arXiv subjects

Andrew Davis

Publications and source records attributed to Andrew Davis.

16 recordsLinked to original sources

arXiv2026

G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes

Physics-aware recurrent convolutional networks (PARC) have demonstrated strong performance in predicting nonlinear spatiotemporal dynamics by embedding differential operators directly into the computational graph of a neural network. However, pixel-based convolutions are restricted to static, uniform Cartesian grids, making them ill-suited to following evolving localized structures in an efficient manner. Graph neural networks (GNNs) naturally handle irregular spatial discretizations, but existing graph-based physics-aware deep learning (PADL) methods have difficulty handling extreme nonlinear regimes. To address these limitations, we propose Graph PARC (G-PARC), which uses moving least squares (MLS) kernels to approximate spatial derivatives on unstructured graphs, and embeds the derivatives of governing partial differential equations into the network's computational graph. G-PARC achieves better accuracy with 2-3x fewer parameters than MeshGraphNet, MeshGraphKAN, and GraphSAGE, replacing the traditional encoder-processor-decoder framework with analytically computed differential operators. We demonstrate that G-PARC (1) generalizes across nonuniform spatial and temporal discretizations; (2) handles moving meshes required for structural deformation; and (3) outperforms existing graph-based PADL methods on nonlinear benchmarks including fluvial hydrology, planar shock waves, and elastoplastic dynamics. By embedding explicit physical operators within the flexibility of GNNs, G-PARC enables accurate modeling of extreme nonlinear phenomena on complex computational domains, moving PADLbeyond idealized Cartesian grids.

cs.LG↗

arXiv2026

The SPHEREx Instrument: Calibration, testing and performance measurements of the NIR 2 spectroscopic surveyor from the laboratory to in-orbit commissioning

The SPHEREx near-infrared space telescope is an all-sky spectroscopic survey mission launched on March 12th, 2025 UTC. In addition to providing the community with a spectral database applicable to a wide range of investigations, it is optimized to address three core science goals: to survey the large scale structure of the Universe for signatures of non-Gaussianity during inflation; to conduct intensity mapping studies of the extragalactic background light for probing the history of galaxy evolution; and to survey the plane of the Milky Way for the prevalence and distribution of water and other biogenic ices. Each of these science goals imposes unique requirements on the performance of the instrument. We detail the design and testing strategies and report the performance results for the full instrument test campaign, ranging from component-level screening to in-orbit tests during the commissioning phase. The instrument, currently operating in full science survey mode, meets all of its driving requirements including optical performance, point source sensitivity, thermal stability and correlated noise minimization.

astro-ph.IM↗

arXiv2026

Chasing Tails: How Do People Respond to Wait Time Distributions?

We use a series of pre-registered, incentive-compatible online experiments to investigate how people evaluate and choose among different waiting time distributions. Our main findings are threefold. First, consistent with prior literature, people show an aversion to both longer expected waits and higher variance. Second, and more surprisingly, moment-based utility models fail to capture preferences when distributions have thick-right tails: indeed, decision-makers strongly prefer distributions with long-right tails (where probability mass is more evenly distributed over a larger support set) relative to tails that exhibit a spike near the maximum possible value, even when controlling for mean, variance, and higher moments. Conditional Value at Risk (CVaR) utility models commonly used in portfolio theory predict these choices well. Third, when given a choice, decision-makers overwhelmingly seek information about right-tail outcomes. These results have practical implications for service operations: (1) service designs that create a spike in long waiting times (such as priority or dedicated queue designs) may be particularly aversive; (2) when informativeness is the goal, providers should prioritize sharing right-tail probabilities or percentiles; and (3) to increase service uptake, providers can strategically disclose (or withhold) distributional information depending on right-tail shape.

econ.GN↗

arXiv2025

The Homotopy Type of Spaces of Flat Connections for Classical Lie Groups

Let $M$ be a smooth manifold. We use Chern-Weil theory to study the characteristic classes of principal $G$-bundles built from continuous families of $\pi_{1}(M)$-representations, where $G$ is a compact Lie group. We then relate these families to the functorial map $$\text{Hom}(\pi_{1}(M), G)\rightarrow\text{Map}_{*}(M,BG)$$ and use this relationship to study the weak homotopy type of the space of flat connections for $U(n)$, $O(n)$, $SO(n)$, and $\text{Spin}(n)$ bundles.

