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Quantum networking and computing technologies demand scalable hardware with high-speed control for large systems of quantum devices. Solid-state platforms have emerged as promising candidates, offering scalable fabrication for a wide range of qubits. Architectures based on spin-photon interfaces allow for highly-connected quantum networks over photonic links, enabling entanglement distribution for quantum networking and distributed quantum computing protocols. With the potential to address these demands, optically-active spin defects in silicon are one proposed platform for building quantum technologies. Here, we electrically excite the silicon T centre in integrated optoelectronic devices that combine nanophotonic waveguides and cavities with p-i-n diodes. We observe single-photon electroluminescence from a cavity-coupled T centre with $g^{(2)}(0)=0.05(2)$. Further, we use the electrically-triggered emission to herald the electron spin state, initializing it with $92(8)\%$ fidelity. This shows, for the first time, electrically-injected single-photon emission from a silicon colour centre and a new method of electrically-triggered spin initialization. These findings present a new telecommunications band light source for silicon and a highly parallel control method for T centre quantum processors, advancing the T centre as a versatile defect for scalable quantum technologies., Comment: 12 pages and 3 figures in main manuscript; 8 pages and 8 figures in supplementary information

We present the discovery of 11 new transiting brown dwarfs and low-mass M-dwarfs from NASA's TESS mission: TOI-2844, TOI-3122, TOI-3577, TOI-3755, TOI-4462, TOI-4635, TOI-4737, TOI-4759, TOI-5240, TOI-5467, and TOI-5882. They consist of 5 brown dwarf companions and 6 very low mass stellar companions ranging in mass from $25 M_{\rm J}$ to $128 M_{\rm J}$. We used a combination of photometric time-series, spectroscopic, and high resolution imaging follow-up as a part of the TESS Follow-up Observing Program (TFOP) in order to characterize each system. With over 50 transiting brown dwarfs confirmed, we now have a large enough sample to directly test different formation and evolutionary scenarios. We provide a renewed perspective on the transiting brown dwarf desert and its role in differentiating between planetary and stellar formation mechanisms. Our analysis of the eccentricity distribution for the transiting brown dwarf sample does not support previous claims of a transition between planetary and stellar formation at $\sim42$ $M_{\rm J}$. We also contribute a first look into the metallicity distribution of transiting companions in the range $7 - 150$ $M_{\rm J}$, showing that this too does not support a $\sim42$ $M_{\rm J}$ transition. Finally, we also detect a significant lithium absorption feature in one of the brown dwarf hosts (TOI-5882) but determine that the host star is likely old based on rotation, kinematic, and photometric measurements. We therefore claim that TOI-5882 may be a candidate for planetary engulfment., Comment: Submitted, 32 pages, 16 figures

In the corona, plasma is accelerated to hundreds of kilometers per second, and heated to temperatures hundreds of times hotter than the Sun's surface, before it escapes to form the solar wind. Decades of space-based experiments have shown that the energization process does not stop after it escapes. Instead, the solar wind continues to accelerate and it cools far more slowly than a freely-expanding adiabatic gas. Recent work suggests that fast solar wind requires additional momentum beyond what can be provided by the observed thermal pressure gradients alone whereas it is sufficient for the slowest wind. The additional acceleration for fast wind can be provided through an Alfv\'en wave pressure gradient. Beyond this fast-slow categorization, however, a subset of slow solar wind exhibits high Alfv\'enicity that suggest Alfv\'en waves could play a larger role in its acceleration compared to conventional slow wind outflows. Through a well-timed conjunction between Solar Orbiter and Parker Solar Probe, we trace the energetics of slow wind to compare with a neighboring Alfv\'enic slow solar wind stream. An analysis that integrates remote and heliospheric properties and modeling of the two distinct solar wind streams finds Alfv\'enic slow solar wind behaves like fast wind, where a wave pressure gradient is required to reconcile its full acceleration, while non-Alfv\'enic slow wind can be driven by its non-adiabatic electron and proton thermal pressure gradients. Derived coronal conditions of the source region indicate good model compatibility but extended coronal observations are required to effectively trace solar wind energetics below Parker's orbit., Comment: 20 pages, 9 figures, accepted for publication to ApJ on January 3, 2025

The matter power spectrum, $P(k)$, is one of the fundamental quantities in the study of large-scale structure in cosmology. Here, we study its small-scale asymptotic limit, and show that for cold dark matter in $d$ spatial dimensions, $P(k)$ has a universal $k^{-d}$ asymptotic scaling with the wave-number $k$, for $k \gg k_{\rm nl}$, where $k_{\rm nl}^{-1}$ denotes the length scale at which non-linearities in gravitational interactions become important. We propose a theoretical explanation for this scaling, based on a non-perturbative analysis of the system's phase-space structure. Gravitational collapse is shown to drive a turbulent phase-space flow of the quadratic Casimir invariant, where the linear and non-linear time scales are balanced, and this balance dictates the $k$ dependence of the power spectrum. A parallel is drawn to Batchelor turbulence in hydrodynamics, where large scales mix smaller ones via tidal interactions. The $k^{-d}$ scaling is also derived by expressing $P(k)$ as a phase-space integral in the framework of kinetic field theory, which is analysed by the saddle-point method; the dominant critical points of this integral are precisely those where the time scales are balanced. The coldness of the dark-matter distribution function -- its non-vanishing only on a $d$-dimensional sub-manifold of phase-space -- underpins both approaches. The theory is accompanied by $1\mathrm{D}$ Vlasov--Poisson simulations, which confirm it., Comment: Submitted, comments welcome

We propose to use interpolating operators for lattice quantum chromodyanmics (QCD) calculations of highly-boosted pions and nucleons with kinematically-enhanced ground-state overlap factors at large momentum. Because this kinematic enhancement applies to the signal but not the variance of the correlation function, these interpolating operators can achieve better signal-to-noise ratios at large momentum. We perform proof-of-principle calculations with boosted pions and nucleons using close-to-physical and larger quark masses to explore the utility of our proposal. Results for effective energies and matrix elements, as well as Lanczos ground-state energy estimators, are consistent with theoretical expectations for signal-to-noise improvement at large momenta.

