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The electrocardiogram (ECG) is an inexpensive and widely available tool for cardiovascular assessment. Despite its standardized format and small file size, the high complexity and inter-individual variability of ECG signals (typically a 60,000-size vector) make it challenging to use in deep learning models, especially when only small datasets are available. This study addresses these challenges by exploring feature generation methods from representative beat ECGs, focusing on Principal Component Analysis (PCA) and Autoencoders to reduce data complexity. We introduce three novel Variational Autoencoder (VAE) variants: Stochastic Autoencoder (SAE), Annealed beta-VAE (Abeta-VAE), and cyclical beta-VAE (Cbeta-VAE), and compare their effectiveness in maintaining signal fidelity and enhancing downstream prediction tasks. The Abeta-VAE achieved superior signal reconstruction, reducing the mean absolute error (MAE) to 15.7 plus-minus 3.2 microvolts, which is at the level of signal noise. Moreover, the SAE encodings, when combined with ECG summary features, improved the prediction of reduced Left Ventricular Ejection Fraction (LVEF), achieving an area under the receiver operating characteristic curve (AUROC) of 0.901. This performance nearly matches the 0.910 AUROC of state-of-the-art CNN models but requires significantly less data and computational resources. Our findings demonstrate that these VAE encodings are not only effective in simplifying ECG data but also provide a practical solution for applying deep learning in contexts with limited-scale labeled training data.

Dense cold molecular cores/filaments are surrounded by an envelope of translucent gas. Some of the low-J emission lines of CO and HCO$^+$ isotopologues are more sensitive to the conditions either in the translucent environment or in the dense cold one. We propose a cloud model composed of three homogeneous slabs of gas along each LoS, representing an envelope and a shielded inner layer. IRAM-30m data from the ORION-B large program toward the Horsehead nebula are used to demonstrate the method's capability. We use the non-LTE radiative transfer code RADEX to model the line profiles from the kinetic temperature $T_{kin}$, the volume density $n_{H_2}$, kinematics and chemical properties of the different layers. We then use a maximum likelihood estimator to simultaneously fit the lines of the CO and HCO$^+$ isotopologues. We constrain column density ratios to limit the variance on the estimates. This simple heterogeneous model provides good fits of the fitted lines over a large part of the cloud. The decomposition of the intensity into three layers allows to discuss the distribution of the estimated physical/chemical properties along the LoS. About 80$\%$ the CO integrated intensity comes from the envelope, while $\sim55\%$ of that of the (1-0) and (2-1) lines of C$^{18}$O comes from the inner layer. The $N(^{13}CO)/N(C^{18}O)$ in the envelope increases with decreasing $A_v$, and reaches $25$ in the pillar outskirts. The envelope $T_{kin}$ varies from 25 to 40 K, that of the inner layer drops to $\sim 15$ K in the western dense core. The inner layer $n_{H_2}$ is $\sim 3\times10^4\,\text{cm}^{-3}$ toward the filament and it increases by a factor $10$ toward dense cores. The proposed method correctly retrieves the physical/chemical properties of the Horsehead nebula and offers promising prospects for less supervised model fits of wider-field datasets.

Given a directed graph, the Minimal Feedback Arc Set (FAS) problem asks for a minimal set of arcs in a directed graph, which, when removed, results in an acyclic graph. Equivalently, the FAS problem asks to find an ordering of the vertices that minimizes the number of feedback arcs. This is considered an algorithmic problem of central importance in discrete mathematics, with varied applications to problems in computer science and operations research. Berger and Shor, in 1990, developed upper bounds for the FAS problem in general directed graphs. Here we find asymptotic lower bounds for the FAS problem in a class of random graphs given by the Erd\H{o}s-R\'{e}nyi model G(n,M), with n vertices and M (undirected) edges, the latter randomly chosen. Each edge is then randomly given a direction to form our directed graph. Our interest is in developing a $\textit{lower}$ bound for the minimal feedback arc set that holds with probability 1 as $n\rightarrow \infty$. We show that $$Pr\left(\textbf{Y}^* \le M \left( \frac{1}{2} -\sqrt{\frac{\log n}{2\Delta_{av}}}\right)\right)$$ approaches zero exponentially in $n$, with $\textbf{Y}^*$ the (random) size of the minimal feedback set and $\Delta_{av}=M/n$ the average vertex degree. We subsequently apply our lower bounds to a set of experimental FAS data on related random graphs, as developed by Kathrin Hanauer. Not only does our formula provide a reasonably close lower bound for the minimal set, but the approximation that lies midway between our lower bound and the obvious upper bound of $M/2$ is remarkably close to the computed FAS data over a range of experiments, suggesting that this approximation may in fact be asymptotic to the minimal number of feedback arcs, for large $n$, and an excellent estimate even for moderate values., Comment: 11 pages, 4 figures