math.AT↗

arXiv2024

Exascale Simulations of Fusion and Fission Systems

We discuss pioneering heat and fluid flow simulations of fusion and fission energy systems with NekRS on exascale computing facilities, including Frontier and Aurora. The Argonne-based code, NekRS, is a highly-performant open-source code for the simulation of incompressible and low-Mach fluid flow, heat transfer, and combustion with a particular focus on turbulent flows in complex domains. It is based on rapidly convergent high-order spectral element discretizations that feature minimal numerical dissipation and dispersion. State-of-the-art multilevel preconditioners, efficient high-order time-splitting methods, and runtime-adaptive communication strategies are built on a fast OCCA-based kernel library, libParanumal, to provide scalability and portability across the spectrum of current and future high-performance computing platforms. On Frontier, Nek5000/RS has achieved an unprecedented milestone in breaching over 1 trillion degrees of freedom with the spectral element methods for the simulation of the CHIMERA fusion technology testing platform. We also demonstrate for the first time the use of high-order overset grids at scale.

cs.CE↗

arXiv2024

Defending Large Language Models Against Attacks With Residual Stream Activation Analysis

The widespread adoption of Large Language Models (LLMs), exemplified by OpenAI's ChatGPT, brings to the forefront the imperative to defend against adversarial threats on these models. These attacks, which manipulate an LLM's output by introducing malicious inputs, undermine the model's integrity and the trust users place in its outputs. In response to this challenge, our paper presents an innovative defensive strategy, given white box access to an LLM, that harnesses residual activation analysis between transformer layers of the LLM. We apply a novel methodology for analyzing distinctive activation patterns in the residual streams for attack prompt classification. We curate multiple datasets to demonstrate how this method of classification has high accuracy across multiple types of attack scenarios, including our newly-created attack dataset. Furthermore, we enhance the model's resilience by integrating safety fine-tuning techniques for LLMs in order to measure its effect on our capability to detect attacks. The results underscore the effectiveness of our approach in enhancing the detection and mitigation of adversarial inputs, advancing the security framework within which LLMs operate.

cs.CR↗

arXiv2024

SPHEREx: NASA's Near-Infrared Spectrophotmetric All-Sky Survey

SPHEREx, the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and ices Explorer, is a NASA MIDEX mission planned for launch in 2024. SPHEREx will carry out the first all-sky spectral survey at wavelengths between 0.75 micron and 5 micron with spectral resolving power ~40 between 0.75 and 3.8 micron and ~120 between 3.8 and 5 micron At the end of its two-year mission, SPHEREx will provide 0.75-to-5 micron spectra of each 6.2"x6.2" pixel on the sky - 14 billion spectra in all. This paper updates an earlier description of SPHEREx presenting changes made during the mission's Preliminary Design Phase, including a discussion of instrument integration and test and a summary of the data processing, analysis, and distribution plans.

astro-ph.IM↗

arXiv2022

Quantum chaos and phase transition in the Yukawa-SYK model

We analyze the quantum chaotic behavior of the Yukawa-SYK model as a function of filling and temperature, which describes random Yukawa interactions between $N$ complex fermions and $M$ bosons in zero spatial dimensions, for both the non-Fermi liquid and insulating states at finite temperature and chemical potential. We solve the ladder equations for the out-of-time-order correlator (OTOC) for both the bosons and fermions. Despite the appearance of the chemical potential in the Hamiltonian, which explicitly introduces an additional energy scale, the OTOCs for the fermions and bosons in the non-Fermi liquid state turn out to be unaffected, and the Lyapunov exponents that diagnose chaos remain maximal. As the chemical potential increases, the system is known to experience a first-order transition from a critical phase to a gapped insulating phase. We postulate that the boundary of the region in parameter space where each phase is (meta)stable coincides with the curve on which the Lyapunov exponent is maximal. By calculating the exponent in the insulating phase and comparing to numerical results on the boundaries of stability, we show that this is plausible.