Neutrino-nucleus scattering cross sections are critical theoretical inputs for long-baseline neutrino oscillation experiments. However, robust modeling of these cross sections remains challenging. For a simple but physically motivated toy model of the DUNE experiment, we demonstrate that an accurate neural-network model of the cross section -- leveraging Standard Model symmetries -- can be learned from near-detector data. We then perform a neutrino oscillation analysis with simulated far-detector events, finding that the modeled cross section achieves results consistent with what could be obtained if the true cross section were known exactly. This proof-of-principle study highlights the potential of future neutrino near-detector datasets and data-driven cross-section models., Comment: 5 pages, 2 figures + 6 pages Supplemental Material

Recent work introduced a new framework for analyzing correlation functions with improved convergence and signal-to-noise properties, as well as rigorous quantification of excited-state effects, based on the Lanczos algorithm and spurious eigenvalue filtering with the Cullum-Willoughby test. Here, we extend this framework to the analysis of correlation-function matrices built from multiple interpolating operators in lattice quantum chromodynamics (QCD) by constructing an oblique generalization of the block Lanczos algorithm, as well as a new physically motivated reformulation of the Cullum-Willoughby test that generalizes to block Lanczos straightforwardly. The resulting block Lanczos method directly extends generalized eigenvalue problem (GEVP) methods, which can be viewed as applying a single iteration of block Lanczos. Block Lanczos provides qualitative and quantitative advantages over GEVP methods analogous to the benefits of Lanczos over the standard effective mass, including faster convergence to ground- and excited-state energies, explicitly computable two-sided error bounds, straightforward extraction of matrix elements of external currents, and asymptotically constant signal-to-noise. No fits or statistical inference are required. Proof-of-principle calculations are performed for noiseless mock-data examples as well as two-by-two proton correlation-function matrices in lattice QCD., Comment: 39+13 pages, 36 figures, 7 tables

We compiled the X-ray and soft gamma-ray observations of the Galactic black hole binary XTE J1859+226 in the 1999--2000 outburst from RXTE, ASCA, BeppoSAX and CGRO. Throughout systematic spectral analysis using a two-component model consisting of a multi-temperature accretion disk plus a fraction of its flux convolved with an empirical Comptonized powerlaw component, we found that the innermost radius ($r_{\rm in}$) and temperature (Tin) of the disk are very variable with time in the rising phase of soft X-ray flux where Type-A/B/C low-frequency quasi-periodic oscillations (QPOs) were found. After this phase, $r_{\rm in}$ remains constant at around 60 km assuming a distance of 8 kpc and an inclination angle of 67$^{\circ}$, and Tin smoothly decays with time. The constant $r_{\rm in}$ suggests a presence of the innermost stable circular orbit (ISCO), with $r_{\rm in}$ repeatedly moving closer and farther away from the ISCO in the rising phase. Both disk parameters are remarkably correlated with independently analyzed timing properties such as QPO frequency and rms variability. Type-A/B QPOs are seen only when $r_{\rm in}$ is close to the ISCO, while Type-C are seen when $r_{\rm in}$ is truncated and the frequency changes with a relation of $r^{-1.0}_{\rm in}$, supporting that Type-C QPOs occur at the inner edge of the truncated disk. Accurate determinations of the frequency--$r_{\rm in}$ relation for various objects should be a powerful tool to discriminate plausible Type-C QPO models. Furthermore, we suggest that jet ejection events may occur when $r_{\rm in}$ rapidly approaches to the ISCO, along with rapid changes of the disk flux, the rms variability and the hardness ratio. A rapid shrinkage of $r_{\rm in}$ down to the ISCO can be a useful index as a precursor of radio flares for triggering Target-of-Opportunity observations and would provide constraints on jet launching mechanisms., Comment: 21 pages, 16 figures, accepted for publication in PASJ

This paper investigates the maximum downlink spectral efficiency of low earth orbit (LEO) constellations. Spectral efficiency, in this context, refers to the sum rate of the entire network per unit spectrum per unit area on the earth's surface. For practicality all links employ single-user codebooks and treat interference as noise. To estimate the maximum achievable spectral efficiency, we propose and analyze a regular configuration, which deploys satellites and ground terminals in hexagonal lattices. Additionally, for wideband networks with arbitrary satellite configurations, we introduce a subband allocation algorithm aimed at maximizing the overall spectral efficiency. Simulation results indicate that the regular configuration is more efficient than random configurations. As the number of randomly placed satellites increases within an area, the subband allocation algorithm achieves a spectral efficiency that approaches the spectral efficiency achieved by the regular configuration. Further improvements are demonstrated by reconfiguring associations so that nearby transmitters avoid pointing to the same area.

