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With close to three years of observations in hand, the NEID Earth Twin Survey (NETS) is starting to unearth new astrophysical signals for a curated sample of bright, radial velocity (RV)-quiet stars. We present the discovery of the first NETS exoplanet, HD 86728 b, a $m_p\sin i = 9.16^{+0.55}_{-0.56}\ \rm{M}_\oplus$ planet on a circular, $P=31.1503^{+0.0062}_{-0.0066}$ d orbit, thereby confirming a candidate signal identified by Hirsch et al. (2021). We confirm the planetary origin of the detected signal, which has a semi-amplitude of just $K=1.91^{+0.11}_{-0.12}$ m s$^{-1}$, via careful analysis of the NEID RVs and spectral activity indicators, and we constrain the mass and orbit via fits to NEID and archival RV measurements. The host star is intrinsically quiet at the $\sim1$ m s$^{-1}$ level, with the majority of this variability likely stemming from short-timescale granulation. HD 86728 b is among the small fraction of exoplanets with similar masses and periods that have no known planetary siblings., Comment: Submitted to AAS Journals. 18 pages, 10 figures, 3 tables, 1 appendix

A significant barrier to achieving the radial velocity (RV) measurement accuracy and precision required to characterize terrestrial mass exoplanets is the existence of time-variable features in the measured spectra, from both telluric absorption and stellar variability, which affect measured line shapes and can cause apparent RV shifts. Reaching the desired accuracy using traditional techniques often requires avoiding lines contaminated by stellar variability and/or changing tellurics, and thus discarding a large fraction of the spectrum, lowering precision. New data-driven methods can help achieve extremely precise and accurate RVs by enabling the use of a larger fraction of the available data. While there exist methods for modeling telluric features or the stellar variability individually, there is a need for additional tools that are capable of modeling them simultaneously at the spectral level. Here we present StellarSpectraObservationFitting.jl (SSOF), a Julia package for measuring Doppler shifts and creating data-driven models (with fast, physically-motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum, while accounting for the wavelength-dependent instrumental line-spread function. We demonstrate SSOF's state-of-the-art performance on data from the NEID RV spectrograph on the WIYN 3.5m Telescope for multiple stars. We show SSOF's, ability to accurately identify and characterize spectral variability and provide $\sim$2-6x smaller photon-limited errors over the NEID CCF-based pipeline and match the performance of SERVAL, a leading template-based pipeline, using only observed EPRV spectra., Comment: 47 pages, 21 figures, 7 tables. In revision for AAS Journals. Code at https://github.com/christiangil/StellarSpectraObservationFitting.jl

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

For solar-type stars, spots and their associated magnetic regions induce radial velocity perturbations through the Doppler rotation signal and the suppression of convective blueshift -- collectively known as rotation-modulation. We developed the Rotation-Convection (RC) model: a method of detrending and characterizing rotation-modulation, using only cross-correlation functions or 1-dimensional spectra, without the need for continuous high cadence measurements. The RC method uses a simple model for the anomalous radial velocity induced by an active region and has two inputs: stellar flux (or a flux proxy) and the relative radial velocity between strongly and weakly absorbed wavelengths (analogous to the bisector-inverse slope). On NEID solar data (three month baseline), the RC model lowers the amplitude of rotationally-modulated stellar activity to below the meter-per-second level. For the standard star HD 26965, the RC model detrends the activity signal to the meter-per-second level for HARPS, EXPRES, and NEID observations, even though the temporal density and timespan of the observations differs by an order of magnitude between the three datasets. In addition to detrending, the RC model also characterizes the rotation-modulation signal. From comparison with the Solar Dynamics Observatory, we confirmed that the model accurately recovers and separates the rotation and convection radial velocity components. We also mapped the amplitude of the rotation and convection perturbations as a function of height within the stellar atmosphere. Probing stellar atmospheres with our revised spot model will fuel future innovations in stellar activity mitigation, enabling robust exoplanet detection., Comment: Accepted to AJ, 22 pages, 9 figures

