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The radial velocity catalog from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) is unique in its simultaneously large volume and high precision as a result of its decade-long survey duration, multiplexing (600 fibers), and spectral resolution of $R \sim 22,500$. However, previous data reductions of APOGEE have not fully realized the potential radial velocity (RV) precision of the instrument. Here we present an RV catalog based on a new reduction of all 2.6 million visits of APOGEE DR17 and validate it against improved estimates for the theoretical RV performance. The core ideas of the new reduction are the simultaneous modeling of all components in the spectra, rather than a separate subtraction of point estimates for the sky, and a marginalization over stellar types, rather than a grid search for an optimum. We show that this catalog, when restricted to RVs measured with the same fiber, achieves noise-limited precision down to 30 m/s and delivers well-calibrated uncertainties. We also introduce a general method for calibrating fiber-to-fiber constant RV offsets and demonstrate its importance for high RV precision work in multi-fiber spectrographs. After calibration, we achieve 47 m/s RV precision on the combined catalog with RVs measured with different fibers. This degradation in precision relative to measurements with only a single fiber suggests that refining line spread function models should be a focus in SDSS-V to improve the fiber-unified RV catalog., Comment: 22 pages, 13 figures, SDSS-V Technical Paper, Submitted to AJ

We investigate the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we perform a systematic visual inspection of the $0.7\,\rm{deg}^2$ Euclid ERO data towards the Perseus cluster using both the high-resolution VIS $I_{\scriptscriptstyle\rm E}$ band, and the lower resolution NISP bands. We inspect every extended source brighter than magnitude $23$ in $I_{\scriptscriptstyle\rm E}$ with $41$ expert human classifiers. This amounts to $12\,086$ stamps of $10^{\prime\prime}\,\times\,10^{\prime\prime}$. We find $3$ grade A and $13$ grade B candidates. We assess the validity of these $16$ candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full $14\,000\,{\rm deg}^2$ of the Euclid Wide Survey should contain $100\,000^{+70\,000}_{-30\,000}$ galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of $170\,000$ discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range $0.\!\!^{\prime\prime}68\,<\,\theta_\mathrm{E}\,<1.\!\!^{\prime\prime}24$, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small Einstein radius systems. Whilst it is implausible to visually inspect the full Euclid data set, our results corroborate the promise that Euclid will ultimately deliver a sample of around $10^5$ galaxy-scale lenses., Comment: 21 pages, 20 figures, submitted to A&A

We show that any $SU(2)$ BPS monopole of charge $k$ with rotational spatial dihedral symmetry is gauge equivalent to the Nahm data obtained from affine Toda equations of $C_l^{(1)}$ type when $k=2l$ or $A_{2(l-1)}^{(2)}$ type when $k=2l-1$., Comment: 22 pages; 2 figures

A spin-photon interface is one of the key components of a quantum network. Physical platforms under investigation span the range of modern experimental physics, from ultra-cold atoms and ions to a variety of solid-state systems. Each system has its strengths and weaknesses, typically with a trade-off between spin properties and photonic properties. Currently, the best deterministic single-photon sources use a semiconductor quantum dot embedded in an optical microcavity. However, coherent spin control has not yet been integrated with a state-of-the-art single-photon source, and the magnetic noise from host nuclear spins in the semiconductor environment has placed strong limitations on the spin coherence. Here, we combine high-fidelity all-optical spin control with a quantum dot in an open microcavity, currently the most efficient single-photon source platform available. By imprinting a microwave signal onto a red-detuned optical field, a Raman process, we demonstrate coherent rotations of a hole spin around an arbitrary axis of the Bloch sphere, achieving a maximum {\pi}-pulse fidelity of 98.6%. The cavity enhances the Raman process, enabling ultra-fast Rabi frequencies above 1 GHz. We use our flexible spin control to perform optical cooling of the nuclear spins in the host material via the central hole spin, extending the hole-spin coherence time T2* from 28 ns to 535 ns. Hahn echo preserves the spin coherence on a timescale of 20 {\mu}s, and dynamical decoupling extends the coherence close to the relaxation limit. Both the spin T2* and spin rotation time are much larger than the Purcell-enhanced radiative recombination time, 50 ps, enabling many spin-photon pairs to be created before the spin loses its coherence., Comment: 15 pages, 3 main figures, 6 supplementary figures

