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Extreme ultraviolet (XUV) light sources allow for the probing of bound electron dynamics on attosecond scales, interrogation of high-energy-density matter, and access to novel regimes of strong-field quantum electrodynamics. Despite the importance of these applications, coherent XUV sources remain relatively rare, and those that do exist are limited in their peak intensity and spatio-polarization structure. Here, we demonstrate that photon acceleration of an optical vector vortex pulse in the moving density gradient of an electron beam-driven plasma wave can produce a high-intensity, tunable-wavelength XUV pulse with the same vector vortex structure as the original pulse. Quasi-3D, boosted-frame particle-in-cell simulations show the transition of optical vector vortex pulses with 800-nm wavelengths and intensities below $10^{18}$ W/cm$^2$ to XUV vector vortex pulses with 36-nm wavelengths and intensities exceeding $10^{20}$ W/cm$^2$ over a distance of 1.2 cm. The XUV pulses have sub-femtosecond durations and nearly flat phase fronts. The production of such high-quality, high-intensity XUV vector vortex pulses could expand the utility of XUV light as a diagnostic and driver of novel light-matter interactions., Comment: 25 pages, 5 figures, planned submission to scientific journal

Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable approximation error. Recent work accounts for this error in the form of computational uncertainty, which enables -- at the cost of quadratic complexity -- an explicit tradeoff between computation and precision. Here we extend this development to model selection, which requires significant enhancements to the existing approach, including linear-time scaling in the size of the dataset. We propose a novel training loss for hyperparameter optimization and demonstrate empirically that the resulting method can outperform SGPR, CGGP and SVGP, state-of-the-art methods for GP model selection, on medium to large-scale datasets. Our experiments show that model selection for computation-aware GPs trained on 1.8 million data points can be done within a few hours on a single GPU. As a result of this work, Gaussian processes can be trained on large-scale datasets without significantly compromising their ability to quantify uncertainty -- a fundamental prerequisite for optimal decision-making., Comment: Advances in Neural Information Processing Systems (NeurIPS 2024)

The High Energy Modular Array (HEMA) is one of three instruments that compose the STROBE-X mission concept. The HEMA is a large-area, high-throughput non-imaging pointed instrument based on the Large Area Detector developed as part of the LOFT mission concept. It is designed for spectral timing measurements of a broad range of sources and provides a transformative increase in sensitivity to X-rays in the energy range of 2--30 keV compared to previous instruments, with an effective area of 3.4 m$^{2}$ at 8.5 keV and an energy resolution of better than 300 eV at 6 keV in its nominal field of regard., Comment: 16 pages, 10 figures

Generative AI has shown its values for many software engineering tasks. Still in its infancy, large language model (LLM)-based proof generation lags behind LLM-based code generation. In this paper, we present AutoVerus. AutoVerus uses LLM to automatically generate correctness proof for Rust code. AutoVerus is designed to match the unique features of Verus, a verification tool that can prove the correctness of Rust code using proofs and specifications also written in Rust. AutoVerus consists of a network of LLM agents that are crafted and orchestrated to mimic human experts' three phases of proof construction: preliminary proof generation, proof refinement guided by generic tips, and proof debugging guided by verification errors. To thoroughly evaluate AutoVerus and help foster future research in this direction, we have built a benchmark suite of 150 non-trivial proof tasks, based on existing code-generation benchmarks and verification benchmarks. Our evaluation shows that AutoVerus can automatically generate correct proof for more than 90% of them, with more than half of them tackled in less than 30 seconds or 3 LLM calls.