Surface-gated quantum dots (QDs) in semiconductor heterostructures represent a highly attractive platform for quantum computation and simulation. However, in this implementation, the barriers through which the QD is tunnel-coupled to source and drain reservoirs (or neighboring QDs) are usually non-rigid, and capacitively influenced by the plunger gate voltage (VP). In transport spectroscopy measurements, this leads to complete suppression of current and lifting of Coulomb blockade, for large negative and positive values of VP, respectively. Consequently, the charge-occupancy of the QD can be tuned over a rather small range of VP. By dynamically tuning the tunnel barriers to compensate for the capacitive effect of VP, here we demonstrate a protocol which allows the Coulomb blockade to be preserved over a remarkably large span of charge-occupancies, as demonstrated by clean Coulomb diamonds and well-resolved excited state features. The protocol will be highly beneficial for automated tuning and identification of the gatevoltage-space for optimal operation of QDs, in large arrays required for a scalable spin quantum computing architecture., Comment: 5 pages, 5 figures, 1 table

Characterising drivers of SARS-CoV-2 circulation is crucial for understanding COVID-19 because of the severity of control measures adopted during the pandemic. Whole genome sequence data augmented with demographic metadata provides the best opportunity to do this. We use Random Forest Decision Tree models to analyse a combination of over 4000 SARS-CoV2 sequences from a densely sampled, mixed urban and rural population (Tayside) in Scotland in the period from August 2020 to July 2021, with fine scale geographical and socio-demographic metadata. Comparing periods in versus out of "lockdown" restrictions, we show using genetic distance relationships that individuals from more deprived areas are more likely to get infected during lockdown but less likely to spread the infection further. As disadvantaged communities were the most affected by both COVID-19 and its restrictions, our finding has important implications for informing future approaches to control future pandemics driven by similar respiratory infections., Comment: 186 pages, approx. 48 figures (including Methods ans Supplementary Information)

We present a detailed study of the gas and galaxy properties of the cluster PSZ2 G282.28+49.94 detected in the Planck all-sky survey. The intracluster medium (ICM) of this object at z=0.56 exhibits a cometary-like shape. Combining Chandra and TNG observations, we characterised the spatially resolved thermodynamical properties of the gas and the spatial and velocity distribution of 73 galaxy members. The cluster structure is quite complex with an elongated core region containing the two brightest cluster galaxies and one dense group to the south-east. Since there is no velocity difference between the core and the south-east group, we suggest the presence of a merger along the plane of the sky. This structure is related to complex X-ray and radio features, and thus the merger has likely been caught during the post-merger phase. Comparing the distribution of the ICM and of member galaxies, we find a large offset of $\sim 350$ kpc between the position of the X-ray peak and the centre of a concentration of galaxies, preceding it in the likely direction of motion. This configuration is similar to the famous Bullet Cluster, leading us to dub PSZ2 G282.28+49.94 the "Planck bullet", and represents an ideal situation to provide astrophysical constraints to the self-interaction cross-section ($\sigma/m$) of dark matter particles. These results illustrate the power of a multi-wavelength approach to probe the merging scenario of such complex and distant systems., Comment: Accepted for publication in A&A

Type Ia supernovae (SNe Ia) constitute an historical probe to derive cosmological parameters through the fit of the Hubble-Lema\^itre diagram, i.e. SN Ia distance modulus versus their redshift. In the era of precision cosmology, realistic simulation of SNe Ia for any survey entering in an Hubble-Lema\^itre diagram is a key tool to address observational systematics, like Malmquist bias. As the distance modulus of SNe Ia is derived from the fit of their light-curves, a robust simulation framework is required. In this paper, we present the performances of the simulation framework skysurvey to reproduce the the Zwicky Transient Facility (ZTF) SN Ia DR2 covering the first phase of ZTF running from April 2018 up to December 2020. The ZTF SN Ia DR2 sample correspond to almost 3000 classified SNe Ia of cosmological quality. First, a targeted simulation of the ZTF SN Ia DR2 was carried on to check the validity of the framework after some fine tuning of the observing conditions and instrument performance. Then, a realistic simulation has been run using observing ZTF logs and ZTF SN Ia DR2 selection criteria on simulated light-curves to demonstrate the ability of the simulation framework to match the ZTF SN Ia DR2 sample. Furthermore a redshift dependency of SALT2 light-curve parameters (stretch and colour) was conducted to deduce a volume limited sample, i.e. an unbiased SNe Ia sample, characterized with $z_{lim} \leq 0.06$. This volume limited sample of about 1000 SNe Ia is unique to carry on new analysis on standardization procedure with a precision never reached (those analysis are presented in companion papers)., Comment: 10 pages, 10 figures. Submitted to A & A