cond-mat.str-el↗

arXiv2021

Phase diagram of the spin-1/2 Yukawa-SYK model: Non-Fermi liquid, insulator, and superconductor

We analyze the phase diagram of the Yukawa-Sachdev-Ye-Kitaev model, which describes complex fermions randomly interacting with real bosons via a Yukawa coupling, at finite temperatures and varying fermion density. In a recent work [Phys. Rev. Research 2, 033084 (2020)] it has been shown that, upon varying filling or chemical potential, there exists a first-order quantum phase transition between a non-Fermi liquid (nFL) phase and an insulating phase. Here we show that in such a model with time-reversal symmetry this quantum phase transition is preempted by a pairing phase that develops as a low-temperature instability. As a remnant of the would-be nFL-insulator transition, the superconducting critical temperature rapidly decreases beyond a certain chemical potential. On the other hand, depending on parameters the first-order quantum phase transition extend to finite-temperatures and terminate at a thermal critical point, beyond which the nFL and the insulator become the same phase, similar to that of the liquid-gas and metal-insulator transition in real materials. We determine the pairing phase boundary and the location of the thermal critical point via combined analytic and quantum Monte Carlo numeric efforts. Our results provide the model realization of the transition of nFL's towards superconductivity and insulating states, therefore offer a controlled platform for future investigations of the generic phase diagram that hosts nFL, insulator and superconductor and their phase transitions.

cond-mat.str-el↗

arXiv2020

Timing Calibration of the NuSTAR X-ray Telescope

The Nuclear Spectroscopic Telescope Array (NuSTAR) mission is the first focusing X-ray telescope in the hard X-ray (3-79 keV) band. Among the phenomena that can be studied in this energy band, some require high time resolution and stability: rotation-powered and accreting millisecond pulsars, fast variability from black holes and neutron stars, X-ray bursts, and more. Moreover, a good alignment of the timestamps of X-ray photons to UTC is key for multi-instrument studies of fast astrophysical processes. In this Paper, we describe the timing calibration of the NuSTAR mission. In particular, we present a method to correct the temperature-dependent frequency response of the on-board temperature-compensated crystal oscillator. Together with measurements of the spacecraft clock offsets obtained during downlinks passes, this allows a precise characterization of the behavior of the oscillator. The calibrated NuSTAR event timestamps for a typical observation are shown to be accurate to a precision of ~65 microsec.

astro-ph.IM↗

arXiv2020

Yukawa-SYK model and Self-tuned Quantum Criticality

Non-Fermi liquids (NFL) are a class of strongly interacting gapless fermionic systems without long-lived quasiparticle excitations. An important group of NFL model features itinerant fermions coupled to soft bosonic fluctuations near a quantum-critical point (QCP), and are widely believed to capture the essential physics of many unconventional superconductors. However numerically the direct observation of a canonical NFL behavior in such systems, characterized by a power-law form in the Green's function, has been elusive. Here we consider a Sachdev-Ye-Kitaev (SYK)-like model with random Yukawa interaction between critical bosons and fermions (dubbed Yukawa-SYK model). We show it is immune from minus-sign problem and hence can be solved exactly via large-scale quantum Monte Carlo simulation beyond the large-$N$ limit accessible to analytical approaches. Our simulation demonstrates the Yukawa-SYK model features "self-tuned quantum criticality", namely the system is critical independent of the bosonic bare mass. We put these results to test at finite $N$, and our unbiased numerics reveal clear evidence of these exotic quantum-critical NFL properties -- the power-law behavior in Green's function of fermions and bosons -- which propels the theoretical understanding of critical Planckian metals and unconventional superconductors.

cond-mat.str-el↗

arXiv2017

Generalized Convolutional Neural Networks for Point Cloud Data

The introduction of cheap RGB-D cameras, stereo cameras, and LIDAR devices has given the computer vision community 3D information that conventional RGB cameras cannot provide. This data is often stored as a point cloud. In this paper, we present a novel method to apply the concept of convolutional neural networks to this type of data. By creating a mapping of nearest neighbors in a dataset, and individually applying weights to spatial relationships between points, we achieve an architecture that works directly with point clouds, but closely resembles a convolutional neural net in both design and behavior. Such a method bypasses the need for extensive feature engineering, while proving to be computationally efficient and requiring few parameters.