Galaxies are not in a dynamical steady state. They continually undergo perturbations, e.g., from infalling dwarf galaxies and dark-matter substructure. After a dynamical perturbation, stars phase mix towards a new steady state; in so doing they generally form spiral structures, such as spiral density waves in galaxy disks and the Gaia Snail observed in the vertical phase-space density in the solar neighborhood. Structures in phase-space density can be hard to measure accurately, because spatially varying selection effects imprint their own patterns on the density. However, stellar labels such as metallicity, or other element abundances, or stellar masses and ages, can be measured even in the face of complex or unknown spatial selection functions. We show that if the equilibrium galaxy has phase-space gradients in these labels, any perturbation that could raise a spiral wave in the phase-space density will raise a spiral wave in the distribution of labels as well. We work out the relationship between the spiral patterns in the density and in the labels. As an example, we analyze the Gaia Snail and show that its amplitude and dynamical age as derived from elemental abundances (mainly [Mg/Fe]) follow similar patterns to those derived from the phase-space density. Our best model dates the Snail's perturbation to about 400 Myr ago although we find significant variations with angular momentum in the best-fit age. Conceptually, the ideas presented here are related to Orbital Torus Imaging, chemical tagging, and other methods that use stellar labels to trace dynamics., Comment: Submitted to ApJ, comments are welcome

Generic open quantum systems are notoriously difficult to simulate unless one looks at specific regimes. In contrast, classical dissipative systems can often be effectively described by stochastic processes, which are generally less computationally expensive. Here, we use the paradigmatic case of a dissipative quantum oscillator to give a pedagogic introduction into the modelling of open quantum systems using quasiclassical methods, i.e. classical stochastic methods that use a 'quantum' noise spectrum to capture the influence of the environment on the system. Such quasiclassical methods have the potential to offer insights into the impact of the quantum nature of the environment on the dynamics of the system of interest whilst still being computationally tractable., Comment: Comments welcome

We study the generation of fermion mass in a context where interactions break a discrete chiral symmetry. Then, fermion mass is not protected by a symmetry, no symmetry is broken by the generation of mass, and a vanishing mass no longer enhances a symmetry. We elaborate these scenarios for template fermionic and Yukawa theories in three dimensions where mass can be generated either by fluctuations, strong dynamics, or the vacuum expectation value of a scalar field. We find that fluctuation-induced contributions to fermion mass are parametrically suppressed in the number of fermion flavours $N$. The generation of fermion mass then takes the form of a rapid crossover which turns into a second order quantum phase transition for large $N$, much like in settings with fundamental chiral symmetry. We further discuss theories where fermion mass can be generated spontaneously without breaking any symmetry other than scale symmetry. Implications of our findings are discussed., Comment: 21 pages, 6 figures; v2: 2 figures and clarifications added, version accepted for publication in PRD

Differentiating between real transit events and false positive signals in photometric time series data is a bottleneck in the identification of transiting exoplanets, particularly long-period planets. This differentiation typically requires visual inspection of a large number of transit-like signals to rule out instrumental and astrophysical false positives that mimic planetary transit signals. We build a one-dimensional convolutional neural network (CNN) to separate eclipsing binaries and other false positives from potential planet candidates, reducing the number of light curves that require human vetting. Our CNN is trained using the TESS light curves that were identified by Planet Hunters citizen scientists as likely containing a transit. We also include the background flux and centroid information. The light curves are visually inspected and labeled by project scientists and are minimally pre-processed, with only normalization and data augmentation taking place before training. The median percentage of contaminants flagged across the test sectors is 18% with a maximum of 37% and a minimum of 10%. Our model keeps 100% of the planets for 16 of the 18 test sectors, while incorrectly flagging one planet candidate (0.3%) for one sector and two (0.6%) for the remaining sector. Our method shows potential to reduce the number of light curves requiring manual vetting by up to a third with minimal misclassification of planet candidates., Comment: Under review at The Astronomical Journal

Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they amplify confirmation biases. For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics., Comment: A Position Paper accepted for publication in the 2024 International Conference on Machine Learning (ICML)

Spatial reasoning plays a vital role in both human cognition and machine intelligence, prompting new research into language models' (LMs) capabilities in this regard. However, existing benchmarks reveal shortcomings in evaluating qualitative spatial reasoning (QSR). These benchmarks typically present oversimplified scenarios or unclear natural language descriptions, hindering effective evaluation. We present a novel benchmark for assessing QSR in LMs, which is grounded in realistic 3D simulation data, offering a series of diverse room layouts with various objects and their spatial relationships. This approach provides a more detailed and context-rich narrative for spatial reasoning evaluation, diverging from traditional, toy-task-oriented scenarios. Our benchmark encompasses a broad spectrum of qualitative spatial relationships, including topological, directional, and distance relations. These are presented with different viewing points, varied granularities, and density of relation constraints to mimic real-world complexities. A key contribution is our logic-based consistency-checking tool, which enables the assessment of multiple plausible solutions, aligning with real-world scenarios where spatial relationships are often open to interpretation. Our benchmark evaluation of advanced LMs reveals their strengths and limitations in spatial reasoning. They face difficulties with multi-hop spatial reasoning and interpreting a mix of different view descriptions, pointing to areas for future improvement., Comment: Camera-Ready version for IJCAI 2024