Ultralight scalar fields can experience runaway `superradiant' amplification near spinning black holes, resulting in a macroscopic `axion cloud' which slowly dissipates via continuous monochromatic gravitational waves. For a particular range of boson masses, $\mathcal{O}(10^{-11}$ -- $10^{-10})$ eV, an axion cloud will radiate in the $10$ -- $100$ kHz band of the Levitated Sensor Detector (LSD). Using fiducial models of the mass, spin, and age distributions of stellar-origin black holes, we simulate the present-day Milky Way population of these hypothetical objects. As a first step towards assessing the LSD's sensitivity to the resultant ensemble of GW signals, we compute the corresponding signal-to-noise ratios which build up over a nominal integration time of $10^{7}$ s, assuming the projected sensitivity of the $1$-m LSD prototype currently under construction, as well as for future $10$-m and $100$-m concepts. For a $100$-m cryogenic instrument, hundreds of resolvable signals could be expected if the boson mass $\mu$ is around $3\times10^{-11}$ eV, and this number diminishes with increasing $\mu$ up to $\approx 5.5\times10^{-11}$ eV. The much larger population of unresolved sources will produce a confusion foreground which could be detectable by a $10$-m instrument if $\mu \in (3-4.5)\times10^{-11}$ eV, or by a $100$-m instrument if $\mu \in (3-6)\times10^{-11}$ eV.

Using Mars Atmosphere and Volatile EvolutioN Magnetometer observations, we report the first statistical study of ultra-low frequency (ULF) waves at the Martian foreshock. The analyzed foreshock ULF wave events are observed in the 0.008-0.086 Hz frequency range, with nearly circular and elliptical left-handed polarization in the spacecraft reference frame. These waves are propagated quasi-parallel to the ambient magnetic field, with a moderate wave amplitude. All these properties are consistent with fast magnetosonic waves, most likely generated through the ion-ion right-hand resonant instability. In addition, our results suggest that the associated resonant backstreaming protons' velocities parallel to the mean magnetic field in the solar wind reference frame is $1.33 \pm 0.40$ times the solar wind velocity. The similarity between our results and previous reports at other foreshocks may indicate the presence of a common acceleration process acting in planetary bow shocks and that is responsible for this particular backstreaming population.

Elliptical slice sampling, when adapted to linearly truncated multivariate normal distributions, is a rejection-free Markov chain Monte Carlo method. At its core, it requires analytically constructing an ellipse-polytope intersection. The main novelty of this paper is an algorithm that computes this intersection in $\mathcal{O}(m \log m)$ time, where $m$ is the number of linear inequality constraints representing the polytope. We show that an implementation based on this algorithm enhances numerical stability, speeds up running time, and is easy to parallelize for launching multiple Markov chains., Comment: 13 pages

High-dimensional Bayesian optimization (BO) tasks such as molecular design often require 10,000 function evaluations before obtaining meaningful results. While methods like sparse variational Gaussian processes (SVGPs) reduce computational requirements in these settings, the underlying approximations result in suboptimal data acquisitions that slow the progress of optimization. In this paper we modify SVGPs to better align with the goals of BO: targeting informed data acquisition rather than global posterior fidelity. Using the framework of utility-calibrated variational inference, we unify GP approximation and data acquisition into a joint optimization problem, thereby ensuring optimal decisions under a limited computational budget. Our approach can be used with any decision-theoretic acquisition function and is compatible with trust region methods like TuRBO. We derive efficient joint objectives for the expected improvement and knowledge gradient acquisition functions in both the standard and batch BO settings. Our approach outperforms standard SVGPs on high-dimensional benchmark tasks in control and molecular design.

Zeroth-order optimization (ZO) is a memory-efficient strategy for fine-tuning Large Language Models using only forward passes. However, the application of ZO fine-tuning in memory-constrained settings such as mobile phones and laptops is still challenging since full precision forward passes are infeasible. In this study, we address this limitation by integrating sparsity and quantization into ZO fine-tuning of LLMs. Specifically, we investigate the feasibility of fine-tuning an extremely small subset of LLM parameters using ZO. This approach allows the majority of un-tuned parameters to be quantized to accommodate the constraint of limited device memory. Our findings reveal that the pre-training process can identify a set of "sensitive parameters" that can guide the ZO fine-tuning of LLMs on downstream tasks. Our results demonstrate that fine-tuning 0.1% sensitive parameters in the LLM with ZO can outperform the full ZO fine-tuning performance, while offering wall-clock time speedup. Additionally, we show that ZO fine-tuning targeting these 0.1% sensitive parameters, combined with 4 bit quantization, enables efficient ZO fine-tuning of an Llama2-7B model on a GPU device with less than 8 GiB of memory and notably reduced latency.