Speculative decoding has emerged as a widely adopted method to accelerate large language model inference without sacrificing the quality of the model outputs. While this technique has facilitated notable speed improvements by enabling parallel sequence verification, its efficiency remains inherently limited by the reliance on incremental token generation in existing draft models. To overcome this limitation, this paper proposes an adaptation of speculative decoding which uses discrete diffusion models to generate draft sequences. This allows parallelization of both the drafting and verification steps, providing significant speed-ups to the inference process. Our proposed approach, Speculative Diffusion Decoding (SpecDiff), is validated on standard language generation benchmarks and empirically demonstrated to provide a up to 8.7x speed-up over standard generation processes and up to 2.5x speed-up over existing speculative decoding approaches.

This paper introduces an approach to endow generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles. The proposed method recast the traditional sampling process of generative diffusion models as a constrained optimization problem, steering the generated data distribution to remain within a specified region to ensure adherence to the given constraints. These capabilities are validated on applications featuring both convex and challenging, non-convex, constraints as well as ordinary differential equations, in domains spanning from synthesizing new materials with precise morphometric properties, generating physics-informed motion, optimizing paths in planning scenarios, and human motion synthesis.

The photoexcitation dynamics of molecular materials on the 10-100 nm length scale depend on complex interactions between the electronic and vibrational degrees of freedom, rendering exact calculations difficult or intractable. The adaptive Hierarchy of Pure States (adHOPS) is a formally exact method that leverages the locality imposed by interactions between thermal environments and electronic excitations to achieve size-invariant scaling calculations for single-excitation processes in systems described by a Frenkel-Holstein Hamiltonian. Here, we extend adHOPS to account for arbitrary couplings between the thermal environments and vertical excitation energies, enabling formally exact, size-invariant calculations that involve multiple excitations or states with shared thermal environments. In addition, we introduce a low-temperature correction and effective integration of the noise to reduce the computational expense of including ultrafast vibrational relaxation in HOPS simulations. We present these advances in the latest version of the open-source MesoHOPS library and use MesoHOPS to characterize charge separation at a one-dimensional organic heterojunction when both the electron and hole are mobile., Comment: 27 pages, 15 figures

Contrast set consistency is a robustness measurement that evaluates the rate at which a model correctly responds to all instances in a bundle of minimally different examples relying on the same knowledge. To draw additional insights, we propose to complement consistency with relative consistency -- the probability that an equally accurate model would surpass the consistency of the proposed model, given a distribution over possible consistencies. Models with 100% relative consistency have reached a consistency peak for their accuracy. We reflect on prior work that reports consistency in contrast sets and observe that relative consistency can alter the assessment of a model's consistency compared to another. We anticipate that our proposed measurement and insights will influence future studies aiming to promote consistent behavior in models., Comment: BlackboxNLP 2023 accepted paper camera-ready version; 6 pages main, 3 pages appendix

Electrical capacitance tomography (ECT) is a nonoptical imaging technique in which a map of the interior permittivity of a volume is estimated by making capacitance measurements at its boundary and solving an inverse problem. While previous ECT demonstrations have often been at centimeter scales, ECT is not limited to macroscopic systems. In this paper, we demonstrate ECT imaging of polymer microspheres and bacterial biofilms using a CMOS microelectrode array, achieving spatial resolution of 10 microns. Additionally, we propose a deep learning architecture and an improved multi-objective training scheme for reconstructing out-of-plane permittivity maps from the sensor measurements. Experimental results show that the proposed approach is able to resolve microscopic 3-D structures, achieving 91.5% prediction accuracy on the microsphere dataset and 82.7% on the biofilm dataset, including an average of 4.6% improvement over baseline computational methods.

We introduce a definition of multivariate majorization that is new to the economics literature. Our majorization technique allows us to generalize Mussa and Rosen's (1978) "ironing" to a broad class of multivariate principal-agents problems. Specifically, we consider adverse selection problems in which agents' types are one dimensional but informational externalities create a multidimensional ironing problem. Our majorization technique applies to discrete and continuous type spaces alike and we demonstrate its usefulness for contract theory and mechanism design. We further show that multivariate majorization yields a natural extension of second-order stochastic dominance to multiple dimensions and derive its implications for decision making under multivariate risk.