The analysis of strong lensing images usually involves an external convergence and shear, which are meant to model the effect of perturbations along the line of sight, on top of the main lens. Such a description of line-of-sight perturbations supposes that the corresponding gravitational fields can be treated in the tidal regime. Going one step further introduces additional effects, known as flexion, which have been hitherto neglected in strong lensing. In this work, we build a minimal model for the line-of-sight flexion, which adds four new complex parameters to the lens model. Contrary to convergence and shear, the line-of-sight flexion cannot be projected onto the main lens plane. For a $\Lambda$CDM cosmology, we predict the typical line-of-sight flexion to be on the order of $10^{-3} \mathrm{arcsec}^{-1}$ on galactic scales. Neglecting its effect in lens modelling is found to bias the recovery of other parameters; in particular, the line-of-sight shear can be biased up to $2\sigma$. Accounting for the line-of-sight flexion in our minimal framework restores accuracy, at the the cost of degrading precision. With current imaging capabilities, the line-of-sight flexion is unlikely to be measurable on individual strong lensing images; it must therefore be considered a nuisance parameter rather than an observable in its own right., Comment: 21 + 5 pages, 12 figures. v2: added a citation. v3: minor adjustments

We report on the discovery and validation of a transiting long-period mini-Neptune orbiting a bright (V = 9.0 mag) G dwarf (TOI 4633; R = 1.05 RSun, M = 1.10 MSun). The planet was identified in data from the Transiting Exoplanet Survey Satellite by citizen scientists taking part in the Planet Hunters TESS project. Modeling of the transit events yields an orbital period of 271.9445 +/- 0.0040 days and radius of 3.2 +/- 0.20 REarth. The Earth-like orbital period and an incident flux of 1.56 +/- 0.2 places it in the optimistic habitable zone around the star. Doppler spectroscopy of the system allowed us to place an upper mass limit on the transiting planet and revealed a non-transiting planet candidate in the system with a period of 34.15 +/- 0.15 days. Furthermore, the combination of archival data dating back to 1905 with new high angular resolution imaging revealed a stellar companion orbiting the primary star with an orbital period of around 230 years and an eccentricity of about 0.9. The long period of the transiting planet, combined with the high eccentricity and close approach of the companion star makes this a valuable system for testing the formation and stability of planets in binary systems., Comment: 24 pages, 16 figures, 4 tables

The theta characteristics on a Riemann surface are permuted by the induced action of the automorphism group, with the orbit structure being important for the geometry of the curve and associated manifolds. We describe two new methods for advancing the understanding of these orbits, generalising existing results of Kallel & Sjerve, allowing us to establish the existence of infinitely many curves possessing a unique invariant characteristic as well as determine the number of invariant characteristics for all Hurwitz curves with simple automorphism group. In addition, we compute orbit decompositions for a substantial number of curves with genus $\leq 9$, allowing the identification of where current theoretical understanding falls short and the potential applications of machine learning techniques., Comment: 32 pages

Analysis of microwave sky signals, such as the cosmic microwave background, often requires component separation with multi-frequency methods, where different signals are isolated by their frequency behaviors. Many so-called "blind" methods, such as the internal linear combination (ILC), make minimal assumptions about the spatial distribution of the signal or contaminants, and only assume knowledge of the frequency dependence of the signal. The ILC is a minimum-variance linear combination of the measured frequency maps. In the case of Gaussian, statistically isotropic fields, this is the optimal linear combination, as the variance is the only statistic of interest. However, in many cases the signal we wish to isolate, or the foregrounds we wish to remove, are non-Gaussian and/or statistically anisotropic (in particular for Galactic foregrounds). In such cases, it is possible that machine learning (ML) techniques can be used to exploit the non-Gaussian features of the foregrounds and thereby improve component separation. However, many ML techniques require the use of complex, difficult-to-interpret operations on the data. We propose a hybrid method whereby we train an ML model using only combinations of the data that $\textit{do not contain the signal}$, and combine the resulting ML-predicted foreground estimate with the ILC solution to reduce the error from the ILC. We demonstrate our methods on simulations of extragalactic temperature and Galactic polarization foregrounds, and show that our ML model can exploit non-Gaussian features, such as point sources and spatially-varying spectral indices, to produce lower-variance maps than ILC - eg, reducing the variance of the B-mode residual by factors of up to 5 - while preserving the signal of interest in an unbiased manner. Moreover, we often find improved performance when applying our model to foreground models on which it was not trained., Comment: 22 pages, 13 figures. v2 has some updated references