Sub-Neptunes - volatile-rich exoplanets smaller than Neptune - are intrinsically the most common type of planet known. However, the formation and nature of these objects, as well as the distinctions between sub-classes (if any), remain unclear. Two powerful tools to tease out the secrets of these worlds are measurements of (i) atmospheric composition and structure revealed by transit and/or eclipse spectroscopy, and (ii) mass, radius, and density revealed by transit photometry and Doppler spectroscopy. Here we present OrCAS, a survey to better elucidate the origins, compositions, and atmospheres of sub-Neptunes. This radial velocity survey uses a repeatable, quantifiable metric to select targets suitable for subsequent transmission spectroscopy and address key science themes about the atmospheric & internal compositions and architectures of these systems. Our survey targets 26 systems with transiting sub-Neptune planet candidates, with the overarching goal of increasing the sample of such planets suitable for subsequent atmospheric characterization. This paper lays out our survey's science goals, defines our target prioritization metric, and performs light-curve fits and statistical validation using existing TESS photometry and ground-based follow-up observations. Our survey serves to continue expanding the sample of small exoplanets with well-measured properties orbiting nearby bright stars, ensuring fruitful studies of these systems for many years to come., Comment: 20 pages, 4 figures, 4 tables, 26 sub-Neptunes, 31 TOIs. Accepted to AJ

Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses challenges due to inherent spatial distortions and modality-specific variations. Existing methods largely rely on direct alignment, which often fails to capture complex cross-modal relationships. To address these limitations, we propose a novel framework that aligns gene and image features using a ranking-based alignment loss, preserving relative similarity across modalities and enabling robust multi-scale alignment. To further enhance the alignment's stability, we employ self-supervised knowledge distillation with a teacher-student network architecture, effectively mitigating disruptions from high dimensionality, sparsity, and noise in gene expression data. Extensive experiments on gene expression prediction and survival analysis demonstrate our framework's effectiveness, showing improved alignment and predictive performance over existing methods and establishing a robust tool for gene-guided image representation learning in digital pathology., Comment: 17 pages, 8 figures

We show, using the pseudo-$C_\ell$ technique, how to estimate cosmic shear and galaxy-galaxy lensing power spectra that are insensitive to the effects of multiple sources of lensing bias including source-lens clustering, magnification bias and obscuration effects. All of these effects are of significant concern for ongoing and near-future Stage-IV cosmic shear surveys. Their common attribute is that they all introduce a cosmological dependence into the selection of the galaxy shear sample. Here, we show how a simple adaptation of the pseudo-$C_\ell$ method can help to suppress these biases to negligible levels in a model-independent way. Our approach is based on making pixelised maps of the shear field and then using a uniform weighting of those shear maps when extracting power spectra. To produce unbiased measurements, the weighting scheme must be independent of the cosmological signal, which makes the commonly-used inverse-variance weighting scheme unsuitable for cosmic shear measurements. We demonstrate this explicitly. A frequently-cited motivation for using inverse-variance weights is to minimize the errors on the resultant power spectra. We find that, for a Stage-IV-like survey configuration, this motivation is not compelling: the precision of power spectra recovered from uniform-weighted maps is only very slightly degraded compared to those recovered from an inverse-variance analysis, and we predict no degradation in cosmological parameter constraints. We suggest that other 2-point statistics, such as real-space correlation functions, can be rendered equally robust to these lensing biases by applying those estimators to pixelised shear maps using a uniform weighting scheme., Comment: 12 pages, 5 figures, 1 table. Submitted to MNRAS. Comments welcomed

Patient-centered research is increasingly important in narrowing the gap between research and patient care, yet incorporating patient perspectives into health research has been inconsistent. We propose an automated framework leveraging innovative natural language processing (NLP) and artificial intelligence (AI) with patient portal messages to generate research ideas that prioritize important patient issues. We further quantified the quality of AI-generated research topics. To define patient clinical concerns, we analyzed 614,464 patient messages from 25,549 individuals with breast or skin cancer obtained from a large academic hospital (2013 to 2024), constructing a 2-staged unsupervised NLP topic model. Then, we generated research topics to resolve the defined issues using a widely used AI (ChatGPT-4o, OpenAI Inc, April 2024 version) with prompt-engineering strategies. We guided AI to perform multi-level tasks: 1) knowledge interpretation and summarization (e.g., interpreting and summarizing the NLP-defined topics), 2) knowledge generation (e.g., generating research ideas corresponding to patients issues), 3) self-reflection and correction (e.g., ensuring and revising the research ideas after searching for scientific articles), and 4) self-reassurance (e.g., confirming and finalizing the research ideas). Six highly experienced breast oncologists and dermatologists assessed the significance and novelty of AI-generated research topics using a 5-point Likert scale (1-exceptional, 5-poor). One-third of the AI-suggested research topics were highly significant and novel when both scores were lower than the average. Two-thirds of the AI-suggested topics were novel in both cancers. Our findings demonstrate that AI-generated research topics reflecting patient perspectives via a large volume of patient messages can meaningfully guide future directions in patient-centered health research.

The rapid evolution of Large Language Models (LLMs) offers promising potential to alleviate the global scarcity of mental health professionals. However, LLMs' alignment with essential mental health counseling competencies remains understudied. We introduce CounselingBench, a novel NCMHCE-based benchmark evaluating LLMs across five key mental health counseling competencies. Testing 22 general-purpose and medical-finetuned LLMs, we find frontier models exceed minimum thresholds but fall short of expert-level performance, with significant variations: they excel in Intake, Assessment & Diagnosis yet struggle with Core Counseling Attributes and Professional Practice & Ethics. Medical LLMs surprisingly underperform generalist models accuracy-wise, while at the same time producing slightly higher-quality justifications but making more context-related errors. Our findings highlight the complexities of developing AI systems for mental health counseling, particularly for competencies requiring empathy and contextual understanding. We found that frontier LLMs perform at a level exceeding the minimal required level of aptitude for all key mental health counseling competencies, but fall short of expert-level performance, and that current medical LLMs do not significantly improve upon generalist models in mental health counseling competencies. This underscores the critical need for specialized, mental health counseling-specific fine-tuned LLMs that rigorously aligns with core competencies combined with appropriate human supervision before any responsible real-world deployment can be considered., Comment: 9 Pages, In Submission to NAACL 2025