The Zwicky Transient Facility SN Ia Data Release 2 (ZTF SN Ia DR2) contains more than 3,000 Type Ia supernovae (SNe Ia), providing the largest homogeneous low-redshift sample of SNe Ia. Having at least one spectrum per event, this data collection is ideal for large-scale statistical studies of the photometric, spectroscopic and host-galaxy properties of SNe Ia, particularly of the more rare "peculiar" subclasses. In this paper, we first present the method we developed to spectroscopically classify the SNe in the sample, and the techniques we used to model their multi-band light curves and explore their photometric properties. We then show a method to distinguish between the "peculiar" subtypes and the normal SNe Ia. We also explore the properties of their host galaxies and estimate their relative rates, focusing on the "peculiar" subtypes and their connection to the cosmologically useful SNe Ia. Finally, we discuss the implications of our study with respect to the progenitor systems of the "peculiar" SN Ia events., Comment: 19 pages, 13 figures, submitted to Astronomy and Astrophysics

The Listening in Spatialized Noise Sentences (LiSN-S) is a test to evaluate auditory spatial processing currently only available in the English language. It produces a three-dimensional auditory environment under headphones and uses a simple repetition response protocol to determine speech reception thresholds (SRTs) for sentences presented in competing speech under various conditions. In order to develop the LiSN-S test in Brazilian Portuguese, it was necessary to prepare a speech database recorded by professional voice actresses and to devise presentation software. These sentences were presented to 35 adults (aged between 19 and 40 years) and 24 children (aged between 8 and 10 years), all with normal hearing-verified through tone and speech audiometry and tympanometry-and good performance at school. We used a logistic curve describing word error rate versus presentation level, fitted for each sentence, to select a set of 120 sentences for the test. Furthermore, all selected sentences were adjusted in amplitude for equal intelligibility. The framework of LiSN-S in Brazilian Portuguese is ready for normative data analysis. After its conclusion, we believe it will contribute to diagnosing and rehabilitating Brazilian children with complaints related to hearing difficulties in noisy environments

The $\lambda 21$cm H I emission that is used to trace the gas to dust ratio at high Galactic latitudes has contributions from material beyond the Milky Way disk, with uncertain and likely sub-Solar metallicity and dust content. These contributions can be isolated kinematically and their presence is clear for sightlines with small mean reddening $<$E(B-V)$>$ $\la$ 0.03 mag, which have mean ratios $<$N(H I)$>$/$<$E(B-V)$>$ that are 20-50\% above the high latitude Galactic average $<$N(H I)$>/<$E(B-V)$>=8.3\times10^{21}$cm$^{-2}$mag$^{-1}$. By mapping N(H I) and E(B-V) across H I High Velocity Cloud complexes and the Magellanic Clouds we show that the reddening of this kinematically-isolated gas is on average five times smaller per H I than the high latitude average. However, the aggregate contribution of this gas is small and $<$N(H I)$>/<$E(B-V)$>=8.3\times10^{21}$cm$^{-2}$mag$^{-1}$ is the appropriate value for Galactic gas seen at high latitude using the H I and reddening measures employed here and in our previous work., Comment: Accepted to The Astrophysical Journal

The Fusion Synthesis Engine (FUSE) is a state-of-the-art software suite designed to revolutionize fusion power plant design. FUSE integrates first-principle models, machine learning, and reduced models into a unified framework, enabling comprehensive simulations that go beyond traditional 0D systems studies. FUSE's modular structure supports a hierarchy of model fidelities, from steady-state to time-dependent simulations, allowing for both pre-conceptual design and operational scenario development. This framework accelerates the design process by enabling self-consistent solutions across physics, engineering, and control systems, minimizing the need for iterative expert evaluations. Leveraging modern software practices and parallel computing, FUSE also provides multi-objective optimization, balancing cost, efficiency, and operational constraints. Developed in Julia, FUSE is fully open-source under the Apache 2.0 license, promoting transparency and collaboration within the fusion research community.

It has been known since 1992 that the McKay-Thompson series $T_g(q)$ of the Moonshine module form Hauptmoduln for genus zero subgroups of $SL(2, \mathbb{R})$. In 2021, Lin and Shao constructed a series analogous to the McKay-Thompson series (a twined partition function of the Monster CFT), but using a non-invertible topological defect rather than an element of the Monster group $\mathcal{M}$. This "defect McKay-Thompson series" was found to be invariant under a genus zero subgroup of $SL(2, \mathbb{R})$, but was shown not to be the Hauptmodul of the subgroup. Nevertheless, one might wonder if a weaker version of Borcherds' theorem holds for non-invertible defects: perhaps defect McKay-Thompson series enjoy genus zero invariance groups in $SL(2, \mathbb{R})$, whether or not they are Hauptmoduln for those groups. Using the decompositions of the monster stress tensor found in Bae et al. (2021), we construct several new defect McKay-Thompson series, study their modular properties, and determine their invariance groups in $SL(2, \mathbb{R})$. We discover that many of the invariance groups are not genus zero., Comment: 18 pages, 1 figure, 5 tables

The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large data sets, a granular understanding of the analysis methodology is an essential component of reproducibility. This paper discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation.