Food insecurity is widespread among college students in the United States. Food benefits delivered through the CalFresh program, California's version of the federal Supplemental Nutrition Assistance Program (SNAP), can reduce hunger by helping students pay for groceries, but may not reach all eligible students. To date, higher education systems have lacked good estimates of the share of their students who are eligible for CalFresh and the share who actually receive benefits. To address this information gap, the California Policy Lab (CPL) partnered with the California Community College (CCC) Chancellor's Office, the University of California Office of the President (UCOP), the California Department of Social Services (CDSS), and the California Student Aid Commission (CSAC) to build a linked database of student-level administrative data on college enrollment, financial aid, and CalFresh participation. This database covers all students enrolled at CCC or UC campuses from academic years 2010-11 through 2021-22, along with corresponding FAFSA submissions and CalFresh participation. Using these data, we are able to measure how many college students are likely eligible for CalFresh, and of those how many participate. CalFresh eligibility does not perfectly overlap with student need. Some students who are food insecure may not be eligible for benefits, while some eligible students may not be in great need. This in part reflects the rules of the program, which are designed to capture need but do not always do so perfectly. We attempt to measure eligibility according to the actual rules. Our estimates complement other work that uses survey data to measure students' basic needs (California Community Colleges League, 2022). We estimate that in Fall 2019, on the eve of the COVID pandemic, 16% of California community college students (256,000 students), 31% of UC undergraduate students (69,000 students), and 6% of UC graduate students (3,000 students) were likely eligible for CalFresh benefits. However, the majority of these students did not receive benefits -- only 30% of eligible community college students, 22% of eligible UC undergraduates, and 29% of eligible UC graduate students were actually enrolled in CalFresh. We emphasize that our eligibility determinations are estimates based on information available in existing data, which imperfectly capture some elements of the CalFresh eligibility determination process. They may somewhat overstate or understate student eligibility. However, extensive investigation led us to conclude that the errors are likely not large and that our estimates are a good approximation of the share of students who would be found eligible under individualized determinations. As we discuss below, the higher eligibility rate among UC undergraduates as compared to CCC students reflects program rules that make it easier for UC students to qualify than for CCC students with similar resources. We also find differences in eligibility across groups of students within each segment, reflecting both variation in need and program rules. Among both CCC and UC students, Black and Hispanic students, for example, are more likely to be eligible than are White or Asian American students, and students receiving federal, state, or institutional financial aid are much more likely to be eligible than are students not receiving aid. Benefits receipt generally mirrors this, although there are differences across groups in the share of eligible students who receive benefits. The take-up rate, which is the share of eligible students who participate in CalFresh, is higher for Black students (and, at the UC but not at CCCs, Hispanic students) than for White students, and for students on financial aid than for students who are not. Our report indicates there is much room to improve CalFresh participation among eligible students. Through detailed analyses of the paths to eligibility and of variation in participation rates, we hope to shed light on opportunities for policymakers, higher education administrators, community-based organizations, student groups, and advocates to better connect eligible students to benefits. [Additional support was provided by the Woven Foundation.]

Food insecurity is widespread among college students in the United States. CalFresh food benefits, known federally as the Supplemental Nutrition Assistance Program, or SNAP, can help students in California pay for food, but may not reach all eligible students. To better measure student participation in CalFresh, the California Policy Lab (CPL) partners with the California Community College system (CCC), the University of California Office of the President (UCOP), the California Department of Social Services (CDSS), and the California Student Aid Commission (CSAC). Through this partnership, CPL has created a linked database of student-level administrative data on college enrollment, financial aid, and CalFresh participation that makes this analysis possible.

Food insecurity is widespread among college students in the United States. CalFresh food benefits, known federally as the Supplemental Nutrition Assistance Program, or SNAP, can help students in California pay for food, but may not reach all eligible students. To better measure student participation in CalFresh, the California Policy Lab (CPL) partners with the California Community College system (CCC), the University of California Office of the President (UCOP), the California Department of Social Services (CDSS), and the California Student Aid Commission (CSAC). Through this partnership, CPL has created a linked database of student-level administrative data on college enrollment, financial aid, and CalFresh participation that makes this analysis possible.

Stellar infrared excesses can indicate various phenomena of interest, from protoplanetary disks to debris disks, or (more speculatively) techno-signatures along the lines of Dyson spheres. In this paper, we conduct a large search for such excesses, designed as a data-driven contextual anomaly detection pipeline. We focus our search on FGK stars close to the main sequence to favour non-young host stars. We look for excess in the mid-infrared, unlocking a large sample to search in while favouring extreme IR excess akin to the ones produced by Extreme Debris Disks (EDD). We combine observations from ESA Gaia DR3, 2MASS, and the unWISE of NASA WISE, and create a catalogue of 4,898,812 stars with $G < 16$ mag. We consider a star to have an excess if it is substantially brighter in $W1$ and $W2$ bands than what is predicted from an ensemble of machine-learning models trained on the data, taking optical and near-infrared information as input features. We apply a set of additional cuts (derived from the ML models and the objects' astronomical features) to avoid false-positive and identify a set of 53 objects (a rate of $1.1\times 10^{-5}$), including one previously identified EDD candidate. Typical infrared-excess fractional luminosities we find are in the range 0.005 to 0.1, consistent with known EDDs., Comment: 23 pages, 17 figures, 2 tables. Candidates' IDs and (some) code available at https://github.com/contardog/NotATechnosignatureSearch . Comments welcomed!