Adverse Drug Reactions (ADRs) from psychiatric medications are the leading cause of hospitalizations among mental health patients. With healthcare systems and online communities facing limitations in resolving ADR-related issues, Large Language Models (LLMs) have the potential to fill this gap. Despite the increasing capabilities of LLMs, past research has not explored their capabilities in detecting ADRs related to psychiatric medications or in providing effective harm reduction strategies. To address this, we introduce the Psych-ADR benchmark and the Adverse Drug Reaction Response Assessment (ADRA) framework to systematically evaluate LLM performance in detecting ADR expressions and delivering expert-aligned mitigation strategies. Our analyses show that LLMs struggle with understanding the nuances of ADRs and differentiating between types of ADRs. While LLMs align with experts in terms of expressed emotions and tone of the text, their responses are more complex, harder to read, and only 70.86% aligned with expert strategies. Furthermore, they provide less actionable advice by a margin of 12.32% on average. Our work provides a comprehensive benchmark and evaluation framework for assessing LLMs in strategy-driven tasks within high-risk domains., Comment: 30 pages, 8 figures, 16 tables

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid electrolyte interphase formation and the long testing time (~100 days) for cells to reach the end of life. We propose a systematic feature design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. Two simple Q(V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, achieved a median of 9.20% error for cycle life prediction, outperforming thousands of autoML models using pre-defined features. We attribute the strong performance of our designed features to their physical origins - the voltage ranges identified by our framework capture the effects of formation temperature and microscopic particle resistance heterogeneity. By designing highly interpretable features, our approach can accelerate formation research, leveraging the interplay between data-driven feature design and mechanistic understanding., Comment: Main: 27 pages, 6 figures. SI: 13 pages, 9 figures

Hard X-ray-selected samples of Active Galactic Nuclei (AGN) provide one of the cleanest views of supermassive black hole accretion, but are biased against objects obscured by Compton-thick gas column densities of $N_{\rm H}$ $>$ 10$^{24}$ cm$^{-2}$. To tackle this issue, we present the NuSTAR Local AGN $N_{\rm H}$ Distribution Survey (NuLANDS)$-$a legacy sample of 122 nearby ($z$ $<$ 0.044) AGN primarily selected to have warm infrared colors from IRAS between 25$-$60 $\mu$m. We show that optically classified type 1 and 2 AGN in NuLANDS are indistinguishable in terms of optical [OIII] line flux and mid-to-far infrared AGN continuum bolometric indicators, as expected from an isotropically selected AGN sample, while type 2 AGN are deficient in terms of their observed hard X-ray flux. By testing many X-ray spectroscopic models, we show the measured line-of-sight column density varies on average by $\sim$ 1.4 orders of magnitude depending on the obscurer geometry. To circumvent such issues we propagate the uncertainties per source into the parent column density distribution, finding a directly measured Compton-thick fraction of 35 $\pm$ 9%. By construction, our sample will miss sources affected by severe narrow-line reddening, and thus segregates sources dominated by small-scale nuclear obscuration from large-scale host-galaxy obscuration. This bias implies an even higher intrinsic obscured AGN fraction may be possible, although tests for additional biases arising from our infrared selection find no strong effects on the measured column-density distribution. NuLANDS thus holds potential as an optimized sample for future follow-up with current and next-generation instruments aiming to study the local AGN population in an isotropic manner., Comment: Accepted for publication in ApJ. 50 pages (78 including appendix and bibliography), 21 figures

Nano-patterned magnetic materials have opened new venues on the investigation of strongly correlated phenomena including artificial spin-ice systems, geometric frustration, magnetic monopoles, for technologically important applications such as reconfigurable ferromagnetism. With the advent of atomically thin two-dimensional (2D) van der Waals (vdW) magnets a pertinent question is whether such compounds could make their way into this realm where interactions can be tailored so that unconventional states of matter could be assessed. Here we show that square islands of CrGeTe3 vdW ferromagnets distributed in a grid manifest antiferromagnetic correlations, essential to enable frustration resulting in an artificial spin-ice. By using a combination of SQUID-on-tip microscopy, focused ion beam lithography, and atomistic spin dynamic simulations, we show that pristine, isolated CGT flakes as small as 150*150*60 nm3 have tunable dipole-dipole interactions, which can be precisely controlled by their lateral spacing. There is a crossover between non-interacting islands and significant inter-island anticorrelation depending how they are spatially distributed allowing the creation of complex magnetic patterns not observable at the isolated flakes. Our findings suggest that the cross-talk between the nano-patterned magnets can be explored in the generation of even more complex spin configurations where exotic interactions may be manipulated in an unprecedent way., Comment: Main text: 12 pages, 5 figures. Supplementary information: 11 pages, 5 figures

Mis/disinformation is a common and dangerous occurrence on social media. Misattribution is a form of mis/disinformation that deals with a false claim of authorship, which means a user is claiming someone said (posted) something they never did. We discuss the difference between misinformation and disinformation and how screenshots are used to spread author misattribution on social media platforms. It is important to be able to find the original post of a screenshot to determine if the screenshot is being correctly attributed. To do this we have built several tools to aid in automating this search process. The first is a Python script that aims to categorize Twitter posts based on their structure, extract the metadata from a screenshot, and use this data to group all the posts within a screenshot together. We tested this process on 75 Twitter posts containing screenshots collected by hand to determine how well the script extracted metadata and grouped the individual posts, F1 = 0.80. The second is a series of scrapers being used to collect a dataset that can train and test a model to differentiate between various social media platforms. We collected 16,620 screenshots have been collected from Facebook, Instagram, Truth Social, and Twitter. Screenshots were taken by the scrapers of the web version and mobile version of each platform in both light and dark mode.