Observations of ionic, atomic, or molecular lines are performed to improve our understanding of the interstellar medium (ISM). However, the potential of a line to constrain the physical conditions of the ISM is difficult to assess quantitatively, because of the complexity of the ISM physics. The situation is even more complex when trying to assess which combinations of lines are the most useful. Therefore, observation campaigns usually try to observe as many lines as possible for as much time as possible. We search for a quantitative statistical criterion to evaluate the constraining power of a (or combination of) tracer(s) with respect to physical conditions in order to improve our understanding of the statistical relationships between ISM tracers and physical conditions and helps observers to motivate their observation proposals. The best tracers are obtained by comparing the mutual information between a physical parameter and different sets of lines. We apply this method to simulations of radio molecular lines emitted by a photodissociation region similar to the Horsehead Nebula that would be observed at the IRAM 30m telescope. We search for the best lines to constrain the visual extinction $A_v^{tot}$ or the far UV illumination $G_0$. The most informative lines change with the physical regime (e.g., cloud extinction). Short integration time of the CO isotopologue $J=1-0$ lines already yields much information on the total column density most regimes. The best set of lines to constrain the visual extinction does not necessarily combine the most informative individual lines. Precise constraints on $G_0$ are more difficult to achieve with molecular lines. They require spectral lines emitted at the cloud surface (e.g., [CII] and [CI] lines). This approach allows one to better explore the knowledge provided by ISM codes, and to guide future observation campaigns.

The potential impact of cation-substituted AlN-based materials, such as Al1-xScxN, Al1-xGaxN, and Al1-xBxN, with exceptional electronic, electromechanical, and dielectric properties has spurred research into this broad family of materials. Rare earth (RE) cations are particularly appealing as they could additionally impart optoelectronic or magnetic functionality. However, success in incorporating a significant level of RE cations into AlN has been limited so far because it is thermodynamically challenging to stabilize such heterostructural alloys. Using combinatorial co-sputtering, we synthesized Al1-xRExN (RE = Pr, Tb) thin films and performed a rapid survey of the composition-structure-property relationships as a function of RE alloying. Under our growth conditions, we observe that Al1-xPrxN maintains a phase-pure wurtzite structure until transitioning to amorphous for x>0.22. Al1-xTbxN exhibits a phase-pure wurtzite structure until x<0.15, then exhibits mixed wurtzite and rocksalt phases for 0.16

We study social welfare relations (SWRs) on infinite utility streams. Our main result is a new characterization of a utilitarian SWR on lotteries over welfare distributions for an infinite population. We characterize this SWR as the maximally complete SWR over lotteries on such distributions which satisfies Strong Pareto, Permutation Invariance (elsewhere called "Relative Anonymity" and "Isomorphism Invariance"), Dominance, Ex Ante Indifference, and an Additive Invariance axiom.

This paper presents a comprehensive survey of network analysis research on the film industry, aiming to evaluate its emergence as a field of study and identify potential areas for further research. Many foundational network studies made use of the abundant data from the Internet Movie Database (IMDb) to test network methodologies. This survey focuses more specifically on examining research that employs network analysis to evaluate the film industry itself, revealing the social and business relationships involved in film production, distribution, and consumption. The paper adopts a classification approach based on node type and conducts network analyses of the reviewed literature, mapping citation and co-authorship networks. The findings provide insights into the structure and interconnectedness of the field, offering potential clusters of debates and shedding light on the institutional, geographical, and demographic characteristics shaping this field. In addition, this survey contributes to understanding film industry network analysis and informs researchers interested in network methods within the film industry and related cultural sectors.

Results are presented for the measurement of large-scale anisotropies in the arrival directions of ultra-high-energy cosmic rays detected at the Pierre Auger Observatory during 19 years of operation, prior to AugerPrime, the upgrade of the Observatory. The 3D dipole amplitude and direction are reconstructed above $4\,$EeV in four energy bins. Besides the established dipolar anisotropy in right ascension above $8\,$EeV, the Fourier amplitude of the $8$ to $16\,$EeV energy bin is now also above the $5\sigma$ discovery level. No time variation of the dipole moment above $8\,$EeV is found, setting an upper limit to the rate of change of such variations of $0.3\%$ per year at the $95\%$ confidence level. Additionally, the results for the angular power spectrum are shown, demonstrating no other statistically significant multipoles. The results for the equatorial dipole component down to $0.03\,$EeV are presented, using for the first time a data set obtained with a trigger that has been optimized for lower energies. Finally, model predictions are discussed and compared with observations, based on two source emission scenarios obtained in the combined fit of spectrum and composition above $0.6\,$EeV.