When there are many observations of an astronomical source - many images with different dithers, or many spectra taken at different barycentric velocities - it is standard practice to shift and stack the data, to (for example) make a high signal-to-noise average image or mean spectrum. Bound-saturating measurements are made by manipulating a likelihood function, where the data are treated as fixed, and model parameters are modified to fit the data. Traditional shifting and stacking of data can be converted into a model-fitting procedure, such that the data are not modified, and yet the output is the shift-adjusted mean. The key component of this conversion is a spectral model that is completely flexible but also a continuous function of wavelength (or position in the case of imaging) that can represent any signal being measured by the device after any reasonable translation (or rotation or field distortion). The benefits of a modeling approach are myriad: The sacred data never are modified. Noise maps, data gaps, and bad-data masks don't require interpolation. The output can take the form of an image or spectrum evaluated on a pixel grid, as is traditional. In addition to shifts, the model can account for line-spread or point-spread function variations, world-coordinate-system variations, and calibration or normalization variations. The noise in the output becomes uncorrelated across neighboring pixels as the shifts deliver good coverage in some sense. The only cost is a small increase in computational complexity over that of traditional methods. We demonstrate the method with a small data example and we provide open source sample code for re-use., Comment: submitted to AAS Journals

Stellar age measurements are fundamental to understanding a wide range of astronomical processes, including galactic dynamics, stellar evolution, and planetary system formation. However, extracting age information from Main Sequence stars is complicated, with techniques often relying on age proxies in the absence of direct measurements. The Gaia data releases have enabled detailed studies of the dynamical properties of stars within the Milky Way, offering new opportunities to understand the relationship between stellar age and dynamics. In this study, we leverage high-precision astrometric data from Gaia DR3 to construct a stellar age prediction model based only on stellar dynamical properties; namely, the vertical action. We calibrate two distinct, hierarchical stellar age--vertical action relations, first employing asteroseismic ages for red giant branch stars, then isochrone ages for main-sequence turn-off stars. We describe a framework called "zoomies" based on this calibration, by which we can infer ages for any star given its vertical action. This tool is open-source and intended for community use. We compare dynamical age estimates from "zoomies" with ages derived from other techniques for a sample of open clusters and main-sequence stars with asteroseismic age measurements. We also compare dynamical age estimates for stellar samples from the Kepler, K2, and TESS exoplanet transit surveys. While dynamical age relations are associated with large uncertainty, they are generally mass-independent and depend on homogeneously measured astrometric data. These age predictions are uniquely useful for large-scale demographic investigations, especially in disentangling the relationship between planet occurrence, metallicity, and age for low-mass stars., Comment: 23 pages, 12 figures, 2 tables. Submitted to AAS Journals

This study develops a model-based index creation approach called the Generalized Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Bayesian approach accommodates heteroscedastic model error, multiple shared factors and flexible spatial priors. Moreover, our model, unlike previous index approaches, provides indices with uncertainty. Focusing on Australian risk factor data, the proposed GSCM is used to develop the Area Indices of Behaviors Impacting Cancer product - representing the first area level cancer risk factor index in Australia. This advancement aids in identifying communities with elevated cancer risk, facilitating targeted health interventions., Comment: Submitted to Health and Place

The North Atlantic Ocean circulation, fueled by winds and surface buoyancy fluxes, carries 1.25$\,$PettaWatts of heat poleward in the subtropics, and plays an important role in regulating global weather and climate patterns. Using a series of simulations with perturbed surface forcing, we study how winds and surface heat flux gradients affect the Atlantic meridional heat transport. We decompose the Atlantic meridional heat transport into contributions from circulation cells at warm and cold temperatures (resembling a subtropical gyre and the dense overturning circulation respectively), and a mixed circulation that contains water masses traversing both these cells. Variations in wind stress initially alter the amount of heat carried by the warm and mixed cells, but on long time scales ($>$10 years), changes in the temperature distribution restore the heat transport to equilibrium. Changes in surface buoyancy forcing control the cold cell's circulation, and its associated meridional heat transport, through high-latitude processes., Comment: 14 pages, 3 figures, submitted to the Geophysical Research Letters