Lattice gauge fixing is required to compute gauge-variant quantities, for example those used in RI-MOM renormalization schemes or as objects of comparison for model calculations. Recently, gauge-variant quantities have also been found to be more amenable to signal-to-noise optimization using contour deformations. These applications motivate systematic parameterization and exploration of gauge-fixing schemes. This work introduces a differentiable parameterization of gauge fixing which is broad enough to cover Landau gauge, Coulomb gauge, and maximal tree gauges. The adjoint state method allows gradient-based optimization to select gauge-fixing schemes that minimize an arbitrary target loss function., Comment: 9 pages, 2 figures; Proceedings of the 41st International Symposium on Lattice Field Theory (Lattice 2024)

This paper develops a general data-driven approach to stochastic elastoplastic modelling that leverages atomistic simulation data directly rather than by fitting parameters. The approach is developed in the context of metallic glasses, which present inherent complexities due to their disordered structure. By harvesting statistics from simulated metallic glass shear response histories, the material state is mapped onto a two-dimensional state space consisting of the shear stress and the inelastic contribution to the potential energy. The resulting elastoplastic model is intrinsically stochastic and represented as a non-deterministic dynamical map. The state space statistics provide insights into the deformation physics of metallic glasses, revealing that two state variables are sufficient to describe the main features of the elastoplastic response. In this two-dimensional state space, the gradually quenched metallic glass rejuvenates during the initial quasi-elastic shearing, ultimately reaching a steady state that fluctuates about a fixed point in the state space as rejuvenation and aging balance., Comment: 22 pages, 5 figures

Verifying entanglement with experimental measurements requires that we take the limitations of experimental techniques into account, while still proving that the data obtained could not have been generated from a classical source. In the energy-time degree of freedom, this challenge is exacerbated because realistic high-resolution frequency measurements are obtained as a function of light passing through arbitrary filters positioned at uneven intervals. In this work, we show how the data gathered from these kinds of measurements can be used to fully certify the degree of energy and timing correlations needed to certify energy-time entanglement without having to make special assumptions about the state or the measurement device. We accomplish this by showing how to construct a continuous-variable probability density from the data that can closely estimate, but never over-estimate the correlations (and entanglement) actually present in the system, and note that these methods are applicable to all continuous-variable degrees of freedom (e.g., spatial, field quadratures, etc). We illustrate the feasibility of these methods using frequency and timing correlations obtainable from photon pairs in Spontaneous Parametric Down-Conversion., Comment: 11 pages (10 main plus one appendix), 2 figures

Microwave-optical quantum transduction is a key enabling technology in quantum networking, but has been plagued by a formidable technical challenge. As most microwave-optical-transduction techniques rely on three-wave mixing processes, the processes consume photons from a driving telecom-band (pump) laser to convert input microwave photons into telecom-band photons detuned from the laser by this microwave frequency. However, cleanly separating out single photons detuned only a few GHz away from a classically bright laser in the same spatial mode requires frequency filters of unprecedented extinction over a very narrow transition band, straining the capabilities of today's technology. Instead of confronting this challenge directly, we show how one may achieve the same transduction objective with comparable efficiency using a four-wave mixing process in which $pairs$ of pump photons are consumed to produce transduced optical photons widely separated in frequency from the pump. We develop this process by considering higher-order analogues of photoelasticity and electrostriction than those used in conventional optomechanics, and examine how the efficiency of this process can be made to exceed conventional optomechanical couplings., Comment: 5 pages + 2 more for appendices, and 1 figure

In this paper we generalize the technique of deflation to define two new methods to systematically find many local minima of a nonlinear least squares problem. The methods are based on the Gauss-Newton algorithm, and as such do not require the calculation of a Hessian matrix. They also require fewer deflations than for applying the deflated Newton method on the first order optimality conditions, as the latter finds all stationary points, not just local minima. One application of interest covered in this paper is the inverse eigenvalue problem (IEP) associated with the modelling of spectroscopic data of relevance to the physical and chemical sciences. Open source MATLAB code is provided at https://github.com/AlbanBloorRiley/DeflatedGaussNewton.

Detecting non-classical light is a central requirement for photonics-based quantum technologies. Unrivaled high efficiencies and low dark counts have positioned superconducting nanowire single photon detectors (SNSPDs) as the leading detector technology for fiber and integrated photonic applications. However, a central challenge lies in their integration within photonic integrated circuits regardless of material platform or surface topography. Here, we introduce a method based on transfer printing that overcomes these constraints and allows for the integration of SNSPDs onto arbitrary photonic substrates. We prove this by integrating SNSPDs and showing through-waveguide single-photon detection in commercially manufactured silicon and lithium niobate on insulator integrated photonic circuits. Our method eliminates bottlenecks to the integration of high-quality single-photon detectors, turning them into a versatile and accessible building block for scalable quantum information processing., Comment: 11 pages, 12 figures

Since Parker Solar Probe's (Parker's) first perihelion pass at the Sun, large amplitude Alfv\'en waves grouped in patches have been observed near the Sun throughout the mission. Several formation processes for these magnetic switchback patches have been suggested with no definitive consensus. To provide insight to their formation, we examine the heavy ion properties of several adjacent magnetic switchback patches around Parker's 11th perihelion pass capitalizing on a spacecraft lineup with Solar Orbiter where each samples the same solar wind streams over a large range of longitudes. Heavy ion properties (Fe/O, C$^{6+}$/C$^{5+}$, O$^{7+}$/O$^{6+}$) related to the wind's coronal origin, measured with Solar Orbiter can be linked to switchback patch structures identified near the Sun with Parker. We find that switchback patches do not contain distinctive ion and elemental compositional signatures different than the surrounding non-switchback solar wind. Both the patches and ambient wind exhibit a range of fast and slow wind qualities, indicating coronal sources with open and closed field lines in close proximity. These observations and modeling indicate switchback patches form in coronal hole boundary wind and with a range of source region magnetic and thermal properties. Furthermore, the heavy ion signatures suggest interchange reconnection and/or shear driven processes may play a role in their creation., Comment: Accepted for publication in ApJ on September 4th, 2024