Stochastic natural gradient variational inference (NGVI) is a popular posterior inference method with applications in various probabilistic models. Despite its wide usage, little is known about the non-asymptotic convergence rate in the \emph{stochastic} setting. We aim to lessen this gap and provide a better understanding. For conjugate likelihoods, we prove the first $\mathcal{O}(\frac{1}{T})$ non-asymptotic convergence rate of stochastic NGVI. The complexity is no worse than stochastic gradient descent (\aka black-box variational inference) and the rate likely has better constant dependency that leads to faster convergence in practice. For non-conjugate likelihoods, we show that stochastic NGVI with the canonical parameterization implicitly optimizes a non-convex objective. Thus, a global convergence rate of $\mathcal{O}(\frac{1}{T})$ is unlikely without some significant new understanding of optimizing the ELBO using natural gradients., Comment: ICML 2024

Optimization objectives in the form of a sum of intractable expectations are rising in importance (e.g., diffusion models, variational autoencoders, and many more), a setting also known as "finite sum with infinite data." For these problems, a popular strategy is to employ SGD with doubly stochastic gradients (doubly SGD): the expectations are estimated using the gradient estimator of each component, while the sum is estimated by subsampling over these estimators. Despite its popularity, little is known about the convergence properties of doubly SGD, except under strong assumptions such as bounded variance. In this work, we establish the convergence of doubly SGD with independent minibatching and random reshuffling under general conditions, which encompasses dependent component gradient estimators. In particular, for dependent estimators, our analysis allows fined-grained analysis of the effect correlations. As a result, under a per-iteration computational budget of $b \times m$, where $b$ is the minibatch size and $m$ is the number of Monte Carlo samples, our analysis suggests where one should invest most of the budget in general. Furthermore, we prove that random reshuffling (RR) improves the complexity dependence on the subsampling noise., Comment: Accepted to ICML'24

Machine learning offers a largely unexplored avenue for improving noisy disordered devices in physics using automated algorithms. Through simulations that include disorder in physical devices, particularly quantum devices, there is potential to learn about disordered landscapes and subsequently tune devices based on those insights. In this work, we introduce a novel methodology that employs machine learning, specifically convolutional neural networks (CNNs), to discern the disorder landscape in the parameters of the disordered extended Hubbard model underlying the semiconductor quantum dot spin qubit architectures. This technique takes advantage of experimentally obtainable charge stability diagrams from neighboring quantum dot pairs, enabling the CNN to accurately identify disorder in each parameter of the extended Hubbard model. Remarkably, our CNN can process site-specific disorder in Hubbard parameters, including variations in hopping constants, on-site potentials (gate voltages), and both intra-site and inter-site Coulomb terms. This advancement facilitates the prediction of spatially dependent disorder across all parameters simultaneously with high accuracy ($R^2>0.994$) and fewer parameter constraints, marking a significant improvement over previous methods that were focused only on analyzing on-site potentials at low coupling. Furthermore, our approach allows for the tuning of five or more quantum dots at a time, effectively addressing the often-overlooked issue of crosstalk. Not only does our method streamline the tuning process, potentially enabling fully automated adjustments, but it also introduces a "no trust" verification method to rigorously validate the neural network's predictions. Ultimately, this work aims to lay the groundwork for generalizing our method to tackle a broad spectrum of physical problems.

Multiple dissipative self-assembly protocols designed to create novel structures or to reduce kinetic traps have recently emerged. Specifically, temporal oscillations of particle interactions have been shown effective at both aims, but investigations thus far have focused on systems of simple colloids or their binary mixtures. In this work, we expand our understanding of the effect of temporally oscillating interactions to a two-dimensional coarse-grained viral capsid-like model that undergoes a self-limited assembly. This model includes multiple intrinsic relaxation times due to the internal structure of the capsid subunits and, under certain interaction regimes, proceeds via a two-step nucleation mechanism. We find that oscillations much faster than the local intrinsic relaxation times can be described via a time averaged inter-particle potential across a wide range of interaction strengths, while oscillations much slower than these relaxation times result in structures that adapt to the attraction strength of the current half-cycle. Interestingly, oscillation periods similar to these relaxation times shift the interaction window over which orderly assembly occurs by enabling error correction during the half-cycles with weaker attractions. Our results provide fundamental insights to non-equilibrium self-assembly on temporally variant energy landscapes., Comment: Resubmitted version. 7 main text figures, 19 pages long. 10 page SI included with 4 SI figures