We develop a surface for the electric dipole moment of three interacting helium atoms and use it, together with state-of-the-art potential and polarizability surfaces, to compute the third dielectric virial coefficient, $C_\varepsilon$, for both $^4$He and $^3$He isotopes. Our results agree with previously published data computed using an approximated form for the three-body polarizability, and are extended to the low-temperature regime by including exchange effects. Additionally, the uncertainty of $C_\varepsilon$ is rigorously determined for the first time by propagating the uncertainties of the potential and polarizability surfaces; this uncertainty is much larger than the contribution from the dipole-moment surface to $C_\varepsilon$. Our results compare reasonably well with the limited experimental data. The first-principles values of $C_\epsilon$ computed in this work will enhance the accuracy of primary temperature and pressure metrology based on measurements of the dielectric constant of helium., Comment: 18 pages, 7 figures

SuperBIT was a 0.5-meter near-ultraviolet to near-infrared wide-field telescope that launched on a NASA superpressure balloon into the stratosphere from New Zealand for a 45-night flight. SuperBIT acquired multi-band images of galaxy clusters to study the properties of dark matter using weak gravitational lensing. We provide an overview of the instrument and its various subsystems. We then present the instrument performance from the flight, including the telescope and image stabilization system, the optical system, the power system, and the thermal system. SuperBIT successfully met the instrument's technical requirements, achieving a telescope pointing stability of 0.34 +/- 0.10 arcseconds, a focal plane image stability of 0.055 +/- 0.027 arcseconds, and a PSF FWHM of ~ 0.35 arcseconds over 5-minute exposures throughout the 45-night flight. The telescope achieved a near-diffraction limited point-spread function in all three science bands (u, b, and g). SuperBIT served as a pathfinder to the GigaBIT observatory, which will be a 1.34-meter near-ultraviolet to near-infrared balloon-borne telescope., Comment: 17 pages, 25 pages, published in the Astronomical Journal

The 7-point checklist (7PCL) is widely used in dermoscopy to identify malignant melanoma lesions needing urgent medical attention. It assigns point values to seven attributes: major attributes are worth two points each, and minor ones are worth one point each. A total score of three or higher prompts further evaluation, often including a biopsy. However, a significant limitation of current methods is the uniform weighting of attributes, which leads to imprecision and neglects their interconnections. Previous deep learning studies have treated the prediction of each attribute with the same importance as predicting melanoma, which fails to recognize the clinical significance of the attributes for melanoma. To address these limitations, we introduce a novel diagnostic method that integrates two innovative elements: a Clinical Knowledge-Based Topological Graph (CKTG) and a Gradient Diagnostic Strategy with Data-Driven Weighting Standards (GD-DDW). The CKTG integrates 7PCL attributes with diagnostic information, revealing both internal and external associations. By employing adaptive receptive domains and weighted edges, we establish connections among melanoma's relevant features. Concurrently, GD-DDW emulates dermatologists' diagnostic processes, who first observe the visual characteristics associated with melanoma and then make predictions. Our model uses two imaging modalities for the same lesion, ensuring comprehensive feature acquisition. Our method shows outstanding performance in predicting malignant melanoma and its features, achieving an average AUC value of 85%. This was validated on the EDRA dataset, the largest publicly available dataset for the 7-point checklist algorithm. Specifically, the integrated weighting system can provide clinicians with valuable data-driven benchmarks for their evaluations.

We bring the Kerr--Newman spacetime into the Bondi--Sachs gauge by means of zero angular momentum, null geodesics. We compute the memory effect produced at the black hole horizon by a transient gravitational shock wave, which from future null infinity is seen as a Bondi-Metzner-Sachs supertranslation. This results in a change of the supertransformation charges at infinity between the spacetime geometries defined by the black hole before, and after, the shockwave scattering. For an extremal Kerr--Newman black hole, we give the complementary description of this process in the near-horizon limit, as seen by an observer hovering over the horizon. In this limit, we compute the supertranformation charges and compare them to those calculated at null infinity. We analyze the effect of these transformations on the electromagnetic gauge field and explore the self-interaction between this and the angular momentum of the black hole., Comment: 24 pages, 0 figures

We present in this paper the discovery, properties, and a catalog of 1165 high redshift $6.5 < z < 18$ galaxies found in deep JWST NIRCam imaging from the GTO PEARLS survey combined with data from JWST public fields. We describe our bespoke homogeneous reduction process and our analysis of these areas including the NEP, CEERS, GLASS, NGDEEP, JADES, and ERO SMACS-0723 fields with over 214 arcmin$^{2}$ imaged to depths of $\sim 30$ mag. We describe our rigorous methods for identifying these galaxies, involving the use of Lyman-break strength, detection significance criteria, visual inspection, and integrated photometric redshifts probability distributions predominately at high redshift. Our sample is a robust and highly pure collection of distant galaxies from which we also remove brown dwarf stars, and calculate completeness and contamination from simulations. We include a summary of the basic properties of these $z > 6.5$ galaxies, including their redshift distributions, UV absolute magnitudes, and star formation rates. Our study of these young galaxies reveals a wide range of stellar population properties as seen in their colors and SED fits which we compare to stellar population models, indicating a range of star formation histories, dust, AGN and/or nebular emission. We find a strong trend exists between stellar mass and $(U-V)$ color, as well as the existence of the `main-sequence' of star formation for galaxies as early as $z \sim 12$. This indicates that stellar mass, or an underlying variable correlating with stellar mass, is driving galaxy formation, in agreement with simulation predictions. We also discover ultra-high redshift candidates at $z > 12$ in our sample and describe their properties. Finally, we note a significant observed excess of galaxies compared to models at $z > 12$, revealing a tension between predictions and our observations., Comment: Submitted to AAS Journals, 37 pages