Generic equilibria are derived for turbulent relaxing plasmas via an entropy-maximization procedure that accounts for the short-time conservation of certain collisionless invariants. The conservation of these collisionless invariants endows the system with a partial `memory' of its prior conditions, but is imperfect on long time scales due to the development of a turbulent cascade to small scales, which breaks the precise conservation of phase volume, making this memory imprecise. The equilibria are still determined by the short-time collisionless invariants, but the invariants themselves are driven to a universal form by the nature of the turbulence. This is numerically confirmed for the case of beam instabilities in one-dimensional electrostatic plasmas, where sufficiently strong turbulence appears to cause the distribution function of particle energies to develop a universal power-law tail, with exponent -2., Comment: 11 pages, 7 figures

After leaving the Sun's corona, the solar wind continues to accelerate and cools, but more slowly than expected for a freely expanding adiabatic gas. We use in situ measurements from the Parker Solar Probe and Solar Orbiter spacecrafts to investigate a stream of solar wind as it traverses the inner heliosphere. The observations show heating and acceleration of the the plasma between the outer edge of the corona and near the orbit of Venus, in connection to the presence of large amplitude Alfv\'en waves. Alfv\'en waves are perturbations in the interplanetary magnetic field that transport energy. Our calculations show the damping and mechanical work performed by the Alfv\'en waves is sufficient to power the heating and acceleration of the fast solar wind in the inner heliosphere., Comment: This is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science on August 30 2024, DOI: 10.1126/science.adk6953

We present the results of Sun-as-a-star observations by the NEID Solar Telescope at WIYN Observatory, spanning January 1, 2021 through June 30, 2024. We identify 117,060 observations which are unlikely to be significantly affected by weather, hardware or major calibration issues. We describe several high-level data products being made available to the community to aid in the interpretation and inter comparisons of NEID solar observations. Solar observations demonstrate excellent performance of NEID, including radial velocity (RV) accuracy and long-term stability of better than $\simeq 0.37$ m s$^{-1}$ over $\simeq 3.5$ years, even though NEID was not originally designed or optimized for daytime observations of the Sun. Currently, intrinsic stellar variability is the primary barrier to detecting Earth-analog planets for most nearby, Sun-like stars. We present a comparison of the effectiveness of several methods proposed to mitigate the effects of solar variability on the Sun's estimated RV. We find that the Scalpels algorithm performs particularly well and substantially reduces the RMS RV of solar spectra from over 2 m s$^{-1}$ to 0.277 m s$^{-1}$. Even when training on a subset of days with NEID solar observations and testing on a held-out sample, the RMS of cleaned RV is 0.34-0.42 m s$^{-1}$. This is significantly better than previous attempts at removing solar variability and suggests that the current generation of EPRV instruments are technically capable of detecting Earth-mass planets orbiting a solar twin if provided with sufficient observing time allocations ($\sim 10^3$ nights of observations)., Comment: 25 pages, 14 figures. Submitted to AAS Journals. Data release archived at https://zenodo.org/doi/10.5281/zenodo.13363761

The current public datasets for speech recognition (ASR) tend not to focus specifically on the fairness aspect, such as performance across different demographic groups. This paper introduces a novel dataset, Fair-Speech, a publicly released corpus to help researchers evaluate their ASR models for accuracy across a diverse set of self-reported demographic information, such as age, gender, ethnicity, geographic variation and whether the participants consider themselves native English speakers. Our dataset includes approximately 26.5K utterances in recorded speech by 593 people in the United States, who were paid to record and submit audios of themselves saying voice commands. We also provide ASR baselines, including on models trained on transcribed and untranscribed social media videos and open source models.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Recent work found that an analysis formalism based on the Lanczos algorithm allows energy levels to be extracted from Euclidean correlation functions with faster convergence than existing methods, two-sided error bounds, and no apparent signal-to-noise problems. We extend this formalism to the determination of matrix elements from three-point correlation functions. We demonstrate similar advantages over previously available methods in both signal-to-noise and control of excited-state contamination through example applications to noiseless mock-data as well as calculations of (bare) forward matrix elements of the strange scalar current between both ground and excited states with the quantum numbers of the nucleon., Comment: 28 pages, 16 figures

Recent observations made using the James Webb Space Telescope have identified a number of high-redshift galaxies that are unexpectedly luminous. In light of this, it is clear that a more detailed understanding of the high redshift, pre-reionization universe is required for us to obtain the complete story of galaxy formation. This study is the first in a series that seeks to tell the story of galaxy formation at $z \gtrsim 10$ using a suite of large-scale adaptive mesh refinement cosmological simulations. Our machine-learning-accelerated surrogate model for Population III star formation and feedback, StarNet, gives us an unprecedented ability to obtain physically accurate, inhomogeneous chemical initial conditions for a statistically significant number of galaxies. We find that of the 12,423 halos in the mass range of $10^6\,\,M_\odot < M_\mathrm{vir} < 10^9\,\, M_\odot$ that form in our fiducial simulation, $16\%$ are chemically enriched by Population III supernovae by $z\sim12$. We then profile and compare various cooling processes at the centers of halos, and find a complete absence of atomic cooling halos. All of our halos with central cooling gas are dominated by H$_2$ cooling, metal cooling, or a mixture of the two, even in the presence of a strong H$_2$-photodissociating Lyman-Werner background. We also find that gas accretion through the virial radius is not driven by cooling. We find that gas virialization in halos with $M_\mathrm{vir}\gtrsim10^7\,\,M_\odot$ is supported by bulk turbulent flows, and that thermal energy accounts for only a small fraction of the total kinetic energy. Because of this, the mean gas temperature is well below the virial temperature for these halos. We then compute the mass of gas that is available for Population II star formation, and infer star formation rates for each potential star-forming halo., Comment: 36 pages, 27 figures