For the first time, we demonstrate the application of an inverse Compton scattering X-ray Source, driven by a laser-plasma accelerator, to image an additively manufactured component. X-rays with a mean energy of 380 keV were produced and used to image an additively manufactured part made of an Inconel (Nickel 718) alloy. Because inverse Compton scattering driven by laser-plasma acceleration produces high-energy X-rays while maintaining a focal spot size on the order of a micron, the source can provide several benefits over conventional X-ray production methods, particularly when imaging superalloy parts, with the potential to revolutionise what can be inspected.

The expectation maximization (EM) algorithm is a widespread method for empirical Bayesian inference, but its expectation step (E-step) is often intractable. Employing a stochastic approximation scheme with Markov chain Monte Carlo (MCMC) can circumvent this issue, resulting in an algorithm known as MCMC-SAEM. While theoretical guarantees for MCMC-SAEM have previously been established, these results are restricted to the case where asymptotically unbiased MCMC algorithms are used. In practice, MCMC-SAEM is often run with asymptotically biased MCMC, for which the consequences are theoretically less understood. In this work, we fill this gap by analyzing the asymptotics and non-asymptotics of SAEM with biased MCMC steps, particularly the effect of bias. We also provide numerical experiments comparing the Metropolis-adjusted Langevin algorithm (MALA), which is asymptotically unbiased, and the unadjusted Langevin algorithm (ULA), which is asymptotically biased, on synthetic and real datasets. Experimental results show that ULA is more stable with respect to the choice of Langevin stepsize and can sometimes result in faster convergence., Comment: Accepted to AISTATS'24

Objective. Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limited by some degree of manual user-entry requirements. This study presents a novel artificial intelligence (AI) tool called SpinePose that automatically predicts spinopelvic parameters with high accuracy without the need for manual entry. Methods. SpinePose was trained and validated on 761 sagittal whole-spine X-rays to predict sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), sacral slope (SS), lumbar lordosis (LL), T1-pelvic angle (T1PA), and L1-pelvic angle (L1PA). A separate test set of 40 X-rays was labeled by 4 reviewers, including fellowship-trained spine surgeons and a fellowship-trained radiologist with neuroradiology subspecialty certification. Median errors relative to the most senior reviewer were calculated to determine model accuracy on test images. Intraclass correlation coefficients (ICC) were used to assess inter-rater reliability. Results. SpinePose exhibited the following median (interquartile range) parameter errors: SVA: 2.2(2.3)mm, p=0.93; PT: 1.3(1.2){\deg}, p=0.48; SS: 1.7(2.2){\deg}, p=0.64; PI: 2.2(2.1){\deg}, p=0.24; LL: 2.6(4.0){\deg}, p=0.89; T1PA: 1.1(0.9){\deg}, p=0.42; and L1PA: 1.4(1.6){\deg}, p=0.49. Model predictions also exhibited excellent reliability at all parameters (ICC: 0.91-1.0). Conclusions. SpinePose accurately predicted spinopelvic parameters with excellent reliability comparable to fellowship-trained spine surgeons and neuroradiologists. Utilization of predictive AI tools in spinal imaging can substantially aid in patient selection and surgical planning., Comment: 10 pages, 5 figures, to appear in Journal of Neurosurgery: Spine

Variational families with full-rank covariance approximations are known not to work well in black-box variational inference (BBVI), both empirically and theoretically. In fact, recent computational complexity results for BBVI have established that full-rank variational families scale poorly with the dimensionality of the problem compared to e.g. mean-field families. This is particularly critical to hierarchical Bayesian models with local variables; their dimensionality increases with the size of the datasets. Consequently, one gets an iteration complexity with an explicit (\mathcal{O}(N^2)) dependence on the dataset size (N). In this paper, we explore a theoretical middle ground between mean-field variational families and full-rank families: structured variational families. We rigorously prove that certain scale matrix structures can achieve a better iteration complexity of (\mathcal{O}\left(N\right)), implying better scaling with respect to (N). We empirically verify our theoretical results on large-scale hierarchical models., Comment: Accepted to ICML'24

