] // Al2O3 [0 0 1] and alpha-Ta [1 -1 0] // Al2O3 [1 0 0] orientational relationships, and having residual resistivity ratios (RRR) ~ 60 for 220 nm thick films. On Si, we find a complex grain texturing with Ta [1 1 0] normal to the substrate and RRR ~ 30. We further demonstrate airbridge fabrication using Nb to nucleate alpha-Ta on photoresist surfaces. Superconducting resonators patterned from films on sapphire show TLS-limited quality factors of 1.4 +/- 0.3 x 10^6 at 10 mK. Structural characterization using scanning electron microscopy, X-ray diffraction, low temperature transport, secondary ion mass spectrometry, and transmission electron microscopy reveal the dependence of residual impurities and screw dislocation density on processing conditions. The results provide practical insights for fabrication of high-performing technological devices including qubit arrays, and guide future work on crystallographically deterministic qubit fabrication.

We study type IIB supergravity solutions that are dual to two-dimensional superconformal defects in $d=4$ SCFTs which preserve $\mathcal{N}=(0,2)$ supersymmetry. We consider solutions dual to defects in $\mathcal{N}=4$ SYM theory that have non-trivial monodromy for $U(1)^3\subset SO(6)$ global symmetry and we also allow for the possibility of conical singularities. In addition, we consider the addition of fermionic and bosonic mass terms that have non trivial dependence on the spatial directions transverse to the defect, while preserving the superconformal symmetry of the defect. We compute various physical quantities including the central charges of the defect expressed as a function of the monodromy, the on-shell action as well as associated supersymmetric Renyi entropies. Analogous computations are carried out for superconformal defects in the $\mathcal{N}=1$, $d=4$ Leigh-Strassler SCFT. We also show that the defects of the two SCFTs are connected by a line of bulk marginal mass deformations and argue that they are also related by bulk RG flow., Comment: 93 pages, 8 figures. References and figure added. Various discussions refined, including on the existence of defect solutions

Identifying transition states -- saddle points on the potential energy surface connecting reactant and product minima -- is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train an equivariant neural network potential, NewtonNet, on an ab initio dataset of thousands of organic reactions from which we derive the analytical Hessians from the fully differentiable machine learning (ML) model. By reducing the computational cost by several orders of magnitude relative to the Density Functional Theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We have implemented our ML Hessian algorithm in Sella, an open source software package designed to optimize atomic systems to find saddle point structures, in order to compare transition state optimization against quasi-Newton Hessian updates using DFT or the ML model. We show that the full ML Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2--3$\times$ compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow.

Superconducting quantum processors are a compelling platform for analog quantum simulation due to the precision control, fast operation, and site-resolved readout inherent to the hardware. Arrays of coupled superconducting qubits natively emulate the dynamics of interacting particles according to the Bose-Hubbard model. However, many interesting condensed-matter phenomena emerge only in the presence of electromagnetic fields. Here, we emulate the dynamics of charged particles in an electromagnetic field using a superconducting quantum simulator. We realize a broadly adjustable synthetic magnetic vector potential by applying continuous modulation tones to all qubits. We verify that the synthetic vector potential obeys requisite properties of electromagnetism: a spatially-varying vector potential breaks time-reversal symmetry and generates a gauge-invariant synthetic magnetic field, and a temporally-varying vector potential produces a synthetic electric field. We demonstrate that the Hall effect--the transverse deflection of a charged particle propagating in an electromagnetic field--exists in the presence of the synthetic electromagnetic field., Comment: 9 pages, 5 figures, and Supplementary Information