The Chandra Source Catalog (CSC) is a virtual X-ray astrophysics facility that enables both detailed individual source studies and statistical studies of large samples of X-ray sources detected in ACIS and HRC-I imaging observations obtained by the Chandra X-ray Observatory. The catalog provides carefully-curated, high-quality, and uniformly calibrated and analyzed tabulated positional, spatial, photometric, spectral, and temporal source properties, as well as science-ready X-ray data products. The latter includes multiple types of source- and field-based FITS format products that can be used as a basis for further research, significantly simplifying followup analysis of scientifically meaningful source samples. We discuss in detail the algorithms used for the CSC Release 2 Series, including CSC 2.0, which includes 317,167 unique X-ray sources on the sky identified in observations released publicly through the end of 2014, and CSC 2.1, which adds Chandra data released through the end of 2021 and expands the catalog to 407,806 sources. Besides adding more recent observations, the CSC Release 2 Series includes multiple algorithmic enhancements that provide significant improvements over earlier releases. The compact source sensitivity limit for most observations is ~5 photons over most of the field of view, which is ~2x fainter than Release 1, achieved by co-adding observations and using an optimized source detection approach. A Bayesian X-ray aperture photometry code produces robust fluxes even in crowded fields and for low count sources. The current release, CSC 2.1, is tied to the Gaia-CRF3 astrometric reference frame for the best sky positions for catalog sources., Comment: 66 pages, 17 figures, 16 tables, accepted for publication in The Astrophysical Journal Supplement Series

Reinforcement learning algorithms typically rely on the assumption that the environment dynamics and value function can be expressed in terms of a Markovian state representation. However, when state information is only partially observable, how can an agent learn such a state representation, and how can it detect when it has found one? We introduce a metric that can accomplish both objectives, without requiring access to -- or knowledge of -- an underlying, unobservable state space. Our metric, the $\lambda$-discrepancy, is the difference between two distinct temporal difference (TD) value estimates, each computed using TD($\lambda$) with a different value of $\lambda$. Since TD($\lambda{=}0$) makes an implicit Markov assumption and TD($\lambda{=}1$) does not, a discrepancy between these estimates is a potential indicator of a non-Markovian state representation. Indeed, we prove that the $\lambda$-discrepancy is exactly zero for all Markov decision processes and almost always non-zero for a broad class of partially observable environments. We also demonstrate empirically that, once detected, minimizing the $\lambda$-discrepancy can help with learning a memory function to mitigate the corresponding partial observability. We then train a reinforcement learning agent that simultaneously constructs two recurrent value networks with different $\lambda$ parameters and minimizes the difference between them as an auxiliary loss. The approach scales to challenging partially observable domains, where the resulting agent frequently performs significantly better (and never performs worse) than a baseline recurrent agent with only a single value network., Comment: GitHub URL: https://github.com/brownirl/lambda_discrepancy; Project page: https://lambda-discrepancy.github.io/

Many recent works have explored using language models for planning problems. One line of research focuses on translating natural language descriptions of planning tasks into structured planning languages, such as the planning domain definition language (PDDL). While this approach is promising, accurately measuring the quality of generated PDDL code continues to pose significant challenges. First, generated PDDL code is typically evaluated using planning validators that check whether the problem can be solved with a planner. This method is insufficient because a language model might generate valid PDDL code that does not align with the natural language description of the task. Second, existing evaluation sets often have natural language descriptions of the planning task that closely resemble the ground truth PDDL, reducing the challenge of the task. To bridge this gap, we introduce \benchmarkName, a benchmark designed to evaluate language models' ability to generate PDDL code from natural language descriptions of planning tasks. We begin by creating a PDDL equivalence algorithm that rigorously evaluates the correctness of PDDL code generated by language models by flexibly comparing it against a ground truth PDDL. Then, we present a dataset of $132,037$ text-to-PDDL pairs across 13 different tasks, with varying levels of difficulty. Finally, we evaluate several API-access and open-weight language models that reveal this task's complexity. For example, $87.6\%$ of the PDDL problem descriptions generated by GPT-4o are syntactically parseable, $82.2\%$ are valid, solve-able problems, but only $35.1\%$ are semantically correct, highlighting the need for a more rigorous benchmark for this problem.