Artificial intelligence (AI) has made remarkable progress across various domains, with large language models like ChatGPT gaining substantial attention for their human-like text-generation capabilities. Despite these achievements, spatial reasoning remains a significant challenge for these models. Benchmarks like StepGame evaluate AI spatial reasoning, where ChatGPT has shown unsatisfactory performance. However, the presence of template errors in the benchmark has an impact on the evaluation results. Thus there is potential for ChatGPT to perform better if these template errors are addressed, leading to more accurate assessments of its spatial reasoning capabilities. In this study, we refine the StepGame benchmark, providing a more accurate dataset for model evaluation. We analyze GPT's spatial reasoning performance on the rectified benchmark, identifying proficiency in mapping natural language text to spatial relations but limitations in multi-hop reasoning. We provide a flawless solution to the benchmark by combining template-to-relation mapping with logic-based reasoning. This combination demonstrates proficiency in performing qualitative reasoning on StepGame without encountering any errors. We then address the limitations of GPT models in spatial reasoning. We deploy Chain-of-thought and Tree-of-thoughts prompting strategies, offering insights into GPT's ``cognitive process", and achieving remarkable improvements in accuracy. Our investigation not only sheds light on model deficiencies but also proposes enhancements, contributing to the advancement of AI with more robust spatial reasoning capabilities., Comment: Camera-Ready version for AAAI 2024

This paper studies modulation spectrum features ($\Phi$) and mel-frequency cepstral coefficients ($\Psi$) in joint speaker diarization and identification (JSID). JSID is important as speaker diarization on its own to distinguish speakers is insufficient for many applications, it is often necessary to identify speakers as well. Machine learning models are set up using convolutional neural networks (CNNs) on $\Phi$ and recurrent neural networks $\unicode{x2013}$ long short-term memory (LSTMs) on $\Psi$, then concatenating into fully connected layers. Experiment 1 shows models on both $\Phi$ and $\Psi$ have better diarization error rates (DERs) than models on either alone; a CNN on $\Phi$ has DER 29.09\%, compared to 27.78\% for a LSTM on $\Psi$ and 19.44\% for a model on both. Experiment 1 also investigates aleatoric uncertainties and shows the model on both $\Phi$ and $\Psi$ has mean entropy 0.927~bits (out of 4~bits) for correct predictions compared to 1.896~bits for incorrect predictions which, along with entropy histogram shapes, shows the model helpfully indicates where it is uncertain. Experiment 2 investigates epistemic uncertainties as well as aleatoric using Monte Carlo dropout (MCD). It compares models on both $\Phi$ and $\Psi$ with models trained on x-vectors ($X$), before applying Kalman filter smoothing on epistemic uncertainties for resegmentation and model ensembles. While the two models on $X$ (DERs 10.23\% and 9.74\%) outperform those on $\Phi$ and $\Psi$ (DER 17.85\%) after their individual Kalman filter smoothing, combining them using a Kalman filter smoothing method improves the DER to 9.29\%. Aleatoric uncertainties are higher for incorrect predictions. Both Experiments show models on $\Phi$ do not distinguish overlapping speakers as well as anticipated. However, Experiment 2 shows model ensembles do better with overlapping speakers than individual models do., Comment: 12 pages, 7 figures

We analyse three interacting vacuum dark energy models with the aim of exploring whether the $H_0$ and $\sigma_8$ tensions can be simultaneously resolved in such models. We present the first ever derivation of the covariant gauge-invariant perturbation formalism for the interacting vacuum scenario, and, for the sub-class of geodesic cold dark matter models, connect the evolution of perturbation variables in this approach to the familiar cosmological observables. We show how $H_0$ and $\sigma_8$ evolve in three interacting vacuum models: firstly, a simple linear coupling between the vacuum and cold dark matter; secondly, a coupling which mimics the behaviour of a Chaplygin gas; and finally a coupling which mimics the Shan--Chen fluid dark energy model. We identify, if any, the regions of parameter space which would correspond to a simultaneous resolution of both tensions in these models. When constraints from observational data are added, we show how all the models described are constrained to be close to their $\Lambda$CDM limits., Comment: 23 pages, 8 figures, version 2 matches published version in PDU

Under the assumption of a simple and time-invariant gravitational potential, many Galactic dynamics techniques infer the Milky Way's mass and dark matter distribution from stellar kinematic observations. These methods typically rely on parameterized potential models of the Galaxy and must take into account non-trivial survey selection effects, because they make use of the density of stars in phase space. Large-scale spectroscopic surveys now supply information beyond kinematics in the form of precise stellar label measurements (especially element abundances). These element abundances are known to correlate with orbital actions or other dynamical invariants. Here, we use the Orbital Torus Imaging (OTI) framework that uses abundance gradients in phase space to map orbits. In many cases these gradients can be measured without detailed knowledge of the selection function. We use stellar surface abundances from the APOGEE survey combined with kinematic data from the Gaia mission. Our method reveals the vertical ($z$-direction) orbit structure in the Galaxy and enables empirical measurements of the vertical acceleration field and orbital frequencies in the disk. From these measurements, we infer the total surface mass density, $\Sigma$, and midplane volume density, $\rho_0$, as a function of Galactocentric radius and height. Around the Sun, we find $\Sigma_{\odot}(z=1.1$ kpc)$=72^{+6}_{-9}$M$_{\odot}$pc$^{-2}$ and $\rho_{\odot}(z=0)=0.081^{+0.015}_{-0.009}$ M$_{\odot}$pc$^{-3}$ using the most constraining abundance ratio, [Mg/Fe]. This corresponds to a dark matter contribution in surface density of $\Sigma_{\odot,\mathrm{DM}}(z=1.1$ kpc)$=24\pm4$ M$_{\odot}$pc$^{-2}$, and in total volume mass density of $\rho_{\odot,\mathrm{DM}}(z=0)=0.011\pm0.002$ M$_{\odot}$pc$^{-3}$. Moreover, using these mass density values we estimate the scale length of the low-$\alpha$ disc to be $h_R=2.24\pm0.06$kpc., Comment: Accepted for publication in ApJ. 19 pages, 11 figures, 3 Tables