Stellar winds from massive stars may be significant sources of cosmic rays (CRs). To investigate this connection, we report a detailed study of gamma-ray emission near the young Milky Way star cluster ($\approx$ 0.5 Myr old) in the star-forming region RCW 38 and compare this emission to its stellar wind properties and diffuse X-ray emission. Using 15 years of Fermi-LAT data in the 0.2 $-$ 300 GeV band, we find a significant ($ \sigma > 22$) detection coincident with the star cluster, producing a total $\gamma$-ray luminosity (extrapolated over 0.1 $-$ 500 GeV) of $L_{\gamma} = (2.66\pm 0.92) \times 10^{34}$ erg s$^{-1}$ adopting a power-law spectral model ($\Gamma = 2.34\pm0.04$). Using an empirical relationship and Starburst99, we estimate the total wind power to be $8 \times 10^{36}$ erg s$^{-1}$, corresponding to a CR acceleration efficiency of $\eta_{\rm CR} \simeq 0.4$ for an assumed diffusion coefficient consistent with $D = 10^{28}$ cm$^{2}$ s$^{-1}$. Alternatively, a lower acceleration efficiency of 0.1 can produce this $L_{\gamma}$ if the diffusion coefficient is smaller, $D\simeq 2.5\times10^{27}\,{\rm cm^2\,\,s^{-1}}$. Additionally, we analyze Chandra X-ray data from the region and compare the hot-gas pressure to the CR pressure. We find the former is four orders of magnitude greater, suggesting that the CR pressure is not dynamically important relative to stellar winds. As RCW 38 is too young for supernovae to have occurred, the high CR acceleration efficiency in RCW 38 demonstrates that stellar winds may be an important source of Galactic CRs., Comment: Accepted for publication in ApJ

We prove a Weitzenb\"ock identity for general pairs of constant coefficient homogeneous first order partial differential operators, and deduce from it sufficient algebraic conditions for coerciveness and Morrey estimates under the natural 1/2 boundary conditions. Our proof of the $W^{1,2}$ elliptic estimate relies on the Aronszajn-Necas-Smith coercive estimate. For generalized strongly pseudoconvex domains, we improve the Morrey estimate to a weighted $W^{1,2}$ square function estimate, using a generalized Cauchy-Pompieu reproducing formula and the T1 theorem for singular integrals. We use Van Schaftingen's notion of cocanceling to study the generalized Levi forms appearing.

We prove a Kato square root estimate with anisotropically degenerate matrix coefficients. We do so by doing the harmonic analysis using an auxiliary Riemannian metric adapted to the operator. We also derive $L^2$-solvability estimates for boundary value problems for divergence form elliptic equations with matrix degenerate coefficients. Main tools are chain rules and Piola transformations for fields in matrix weighted $L^2$ spaces, under $W^{1,1}$ homeomorphism., Comment: 27 pages, 7 figures

Recently, we demonstrated self-consistent formation of strongly-magnetized quasar accretion disks (QADs) from cosmological radiation-magnetohydrodynamic-thermochemical galaxy-star formation simulations, including the full STARFORGE physics shown previously to produce a reasonable IMF under typical ISM conditions. Here we study star formation and the stellar IMF in QADs, on scales from 100 au to 10 pc from the SMBH. We show it is critical to include physics often previously neglected, including magnetic fields, radiation, and (proto)stellar feedback. Closer to the SMBH, star formation is suppressed, but the (rare) stars that do form exhibit top-heavy IMFs. Stars can form only in special locations (e.g. magnetic field switches) in the outer QAD. Protostars accrete their natal cores rapidly but then dynamically decouple from the gas and wander, ceasing accretion on timescales ~100 yr. Their jets control initial core accretion, but the ejecta are swept up into the larger-scale QAD flow without much dynamical effect. The strong tidal environment strongly suppresses common-core multiplicity. The IMF shape depends sensitively on un-resolved dynamics of protostellar disks (PSDs), as the global dynamical times can become incredibly short ($\ll$ yr) and tidal fields are incredibly strong, so whether PSDs can efficiently transport angular momentum or fragment catastrophically at $\lesssim 10$ au scales requires novel PSD simulations to properly address. Most analytic IMF models and analogies with planet formation in PSDs fail qualitatively to explain the simulation IMFs, though we discuss a couple of viable models., Comment: 39 pages, 23 figures, replaced with version accepted to the Open Journal of Astrophysics. Additional images and movies from the simulations available at http://www.tapir.caltech.edu/~phopkins/Site/animations/Movies_zoom.html