We establish the Subgradient Theorem for monotone correspondences -- a monotone correspondence is equal to the subdifferential of a potential if and only if it is conservative, i.e. its integral along a closed path vanishes irrespective of the selection from the correspondence along the path. We prove two attendant results: the Potential Theorem, whereby a conservative monotone correspondence can be integrated up to a potential, and the Duality Theorem, whereby the potential has a Fenchel dual whose subdifferential is another conservative monotone correspondence. We use these results to reinterpret and extend Baldwin and Klemperer's (2019) characterization of demand in economies with indivisible goods. We introduce a simple test for existence of Walrasian equilibrium in quasi-linear economies. Fenchel's Duality Theorem implies this test is met when the aggregate utility is concave, which is not necessarily the case with indivisible goods even if all consumers have concave utilities.

In economics, risk aversion is modeled via a concave Bernoulli utility within the expected-utility paradigm. We propose a simple test of expected utility and concavity. We find little support for either: only 30 percent of the choices are consistent with a concave utility, only two out of 72 subjects are consistent with expected utility, and only one of them fits the economic model of risk aversion. Our findings contrast with the preponderance of seemingly "risk-averse" choices that have been elicited using the popular multiple-price list methodology, a result we replicate in this paper. We demonstrate that this methodology is unfit to measure risk aversion, and that the high prevalence of risk aversion it produces is due to parametric misspecification.

$S$ equilibrium synthesizes a century of game-theoretic modeling. $S$-beliefs determine choices as in the refinement literature and level-$k$, without anchoring on Nash equilibrium or imposing ad hoc belief formation. $S$-choices allow for mistakes as in QRE, without imposing rational expectations. $S$ equilibrium is explicitly set-valued to avoid the common practice of selecting the best prediction from an implicitly defined set of unknown, and unaccounted for, size. $S$-equilibrium sets vary with a complexity parameter, offering a trade-off between accuracy and precision unlike in $M$ equilibrium. Simple "areametrics" determine the model's parameter and show that choice sets with a relative size of 5 percent capture 58 percent percent of the data. Goodness-of-fit tests applied to data from a broad array of experimental games confirm $S$ equilibrium's ability to predict behavior in and out of sample. In contrast, choice (belief) predictions of level-$k$ and QRE are rejected in most (all) games.

General equilibrium, the cornerstone of modern economics and finance, rests on assumptions many markets do not meet. Spectrum auctions, electricity markets, and cap-and-trade programs for resource rights often feature non-convexities in preferences or production that can cause non-existence of Walrasian equilibrium and render general equilibrium vacuous. Yquilibrium complements general equilibrium with an optimization approach to (non-) convex economies that does not require perfect competition. Yquilibrium coincides with Walrasian equilibrium when the latter exists. Yquilibrium exists even if Walrasian equilibrium ceases to and produces optimal allocations subject to linear, anonymous, and (approximately) utility-clearing prices.

The photosynthetic apparatus of plants and bacteria combine atomically precise pigment-protein complexes with dynamic membrane architectures to control energy transfer on the 10-100 nm length scales. Recently, synthetic materials have integrated photosynthetic antennae proteins to enhance exciton transport, though the influence of artificial packing on the excited-state dynamics in these biohybrid materials remains unclear. Here, we use the adaptive Hierarchy of Pure States (adHOPS) to perform a formally exact simulation of excitation energy transfer within artificial aggregates of light harvesting complex 2 (LH2) with a range of packing densities. We find that LH2 aggregates support a remarkable exciton diffusion length ranging from 100 nm at a biological packing density to 300 nm at the densest packing previously suggested in an artificial aggregate. The unprecedented scale of these calculations also underscores the efficiency with which adHOPS simulates excited-state processes in realistic molecular materials., Comment: 9 pages, 4 figures

I introduce a concave function of allocations and prices -- the economy's potential -- which measures the difference between utilitarian social welfare and its dual. I show that Walrasian equilibria correspond to roots of the potential: allocations maximize weighted utility and prices minimize weighted indirect utility. Walrasian prices are "utility clearing" in the sense that the utilities consumers expect at Walrasian prices are just feasible. I discuss the implications of this simple duality for equilibrium existence, the welfare theorems, and the interpretation of Walrasian prices.