This paper studies the reduction by symmetry of variational problems on Lie groups and Riemannian homogeneous spaces. We derive the reduced equations of motion in the case of Lie groups endowed with a left-invariant metric, and on Lie groups that admits a bi-invariant metric. We repeated this analysis for Riemannian homogeneous spaces, where we derive the reduced equations by considering an alternative variational problem written in terms of a connection on the horizontal bundle of the underlying Lie group. We study also the case that the underlying Lie group admits a bi-invariant metric, and consider the special case that the homogeneous space is in fact a Riemannian symmetric space. These ideas are applied to geodesics for a rigid body on $SO(3)$ to derive geodesic equations on the dual of its Lie algebra (a vector space), the heavy-top in $SE(3)$ to derive reduced equations of motion on the unit sphere $S^2$, geodesics on $S^2$ as a Riemannian symmetric space endowed with a bi-invariant metric and optimal control problems for applications to robotic manipulators., Comment: 33 pages. arXiv admin note: text overlap with arXiv:2207.13574

Cluster and void formations are key processes in the dynamics of particle-laden turbulence. In this work, we assess the performance of various neural network models for synthesizing preferential concentration fields of particles in turbulence. A database of direct numerical simulations of homogeneous isotropic two-dimensional turbulence with one-way coupled inertial point particles, is used to train the models using vorticity as the input to predict the particle number density fields. We compare autoencoder, U--Net, generative adversarial network (GAN), and diffusion model approaches, and assess the statistical properties of the generated particle number density fields. We find that the GANs are superior in predicting clusters and voids, and therefore result in the best performance. Additionally, we explore a concept of ``supersampling", where neural networks can be used to predict full particle data using only the information of few particles, which yields promising perspectives for reducing the computational cost of expensive DNS computations by avoiding the tracking of millions of particles. We also explore the inverse problem of synthesizing the enstrophy fields using the particle number density distribution as the input at different Stokes numbers. Hence, our study also indicates the potential use of neural networks to predict turbulent flow statistics using experimental measurements of inertial particles.

Cognitive modeling commonly relies on asking participants to complete a battery of varied tests in order to estimate attention, working memory, and other latent variables. In many cases, these tests result in highly variable observation models. A near-ubiquitous approach is to repeat many observations for each test independently, resulting in a distribution over the outcomes from each test given to each subject. Latent variable models (LVMs), if employed, are only added after data collection. In this paper, we explore the usage of LVMs to enable learning across many correlated variables simultaneously. We extend LVMs to the setting where observed data for each subject are a series of observations from many different distributions, rather than simple vectors to be reconstructed. By embedding test battery results for individuals in a latent space that is trained jointly across a population, we can leverage correlations both between disparate test data for a single participant and between multiple participants. We then propose an active learning framework that leverages this model to conduct more efficient cognitive test batteries. We validate our approach by demonstrating with real-time data acquisition that it performs comparably to conventional methods in making item-level predictions with fewer test items., Comment: 11 pages, 6 figures

The inherently low signal-to-noise ratio of NMR and MRI is now being addressed by hyperpolarization methods. For example, iridium-based catalysts that reversibly bind both parahydrogen and ligands in solution can hyperpolarize protons (SABRE) or heteronuclei (X-SABRE) on a wide variety of ligands, using a complex interplay of spin dynamics and chemical exchange processes, with common signal enhancements between $10^3-10^4$. This does not approach obvious theoretical limits, and further enhancement would be valuable in many applications (such as imaging mM concentration species in vivo). Most SABRE/X-SABRE implementations require far lower fields (${\mu}T-mT$) than standard magnetic resonance (>1T), and this gives an additional degree of freedom: the ability to fully modulate fields in three dimensions. However, this has been underexplored because the standard simplifying theoretical assumptions in magnetic resonance need to be revisited. Here we take a different approach, an evolutionary strategy algorithm for numerical optimization, Multi-Axis Computer-aided HEteronuclear Transfer Enhancement for SABRE (MACHETE-SABRE). We find nonintuitive but highly efficient multi-axial pulse sequences which experimentally can produce a 10-fold improvement in polarization over continuous excitation. This approach optimizes polarization differently than traditional methods, thus gaining extra efficiency.