We study an asymptotically free theory of $N$ relativistic Dirac fermions and a real scalar field coupled by Yukawa and scalar self-interactions in three dimensions using functional renormalisation. In the limit of many fermion flavours, the cubic scalar coupling becomes exactly marginal due to quantum fluctuations, leading to a line of strongly-coupled infrared fixed points. Fermion mass can be generated through a quantum phase transition even if chiral symmetry is absent. The line of fixed points terminates at a critical endpoint due to the loss of vacuum stability. Exactly at the endpoint, scale symmetry is broken spontaneously, leading to the generation of fermion mass. Intriguingly, the absence of chiral symmetry is a prerequisite for the spontaneous generation of fermion mass, and not a consequence thereof. We also highlight close similarities between Gross-Neveu and Gross-Neveu--Yukawa theories at and away from critical points, and establish the large-$N$ equivalence of their functional RG flows and quantum effective actions. Further implications including for conformal field theories are indicated., Comment: 24 pages, 5 figures; v2: text added to conclusion, version published in JHEP

We study existence and uniqueness results for the Yamabe problem on non-compact manifolds of negative curvature type. Our first existence and uniqueness result concerns those such manifolds which are asymptotically locally hyperbolic. In this context, our result requires only a partial $C^2$ decay of the metric, namely the full decay of the metric in $C^1$ and the decay of the scalar curvature. In particular, no decay of the Ricci curvature is assumed. In our second result we establish that a local volume ratio condition, when combined with negativity of the scalar curvature at infinity, is sufficient for existence of a solution. Our volume ratio condition appears tight. This paper is based on the DPhil thesis of the first author., Comment: To appear in Analysis in Theory and Applications

Internal waves propagate on the ocean stratification and carry energy and momentum through the ocean interior. The two most significant sources of these waves in the ocean are surface winds and oscillatory tidal flow across topography. We propose a hybrid of these two mechanisms, in which wind induced oscillations of sea surface and isopycnal heights are rapidly communicated to the seafloor via hydrostatic pressure. In the presence of topography, the resulting oscillatory bottom velocity may then generate internal waves in a similar manner to the barotropic tide. We investigate this mechanism in an idealised numerical isopycnal model of a storm passing over a mid ocean ridge, and perform several perturbation experiments in which ocean and wind properties are varied. Bottom-generated internal waves are identified propagating away from the ridge in the wake of the storm. Estimates of the total wave energy suggest that in the right circumstances these waves could be a significant source of internal wave energy, with a local wind work to wave energy conversion rate of up to 50% of the corresponding conversion to surface generated near-inertial waves in our domain. Our results suggest a need for further investigation in less idealised scenarios to more precisely quantity this novel mechanism of deep ocean wave generation, and how it may affect abyssal mixing., Comment: submitted to the Journal of Physical Oceanography

Problems with the cosmological constant model of dark energy motivate the investigation of alternative scenarios. I make the first measurement of the dark energy equation of state using the hierarchical strong lensing time delay likelihood provided by TDCOSMO. I find that the combination of seven TDCOSMO lenses and 33 SLACS lenses is only able to provide a weak constraint on the dark energy equation of state, $w < -1.75$ at 68% confidence, which nevertheless implies the presence of a phantom dark energy component. When the strong lensing time delay data is combined with a collection of cosmic microwave background, baryon acoustic oscillation and Type Ia supernova data, I find that the equation of state is $w = -1.025\pm 0.029$., Comment: 6 pages, 6 figures. V2 matches version published in MNRAS Letters

When turbulent flow is laden with negatively buoyant particles, their mean distribution over the direction of gravity can induce stable density gradients that penalize turbulent fluctuations. This effect is studied numerically for shear-driven flow with dilute non-inertial sediment. The turbulent dynamics and sediment transport depend critically on particle settling velocity $v_s$, splitting into two regimes: homogeneous weakly stratified turbulence and flow with developed turbulence atop an intermittent boundary layer. At intermediate $v_s$, neither state can be sustained and the flow laminarizes., Comment: 5 pages, 5 figures