Pattern separation is a computational process by which dissimilar neural patterns are generated from similar input patterns. We present an information-geometric formulation of pattern separation, where a pattern separator is modelled as a family of statistical distributions on a manifold. Such a manifold maps an input (i.e. coordinates) to a probability distribution that generates firing patterns. Pattern separation occurs when small coordinate changes result in large distances between samples from the corresponding distributions. Under this formulation, we implement a two-neuron system whose probability law forms a 3-dimensional manifold with mutually orthogonal coordinates representing the neurons' marginal and correlational firing rates. We use this highly controlled system to examine the behaviour of spike train similarity indices commonly used in pattern separation research. We found that all indices (except scaling factor) were sensitive to relative differences in marginal firing rates, but no index adequately captured differences in spike trains that resulted from altering the correlation in activity between the two neurons. That is, existing pattern separation metrics appear (A) sensitive to patterns that are encoded by different neurons, but (B) insensitive to patterns that differ only in relative spike timing (e.g. synchrony between neurons in the ensemble).

The Super-pressure Balloon-borne Imaging Telescope (SuperBIT) is a near-diffraction-limited 0.5m telescope that launched via NASA's super-pressure balloon technology on April 16, 2023. SuperBIT achieved precise pointing control through the use of three nested frames in conjunction with an optical Fine Guidance System (FGS), resulting in an average image stability of 0.055" over 300-second exposures. The SuperBIT FGS includes a tip-tilt fast-steering mirror that corrects for jitter on a pair of focal plane star cameras. In this paper, we leverage the empirical data from SuperBIT's successful 45-night stratospheric mission to inform the FGS design for the next-generation balloon-borne telescope. The Gigapixel Balloon-borne Imaging Telescope (GigaBIT) is designed to be a 1.35m wide-field, high resolution imaging telescope, with specifications to extend the scale and capabilities beyond those of its predecessor SuperBIT. A description and analysis of the SuperBIT FGS will be presented along with methodologies for extrapolating this data to enhance GigaBIT's FGS design and fine pointing control algorithm. We employ a systems engineering approach to outline and formalize the design constraints and specifications for GigaBIT's FGS. GigaBIT, building on the SuperBIT legacy, is set to enhance high-resolution astronomical imaging, marking a significant advancement in the field of balloon-borne telescopes., Comment: 13 pages, 7 figures, SPIE Astronomical Telescopes + Instrumentation 2024

We present a full analysis of galaxy major merger pair fractions, merger rates, and mass accretion rates, thus uncovering the role of mergers in galaxy formation at the earliest previously unexplored epoch of $4.5

A generic black hole merger occurs through a restructuring of creases (sharp edges) on the event horizon. This process is studied for a black hole merger in the limit of infinite mass ratio, for which constructing the event horizon reduces to finding a null hypersurface that asymptotes to a Rindler horizon in the Kerr spacetime. Geometrical properties of the creases on this horizon are determined and the results are compared with the predictions of an exact local description of the event horizon in a generic merger. The crease set is shown to have finite area. A recently proposed expression for the gravitational entropy of a crease is shown to diverge at the instant of merger. Caustics on (and off) the event horizon are determined by exploiting the correspondence with the problem of gravitational lensing by a Kerr black hole. Caustics form an "astroid tube" in spacetime, two edges of which lie on the event horizon of the merger. Perturbative expressions for the location of this tube are presented, extending previous work to much higher perturbative order., Comment: 44 pages + appendices, many figures

Ultra-high-energy cosmic rays are known to be mainly of extragalactic origin, and their propagation is limited by energy losses, so their arrival directions are expected to correlate with the large-scale structure of the local Universe. In this work, we investigate the possible presence of intermediate-scale excesses in the flux of the most energetic cosmic rays from the direction of the supergalactic plane region using events with energies above 20 EeV recorded with the surface detector array of the Pierre Auger Observatory up to 31 December 2022, with a total exposure of 135,000 km^2 sr yr. The strongest indication for an excess that we find, with a post-trial significance of 3.1{\sigma}, is in the Centaurus region, as in our previous reports, and it extends down to lower energies than previously studied. We do not find any strong hints of excesses from any other region of the supergalactic plane at the same angular scale. In particular, our results do not confirm the reports by the Telescope Array collaboration of excesses from two regions in the Northern Hemisphere at the edge of the field of view of the Pierre Auger Observatory. With a comparable exposure, our results in those regions are in good agreement with the expectations from an isotropic distribution., Comment: submitted to ApJ