A transfer function approach has recently proven effective for calibrating deep learning (DL) algorithms in quantitative ultrasound (QUS), addressing data shifts at both the acquisition and machine levels. Expanding on this approach, we develop a strategy to 'steal' the functionality of a DL model from one ultrasound machine and implement it on another, in the context of QUS. This demonstrates the ease with which the functionality of a DL model can be transferred between machines, highlighting the security risks associated with deploying such models in a commercial scanner for clinical use. The proposed method is a black-box unsupervised domain adaptation technique that integrates the transfer function approach with an iterative schema. It does not utilize any information related to model internals of the victim machine but it solely relies on the availability of input-output interface. Additionally, we assume the availability of unlabelled data from the testing machine, i.e., the perpetrator machine. This scenario could become commonplace as companies begin deploying their DL functionalities for clinical use. Competing companies might acquire the victim machine and, through the input-output interface, replicate the functionality onto their own machines. In the experiments, we used a SonixOne and a Verasonics machine. The victim model was trained on SonixOne data, and its functionality was then transferred to the Verasonics machine. The proposed method successfully transferred the functionality to the Verasonics machine, achieving a remarkable 98\% classification accuracy in a binary decision task. This study underscores the need to establish security measures prior to deploying DL models in clinical settings., Comment: 8 pages, 4 figures, 4 tables

Over one in five adults in the US lives with a mental illness. In the face of a shortage of mental health professionals and offline resources, online short-form video content has grown to serve as a crucial conduit for disseminating mental health help and resources. However, the ease of content creation and access also contributes to the spread of misinformation, posing risks to accurate diagnosis and treatment. Detecting and understanding engagement with such content is crucial to mitigating their harmful effects on public health. We perform the first quantitative study of the phenomenon using YouTube Shorts and Bitchute as the sites of study. We contribute MentalMisinfo, a novel labeled mental health misinformation (MHMisinfo) dataset of 739 videos (639 from Youtube and 100 from Bitchute) and 135372 comments in total, using an expert-driven annotation schema. We first found that few-shot in-context learning with large language models (LLMs) are effective in detecting MHMisinfo videos. Next, we discover distinct and potentially alarming linguistic patterns in how audiences engage with MHMisinfo videos through commentary on both video-sharing platforms. Across the two platforms, comments could exacerbate prevailing stigma with some groups showing heightened susceptibility to and alignment with MHMisinfo. We discuss technical and public health-driven adaptive solutions to tackling the "epidemic" of mental health misinformation online., Comment: 12 pages, in submission to ICWSM

Evidence to guide healthcare decisions is often limited by a lack of relevant and trustworthy literature as well as difficulty in contextualizing existing research for a specific patient. Large language models (LLMs) could potentially address both challenges by either summarizing published literature or generating new studies based on real-world data (RWD). We evaluated the ability of five LLM-based systems in answering 50 clinical questions and had nine independent physicians review the responses for relevance, reliability, and actionability. As it stands, general-purpose LLMs (ChatGPT-4, Claude 3 Opus, Gemini Pro 1.5) rarely produced answers that were deemed relevant and evidence-based (2% - 10%). In contrast, retrieval augmented generation (RAG)-based and agentic LLM systems produced relevant and evidence-based answers for 24% (OpenEvidence) to 58% (ChatRWD) of questions. Only the agentic ChatRWD was able to answer novel questions compared to other LLMs (65% vs. 0-9%). These results suggest that while general-purpose LLMs should not be used as-is, a purpose-built system for evidence summarization based on RAG and one for generating novel evidence working synergistically would improve availability of pertinent evidence for patient care., Comment: 28 pages (2 figures, 3 tables) inclusive of 8 pages of supplemental materials (4 supplemental figures and 4 supplemental tables)

This work introduces a method for determining the energy spectrum of lattice quantum chromodynamics (LQCD) by applying the Lanczos algorithm to the transfer matrix and using a bootstrap generalization of the Cullum-Willoughby method to filter out spurious eigenvalues. Proof-of-principle analyses of the simple harmonic oscillator and the LQCD proton mass demonstrate that this method provides faster ground-state convergence than the "effective mass," which is related to the power-iteration algorithm. Lanczos provides more accurate energy estimates than multi-state fits to correlation functions with small imaginary times while achieving comparable statistical precision. Two-sided error bounds are computed for Lanczos results and guarantee that excited-state effects cannot shift Lanczos results far outside their statistical uncertainties. Further, Lanczos results avoid the exponential signal-to-noise degradation present in he power-iteration method / effective mass., Comment: Improved spurious eigenvalue filtering, analysis of large-iteration behavior, presentation, and comparison with multi-state fits

Over a quarter of white dwarfs have photospheric metal pollution, which is evidence for recent accretion of exoplanetary material. While a wide range of mechanisms have been proposed to account for this pollution, there are currently few observational constraints to differentiate between them. To investigate the driving mechanism, we observe a sample of polluted and non-polluted white dwarfs in wide binary systems with main-sequence stars. Using the companion stars' metallicities as a proxy for the white dwarfs' primordial metallicities, we compare the metallicities of polluted and non-polluted systems. Because there is a well-known correlation between giant planet occurrence and higher metallicity (with a stronger correlation for close-in and eccentric planets), these metallicity distributions can be used to probe the role of gas giants in white dwarf accretion. We find that the metallicity distributions of polluted and non-polluted systems are consistent with the hypothesis that both samples have the same underlying metallicity distribution. However, we note that this result is likely biased by several selection effects. Additionally, we find no significant trend between white dwarf accretion rates and metallicity. These findings suggest that giant planets are not the dominant cause of white dwarf accretion events in binary systems., Comment: 17 pages, 9 figures, accepted to MNRAS

Reliable control of quantum information in matter-based qubits requires precisely applied external fields, and unaccounted for spatial cross-talk of these fields between adjacent qubits leads to loss of fidelity. We report a CMOS foundry-produced, micro-fabricated silicon nitride (Si3N4) optical waveguide for addressing a chain of eight, unequally-spaced trapped barium ions with crosstalk compatible with scalable quantum information processing. The crosstalk mitigation techniques incorporated into the chip design result in a reduction of the measured optical field by at least 50.8(1.3) dB between adjacent waveguide outputs near 650 nm and similar behavior for devices designed for 493 nm and 585 nm. The waveguide outputs near 650 nm, along with a global laser near 493 nm were used to laser-cool a chain of eight barium-138 ions, and a camera imaged the resulting fluorescence at 493 nm., Comment: 9 pages, 7 figures