Structured illumination can reject out-of-focus signal from a sample, enabling high-speed and high-contrast imaging over large areas with widefield detection optics. Currently, this optical-sectioning technique is limited by image reconstruction artefacts and the need for sequential imaging of multiple colour channels. We combine multicolour interferometric pattern generation with machine-learning processing, permitting high-contrast, real-time reconstruction of image data. The method is insensitive to background noise and unevenly phase-stepped illumination patterns. We validate the method in silico and demonstrate its application on diverse specimens, ranging from fixed and live biological cells to synthetic biosystems, imaging at up to 37 Hz across a 44 x 44 $\mu m^2$ field of view.

In this work, we study the reduction by a Lie group of symmetries of variational collision avoidance probelms of multiple agents evolving on a Riemannian manifold and derive necessary conditions for the reduced extremals. The problem consists of finding non-intersecting trajectories of a given number of agents, among a set of admissible curves, to reach a specified configuration, based on minimizing an energy functional that depends on the velocity, covariant acceleration and an artificial potential function used to prevent collision among the agents., Comment: 7 pages, 8th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control, LHMNC 2024. arXiv admin note: text overlap with arXiv:2310.18057, arXiv:2207.13574

This paper studies sufficient conditions in a variational obstacle avoidance problem on complete Riemannian manifolds. That is, we minimize an action functional, among a set of admissible curves, which depends on an artificial potential function used to avoid obstacles. We provide necessary and sufficient conditions under which the resulting critical points, the so-called modified Riemannian cubics, are local minimizers. We then study the theory of reduction by symmetries of sufficient conditions for optimality in variational obstacle avoidance problems on Lie groups endowed with a left-invariant metric. This amounts to left-translating the Bi-Jacobi fields described to the Lie algebra, and studying the corresponding bi-conjugate points. New conditions are provided in terms of the invertibility of a certain matrix., Comment: 9 pages, 8th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control. arXiv admin note: text overlap with arXiv:2207.13574

Training and inference in Gaussian processes (GPs) require solving linear systems with $n\times n$ kernel matrices. To address the prohibitive $\mathcal{O}(n^3)$ time complexity, recent work has employed fast iterative methods, like conjugate gradients (CG). However, as datasets increase in magnitude, the kernel matrices become increasingly ill-conditioned and still require $\mathcal{O}(n^2)$ space without partitioning. Thus, while CG increases the size of datasets GPs can be trained on, modern datasets reach scales beyond its applicability. In this work, we propose an iterative method which only accesses subblocks of the kernel matrix, effectively enabling mini-batching. Our algorithm, based on alternating projection, has $\mathcal{O}(n)$ per-iteration time and space complexity, solving many of the practical challenges of scaling GPs to very large datasets. Theoretically, we prove the method enjoys linear convergence. Empirically, we demonstrate its fast convergence in practice and robustness to ill-conditioning. On large-scale benchmark datasets with up to four million data points, our approach accelerates GP training and inference by speed-up factors up to $27\times$ and $72 \times$, respectively, compared to CG., Comment: AISTATS 2024

Laser wakefield accelerators (LWFAs) have electric fields that are orders of magnitude larger than those of conventional accelerators, promising an attractive, small-scale alternative for next-generation light sources and lepton colliders. The maximum energy gain in a single-stage LWFA is limited by dephasing, which occurs when the trapped particles outrun the accelerating phase of the wakefield. Here, we demonstrate that a single space-time structured laser pulse can be used for ionization injection and electron acceleration over many dephasing lengths in the bubble regime. Simulations of a dephasingless laser wakefield accelerator driven by a 6.2-J laser pulse show 25 pC of injected charge accelerated over 20 dephasing lengths (1.3 cm) to a maximum energy of 2.1 GeV. The space-time structured laser pulse features an ultrashort, programmable-trajectory focus. Accelerating the focus, reducing the focused spot-size variation, and mitigating unwanted self-focusing stabilize the electron acceleration, which improves beam quality and leads to projected energy gains of 125 GeV in a single, sub-meter stage driven by a 500-J pulse., Comment: 18 pages, 4 figures