cs.CV↗

arXiv2016

Parallel local approximation MCMC for expensive models

Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model, such as a system of partial differential equations. In recent work [Conrad et al. JASA 2016, arXiv:1402.1694], we described a framework for constructing and refining local approximations of such models during an MCMC simulation. These posterior--adapted approximations harness regularity of the model to reduce the computational cost of inference while preserving asymptotic exactness of the Markov chain. Here we describe two extensions of that work. First, we prove that samplers running in parallel can collaboratively construct a shared posterior approximation while ensuring ergodicity of each associated chain, providing a novel opportunity for exploiting parallel computation in MCMC. Second, focusing on the Metropolis--adjusted Langevin algorithm, we describe how a proposal distribution can successfully employ gradients and other relevant information extracted from the approximation. We investigate the practical performance of our strategies using two challenging inference problems, the first in subsurface hydrology and the second in glaciology. Using local approximations constructed via parallel chains, we successfully reduce the run time needed to characterize the posterior distributions in these problems from days to hours and from months to days, respectively, dramatically improving the tractability of Bayesian inference.

stat.CO↗

arXiv2014

Neutron shielding and activation of the MASTU device and surrounds

A significant functional upgrade is planned for the Mega Ampere Spherical Tokamak (MAST) device, located at Culham in the UK, including the implementation of a notably greater neutral beam injection power. This upgrade will cause the emission of a substantially increased intensity of neutron radiation for a substantially increased amount of time upon operation of the device. Existing shielding and activation precautions are shown to prove insufficient in some regards, and recommendations for improvements are made, including the following areas: shielding doors to MAST shielded facility enclosure (known as "the blockhouse"); north access tunnel; blockhouse roof; west cabling duct. In addition, some specific neutronic dose rate questions are addressed and answered; those discussed here relate to shielding penetrations and dose rate reflected from the air above the device ("skyshine").

physics.ins-det↗

arXiv2013

Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks

Scalability properties of deep neural networks raise key research questions, particularly as the problems considered become larger and more challenging. This paper expands on the idea of conditional computation introduced by Bengio, et. al., where the nodes of a deep network are augmented by a set of gating units that determine when a node should be calculated. By factorizing the weight matrix into a low-rank approximation, an estimation of the sign of the pre-nonlinearity activation can be efficiently obtained. For networks using rectified-linear hidden units, this implies that the computation of a hidden unit with an estimated negative pre-nonlinearity can be ommitted altogether, as its value will become zero when nonlinearity is applied. For sparse neural networks, this can result in considerable speed gains. Experimental results using the MNIST and SVHN data sets with a fully-connected deep neural network demonstrate the performance robustness of the proposed scheme with respect to the error introduced by the conditional computation process.

cs.LG↗

arXiv2010

Virialization of high redshift dark matter haloes

We present results of a study of the virial state of high redshift dark matter haloes in an N-body simulation. We find that the majority of collapsed, bound haloes are not virialized at any redshift slice in our study ($z=15-6$) and have excess kinetic energy. At these redshifts, merging is still rampant and the haloes cannot strictly be treated as isolated systems. To assess if this excess kinetic energy arises from the environment, we include the surface pressure term in the virial equation explicitly and relax the assumption that the density at the halo boundary is zero. Upon inclusion of the surface term, we find that the haloes are much closer to virialization, however, they still have some excess kinetic energy. We report trends of the virial ratio including the extra surface term with three key halo properties: spin, environment, and concentration. We find that haloes with closer neighbors depart more from virialization, and that haloes with larger spin parameters do as well. We conclude that except at the lowest masses ($M < 10^6 \Msun$), dark matter haloes at high redshift are not fully virialized. This finding has interesting implications for galaxy formation at these high redshifts, as the excess kinetic energy will impact the subsequent collapse of baryons and the formation of the first disks and/or baryonic structures.

astro-ph.CO↗