Radiative lifetimes of three elements of the nitrogen group have been experimentally investigated at the Double ElectroStatic Ion Ring Experiment (DESIREE) facility at Stockholm University. The experiments were performed through selective laser photodetachment of excited states of P$^-$, As$^-$ and Sb$^-$ ions stored in a cryogenic storage ring. The experimental results were compared with theoretically predicted lifetimes, yielding a mixture of very good agreements in some cases and large discrepancies in others. These results are part of our efforts to map out the lifetimes of all excited states in negative ions. This data can be used to benchmark atomic theories, in particularly with respect to the degree of electron correlation that is incorporated in various theoretical models.

High-precision measurements of the electron affinities (EA) of the three stable isotopes of silicon, $^{28}$Si, $^{29}$Si and $^{30}$Si, have been performed at the cryogenic electrostatic ion-beam storage ring DESIREE. The quantum states of the ions were manipulated using laser depletion, and the ions were photodetached by laser photodetachment threshold spectroscopy. These EA values are the first reported for $^{29}$Si$^-$ and $^{30}$Si$^-$ and provide a reduced uncertainty for $^{28}$Si$^-$. The resulting EAs are $EA(^{28}$Si$) = 1.38952201(17)$ eV, $EA(^{29}$Si$) = 1.38952172(12)$ eV and $EA(^{29}$Si$) = 1.38952078(12)$ eV, with the corresponding isotope shifts $IS(^{29-28}$Si$) = 0.29(16)$ micro eV and $IS(^{30-28}$Si$) = 1.23(16) $ micro eV. In addition to these measurements, the resolution and signal-to-background level was sufficient to reveal the hyperfine structure splitting in the $^{29}$Si$^-$ isotope, which we report to be $1.8(4) micro eV.

Bayesian Optimization (BO) is a foundational strategy in the field of engineering design optimization for efficiently handling black-box functions with many constraints and expensive evaluations. This paper introduces a fast and accurate BO framework that leverages Pre-trained Transformers for Bayesian Optimization (PFN4sBO) to address constrained optimization problems in engineering. Unlike traditional BO methods that rely heavily on Gaussian Processes (GPs), our approach utilizes Prior-data Fitted Networks (PFNs), a type of pre-trained transformer, to infer constraints and optimal solutions without requiring any iterative retraining. We demonstrate the effectiveness of PFN-based BO through a comprehensive benchmark consisting of fifteen test problems, encompassing synthetic, structural, and engineering design challenges. Our findings reveal that PFN-based BO significantly outperforms Constrained Expected Improvement and Penalty-based GP methods by an order of magnitude in speed while also outperforming them in accuracy in identifying feasible, optimal solutions. This work showcases the potential of integrating machine learning with optimization techniques in solving complex engineering challenges, heralding a significant leap forward for optimization methodologies, opening up the path to using PFN-based BO to solve other challenging problems, such as enabling user-guided interactive BO, adaptive experiment design, or multi-objective design optimization. Additionally, we establish a benchmark for evaluating BO algorithms in engineering design, offering a robust platform for future research and development in the field. This benchmark framework for evaluating new BO algorithms in engineering design will be published at https://github.com/rosenyu304/BOEngineeringBenchmark., Comment: Under review of Structural and Multidisciplinary Optimization

Instruction tuning is an important step in making language models useful for direct user interaction. However, many legal tasks remain out of reach for most open LLMs and there do not yet exist any large scale instruction datasets for the domain. This critically limits research in this application area. In this work, we curate LawInstruct, a large legal instruction dataset, covering 17 jurisdictions, 24 languages and a total of 12M examples. We present evidence that domain-specific pretraining and instruction tuning improve performance on LegalBench, including improving Flan-T5 XL by 8 points or 16\% over the baseline. However, the effect does not generalize across all tasks, training regimes, model sizes, and other factors. LawInstruct is a resource for accelerating the development of models with stronger information processing and decision making capabilities in the legal domain.