The Interplanetary Network (IPN) is a detection, localization and alert system that utilizes the arrival time of transient signals in gamma-ray detectors on spacecraft separated by planetary baselines to geometrically locate the origin of these transients. Due to the changing astrophysical landscape and the new emphasis on time domain and multi-messenger astrophysics (TDAMM) from the Pathways to Discovery in Astronomy and Astrophysics for the 2020s, this Gamma-ray Transient Network Science Analysis Group was tasked to understand the role of the IPN and high-energy monitors in this new era. The charge includes describing the science made possible with these facilities, tracing the corresponding requirements and capabilities, and highlighting where improved operations of existing instruments and the IPN would enhance TDAMM science. While this study considers the full multiwavelength and multimessenger context, the findings are specific to space-based high-energy monitors. These facilities are important both for full characterization of these transients as well as facilitating follow-up observations through discovery and localization. The full document reports a brief history of this field, followed by our detailed analyses and findings in some 68 pages, providing a holistic overview of the role of the IPN and high-energy monitors in the coming decades., Comment: Terms of Reference and additional information on the Science Analysis Group are available at https://pcos.gsfc.nasa.gov/sags/gtn-sag.php

Advanced tensor network numerical methods are used to explore the fidelity of repeated SWAP operations on a system comprising 20-100 quantum dot spin qubits in the presence of valley leakage and electrostatic crosstalk. The fidelity of SWAP gates is largely unaffected by Zeeman splitting and valley splitting, except when these parameters come into resonance. The fidelity remains independent of the overall valley phase for valley eigenstates, while for generic valley states, some minor corrections arise. We analyze the fidelity scaling for long qubit chains without valley effects, where crosstalk represents the only error source., Comment: 7 pages, 6 figures

We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "linear") rate under perfect variational family specification. In particular, we prove a quadratic bound on the gradient variance of the STL estimator, one which encompasses misspecified variational families. Combined with previous works on the quadratic variance condition, this directly implies convergence of BBVI with the use of projected stochastic gradient descent. For the projection operator, we consider a domain with triangular scale matrices, which the projection onto is computable in $\Theta(d)$ time, where $d$ is the dimensionality of the target posterior. We also improve existing analysis on the regular closed-form entropy gradient estimators, which enables comparison against the STL estimator, providing explicit non-asymptotic complexity guarantees for both., Comment: Accepted to AISTATS'24; v5: fixed missing expectations in iteration complexity statements; v6: changed to an indexing-friendly bibliography style

We develop a practical machine learning approach to determine the disorder landscape of Majorana nanowires by using training of the conductance matrix and inverting the conductance data in order to obtain the disorder details in the system. The inversion carried out through machine learning using different disorder parametrizations turns out to be unique in the sense that any input tunnel conductance as a function of chemical potential and Zeeman energy can indeed be inverted to provide the correct disorder landscape. Our work opens up a qualitatively new direction of directly determining the topological invariant and the Majorana wave-function structure corresponding to a transport profile of a device using simulations that quantitatively match the specific conductance profile. In addition, this also opens up the possibility for optimizing Majorana systems by figuring out the (generally unknown) underlying disorder only through the conductance data. An accurate estimate of the applicable spin-orbit coupling in the system can also be obtained within the same scheme., Comment: 13 pages, 29 figures

Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential moderators before administering the treatment in an experiment, doing so may also bias causal effect estimation if the covariate measurement primes respondents to react differently to the treatment. This paper formally analyzes this trade-off between post-treatment and priming biases in three experimental designs that vary when moderators are measured: pre-treatment, post-treatment, or a randomized choice between the two. We derive nonparametric bounds for interactions between the treatment and the moderator under each design and show how to use substantive assumptions to narrow these bounds. These bounds allow researchers to assess the sensitivity of their empirical findings to priming and post-treatment bias. We then apply the proposed methodology to a survey experiment on electoral messaging., Comment: 28 pages (main text), 22 pages (supplementary materials), 5 figures