We present AspGap, a new approach to infer stellar labels from low-resolution Gaia XP spectra, including precise [$\alpha$/M] estimates for the first time. AspGap is a neural-network based regression model trained on APOGEE spectra. In the training step, AspGap learns to use XP spectra not only to predict stellar labels but also the high-resolution APOGEE spectra that lead to the same stellar labels. The inclusion of this last model component -- dubbed the hallucinator -- creates a more physically motivated mapping and significantly improves the prediction of stellar labels in the validation, particularly of [$\alpha$/M]. For giant stars, we find cross-validated rms accuracies for Teff, log g, [M/H], [$\alpha$/M] of ~1%, 0.12 dex, 0.07 dex, 0.03 dex, respectively. We also validate our labels through comparison with external datasets and through a range of astrophysical tests that demonstrate that we are indeed determining [$\alpha$/M] from the XP spectra, rather than just inferring it indirectly from correlations with other labels. We publicly release the AspGap codebase, along with our stellar parameter catalog for all giants observed by Gaia XP. AspGap enables new insights into the formation and chemo-dynamics of our Galaxy by providing precise [$\alpha$/M] estimates for 23 million giant stars, including 12 million with radial velocities from Gaia., Comment: 29 pages, 19 figures, submitted to ApJS

While several previous studies have devised methods for segmentation of polyps, most of these methods are not rigorously assessed on multi-center datasets. Variability due to appearance of polyps from one center to another, difference in endoscopic instrument grades, and acquisition quality result in methods with good performance on in-distribution test data, and poor performance on out-of-distribution or underrepresented samples. Unfair models have serious implications and pose a critical challenge to clinical applications. We adapt an implicit bias mitigation method which leverages Bayesian predictive uncertainties during training to encourage the model to focus on underrepresented sample regions. We demonstrate the potential of this approach to improve generalisability without sacrificing state-of-the-art performance on a challenging multi-center polyp segmentation dataset (PolypGen) with different centers and image modalities., Comment: To be presented at the Fairness of AI in Medical Imaging (FAIMI) MICCAI 2023 Workshop and published in volumes of the Springer Lecture Notes Computer Science (LNCS) series

Cancer is a significant health issue globally and it is well known that cancer risk varies geographically. However in many countries there are no small area level data on cancer risk factors with high resolution and complete reach, which hinders the development of targeted prevention strategies. Using Australia as a case study, the 2017-2018 National Health Survey was used to generate prevalence estimates for 2221 small areas across Australia for eight cancer risk factor measures covering smoking, alcohol, physical activity, diet and weight. Utilising a recently developed Bayesian two-stage small area estimation methodology, the model incorporated survey-only covariates, spatial smoothing and hierarchical modelling techniques, along with a vast array of small area level auxiliary data, including census, remoteness, and socioeconomic data. The models borrowed strength from previously published cancer risk estimates provided by the Social Health Atlases of Australia. Estimates were internally and externally validated. We illustrated that in 2017-18 health behaviours across Australia exhibited more spatial disparities than previously realised by improving the reach and resolution of formerly published cancer risk factors. The derived estimates reveal higher prevalence of unhealthy behaviours in more remote areas, and areas of lower socioeconomic status; a trend that aligns well with previous work. Our study addresses the gaps in small area level cancer risk factor estimates in Australia. The new estimates provide improved spatial resolution and reach and will enable more targeted cancer prevention strategies at the small area level, supporting policy makers, researchers, and the general public in understanding the spatial distribution of cancer risk factors in Australia. To help disseminate the results of this work, they will be made available in the Australian Cancer Atlas 2.0., Comment: Submitted to the International Journal of Health Geographics

Obtaining labelled data to train deep learning methods for estimating animal pose is challenging. Recently, synthetic data has been widely used for pose estimation tasks, but most methods still rely on supervised learning paradigms utilising synthetic images and labels. Can training be fully unsupervised? Is a tiny synthetic dataset sufficient? What are the minimum assumptions that we could make for estimating animal pose? Our proposal addresses these questions through a simple yet effective self-supervised method that only assumes the availability of unlabelled images and a small set of synthetic 2D poses. We completely remove the need for any 3D or 2D pose annotations (or complex 3D animal models), and surprisingly our approach can still learn accurate 3D and 2D poses simultaneously. We train our method with unlabelled images of horses mainly collected for YouTube videos and a prior consisting of 2D synthetic poses. The latter is three times smaller than the number of images needed for training. We test our method on a challenging set of horse images and evaluate the predicted 3D and 2D poses. We demonstrate that it is possible to learn accurate animal poses even with as few assumptions as unlabelled images and a small set of 2D poses generated from synthetic data. Given the minimum requirements and the abundance of unlabelled data, our method could be easily deployed to different animals.