We report the results of observations of p-mode oscillations in the G0 subgiant star HD 35833 in both radial velocities and photometry with NEID and TESS, respectively. We achieve separate, robust detections of the oscillation signal with both instruments (radial velocity amplitude $A_{\rm RV}=1.11\pm0.09$ m s$^{-1}$, photometric amplitude $A_{\rm phot}=6.42\pm0.60$ ppm, frequency of maximum power $\nu_{\rm max} = 595.71\pm17.28$ $\mu$Hz, and mode spacing $\Delta \nu = 36.65\pm0.96$ $\mu$Hz) as well as a non-detection in a TESS sector concurrent with the NEID observations. These data shed light on our ability to mitigate the correlated noise impact of oscillations with radial velocities alone, and on the robustness of commonly used asteroseismic scaling relations. The NEID data are used to validate models for the attenuation of oscillation signals for exposure times $t<\nu_{\rm max}^{-1}$, and we compare our results to predictions from theoretical scaling relations and find that the observed amplitudes are weaker than expected by $>4\sigma$, hinting at gaps in the underlying physical models., Comment: 19 Pages, 14 Figures, Appendix

HD 166620 was recently identified as a Maunder Minimum candidate based on nearly 50 years of Ca II H & K activity data from Mount Wilson and Keck-HIRES (Baum et al. 2022). These data showed clear cyclic behavior on a 17-year timescale during the Mount Wilson survey that became flat when picked up later with Keck-HIRES planet-search observations. Unfortunately, the transition between these two data sets -- and therefore the transition into the candidate Maunder Minimum phase -- contained little to no data. Here we present additional Mount Wilson data not present in Baum et al. (2022) along with photometry over a nearly 30-year baseline that definitively trace the transition from cyclic activity to a prolonged phase of flat activity. We present this as conclusive evidence of the star entering a grand magnetic minimum and therefore the first true Maunder Minimum analog. We further show that neither the overall brightness nor the chromospheric activity level (as measured by S$_{\mathrm{HK}}$) is significantly lower during the grand magnetic minimum than its activity cycle minimum, implying that anomalously low mean or instantaneous activity levels are not a good diagnostic or criterion for identifying additional Maunder Minimum candidates. Intraseasonal variability in S$_{\mathrm{HK}}$, however, is lower in the star's grand minimum; this may prove a useful symptom of the phenomenon., Comment: 6 pages, 1 figure, submitted to ApJL

Objective: We developed a hybrid volume surface integral equation (VSIE) method based on domain decomposition to perform fast and accurate magnetic resonance imaging (MRI) simulations that include both remote and local conductive elements. Methods: We separated the conductive surfaces present in MRI setups into two domains and optimized electromagnetic (EM) modeling for each case. Specifically, interactions between the body and EM waves originating from local radiofrequency (RF) coils were modeled with the precorrected fast Fourier transform, whereas the interactions with remote conductive surfaces (RF shield, scanner bore) were modeled with a novel cross tensor train-based algorithm. We compared the hybrid- VSIE with other VSIE methods for realistic MRI simulation setups. Results: The hybrid-VSIE was the only practical method for simulation using 1 mm voxel isotropic resolution (VIR). For 2 mm VIR, our method could be solved at least 23 times faster and required 760 times lower memory than traditional VSIE methods. Conclusion: The hybrid-VSIE demonstrated a marked improvement in terms of convergence times of the numerical EM simulation compared to traditional approaches in multiple realistic MRI scenarios. Significance: The efficiency of the novel hybrid-VSIE method could enable rapid simulations of complex and comprehensive MRI setups., Comment: 10 pages, 6 figures