More than 3000 spectroscopically confirmed Type Ia supernovae (SNe Ia) are presented in the Zwicky Transient Facility SN Ia Data Release 2 (ZTF DR2). In this paper, we detail the spectral properties of 482 SNe Ia near maximum light, up to a redshift limit of $z$ $\leq$ 0.06. We measure the velocities and pseudo-equivalent widths (pEW) of key spectral features (Si II $\lambda$5972 and Si II $\lambda$6355) and investigate the relation between the properties of the spectral features and the photometric properties from the SALT2 light-curve parameters as a function of spectroscopic sub-class. We discuss the non-negligible impact of host galaxy contamination on SN Ia spectral classifications, as well as investigate the accuracy of spectral template matching of the ZTF DR2 sample. We define a new subclass of underluminous SNe Ia (`04gs-like') that lie spectroscopically between normal SNe Ia and transitional 86G-like SNe Ia (stronger Si II $\lambda$5972 than normal SNe Ia but significantly weaker Ti II features than `86G-like' SNe). We model these `04gs-like' SN Ia spectra using the radiative-transfer spectral synthesis code tardis and show that cooler temperatures alone are unable to explain their spectra; some changes in elemental abundances are also required. However, the broad continuity in spectral properties seen from bright (`91T-like') to faint normal SN Ia, including the transitional and 91bg-like SNe Ia, suggests that variations within a single explosion model may be able to explain their behaviour.

Let ${\mathfrak g}$ be a Garding-Dirichlet operator on the set S(n) of symmetric $n\times n$ matrices. We assume that ${\mathfrak g}$ is $I$-central, that is, $D_I {\mathfrak g} = k I$ for some $k>0$. Then $$ {\mathfrak g}(A)^{1\over N} \ \geq\ {\mathfrak g}(I)^{1\over N} (\det\, A)^{1\over n} \qquad \forall\, A>0. $$ From work of Guo, Phong, Tong, Abja, Dinew, Olive and many others, this inequality has important applications.

Using lattice QCD simulations with $O(a)$-improved Wilson quarks and physical up, down and strange quark masses, we investigate the properties of thermal QCD matter at the temperatures $T=\{128,154,192\}\mathrm{MeV}$ with a fixed lattice spacing $a=0.064\mathrm{fm}$ and volume $V=(6.1\text{fm})^3$. We find that the pion quasiparticle, defined as the low-energy pole in the two-point function of the axial charge, becomes lighter as the temperature increases and give an argument based on hydrodynamics as to why the pole becomes purely diffusive above the chiral crossover. We study the thermal modification of the isovector vector spectral function using the Backus-Gilbert method, finding an enhancement at low energies and a depletion at energies around $1\mathrm{GeV}$.The analogous study of the axial-vector channel reveals a larger enhancement at energies below $1\mathrm{GeV}$, and we show that these findings are consistent with rigorous spectral sum rules. The difference between vector and axial-vector correlators, an order parameter for chiral symmetry, turns out to be overall suppressed by more than an order of magnitude at the crossover., Comment: 47 pages, 20 figures. arXiv admin note: text overlap with arXiv:2211.15558

Type Ia supernova (SN Ia) light curves have a secondary maximum that exists in the $r$, $i$, and near-infrared filters. The secondary maximum is relatively weak in the $r$ band, but holds the advantage that it is accessible, even at high redshift. We used Gaussian Process fitting to parameterise the light curves of 893 SNe Ia from the Zwicky Transient Facility's (ZTF) second data release (DR2), and we were able to extract information about the timing and strength of the secondary maximum. We found $>5\sigma$ correlations between the light curve decline rate ($\Delta m_{15}(g)$) and the timing and strength of the secondary maximum in the $r$ band. Whilst the timing of the secondary maximum in the $i$ band also correlates with $\Delta m_{15}(g)$, the strength of the secondary maximum in the $i$ band shows significant scatter as a function of $\Delta m_{15}(g)$. We found that the transparency timescales of 97 per cent of our sample are consistent with double detonation models, and that SNe Ia with small transparency timescales ($<$ 32 d) reside predominantly in locally red environments. We measured the total ejected mass for the normal SNe Ia in our sample using two methods, and both were consistent with medians of $1.3\ \pm \ 0.3$ and $1.2\ \pm\ 0.2$ solar masses. We find that the strength of the secondary maximum is a better standardisation parameter than the SALT light curve stretch ($x_1$). Finally, we identified a spectral feature in the $r$ band as Fe II, which strengthens during the onset of the secondary maximum. The same feature begins to strengthen at $<$ 3 d post maximum light in 91bg-like SNe. Finally, the correlation between $x_1$ and the strength of the secondary maximum was best fit with a broken line, with a split at $x_1^0\ =\ -0.5\ \pm\ 0.2$, suggestive of the existence of two populations of SNe Ia., Comment: 18 pages, 14 figures

Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-2024 admissions cycle, these colleges faced unprecedented challenges. First, the number of applications has been steadily growing. Second, test-optional policies that have remained in place since the COVID-19 pandemic limit access to key information historically predictive of academic success. Most recently, longstanding debates over affirmative action culminated in the Supreme Court banning race-conscious admissions. Colleges have explored machine learning (ML) models to address the issues of scale and missing test scores, often via ranking algorithms intended to focus on 'top' applicants. However, the Court's ruling will force changes to these models, which were able to consider race as a factor in ranking. There is currently a poor understanding of how these mandated changes will shape applicant ranking algorithms, and, by extension, admitted classes. We seek to address this by quantifying the impact of different admission policies on the applications prioritized for review. We show that removing race data from a developed applicant ranking algorithm reduces the diversity of the top-ranked pool without meaningfully increasing the academic merit of that pool. We contextualize this impact by showing that excluding data on applicant race has a greater impact than excluding other potentially informative variables like intended majors. Finally, we measure the impact of policy change on individuals by comparing the arbitrariness in applicant rank attributable to policy change to the arbitrariness attributable to randomness. We find that any given policy has a high degree of arbitrariness and that removing race data from the ranking algorithm increases arbitrariness in outcomes for most applicants., Comment: 25 pages, 8 figures

NLP in mental health has been primarily social media focused. Real world practitioners also have high case loads and often domain specific variables, of which modern LLMs lack context. We take a dataset made by recruiting 644 participants, including individuals diagnosed with Bipolar Disorder (BD), Schizophrenia (SZ), and Healthy Controls (HC). Participants undertook tasks derived from a standardized mental health instrument, and the resulting data were transcribed and annotated by experts across five clinical variables. This paper demonstrates the application of contemporary language models in sequence-to-sequence tasks to enhance mental health research. Specifically, we illustrate how these models can facilitate the deployment of mental health instruments, data collection, and data annotation with high accuracy and scalability. We show that small models are capable of annotation for domain-specific clinical variables, data collection for mental-health instruments, and perform better then commercial large models.

In response to the rapidly escalating costs of computing with large matrices and tensors caused by data movement, several lossy compression methods have been developed to significantly reduce data volumes. Unfortunately, all these methods require the data to be decompressed before further computations are done. In this work, we develop a lossy compressor that allows a dozen fairly fundamental operations directly on compressed data while offering good compression ratios and modest errors. We implement a new compressor PyBlaz based on the familiar GPU-powered PyTorch framework, and evaluate it on three non-trivial applications, choosing different number systems for internal representation. Our results demonstrate that the compressed-domain operations achieve good scalability with problem sizes while incurring errors well within acceptable limits. To our best knowledge, this is the first such lossy compressor that supports compressed-domain operations while achieving acceptable performance as well as error., Comment: An extended but earlier version of paper in https://dl.acm.org/doi/10.1145/3624062.3625122 published at the DRBSD Workshop in 2023

As TeV gamma-ray astronomy progresses into the era of the Cherenkov Telescope Array (CTA), instantaneously following up on gamma-ray transients is becoming more important than ever. To this end, a worldwide network of Imaging Atmospheric Cherenkov Telescopes has been proposed. Australia is ideally suited to provide coverage of part of the Southern Hemisphere sky inaccessible to H.E.S.S. in Namibia and the upcoming CTA-South in Chile. This study assesses the sources detectable by a small, transient-focused array in Australia based on CTA telescope designs. The TeV emission of extragalactic sources (including the majority of gamma-ray transients) can suffer significant absorption by the extragalactic background light. As such, we explored the improvements possible by implementing stereoscopic and topological triggers, as well as lowered image cleaning thresholds, to access lower energies. We modelled flaring gamma-ray sources based on past measurements from the satellite-based gamma-ray telescope Fermi-LAT. We estimate that an array of four Medium-Sized Telescopes (MSTs) would detect $\sim$24 active galactic nucleus flares >5$\sigma$ per year, up to a redshift of $z\approx1.5$. Two MSTs achieved $\sim$80-90% of the detections of four MSTs. The modelled Galactic transients were detectable within the observation time of one night, 11 of the 21 modelled gamma-ray bursts were detectable, as were $\sim$10% of unidentified transients. An array of MST-class telescopes would thus be a valuable complementary telescope array for transient TeV gamma-ray astronomy., Comment: 13 pages, 13 figures, 4 tables, accepted for publication in PASA

The Pierre Auger Observatory is the most sensitive instrument to detect photons with energies above $10^{17}$ eV. It measures extensive air showers generated by ultra high energy cosmic rays using a hybrid technique that exploits the combination of a fluorescence detector with a ground array of particle detectors. The signatures of a photon-induced air shower are a larger atmospheric depth of the shower maximum ($X_{max}$) and a steeper lateral distribution function, along with a lower number of muons with respect to the bulk of hadron-induced cascades. In this work, a new analysis technique in the energy interval between 1 and 30 EeV (1 EeV = $10^{18}$ eV) has been developed by combining the fluorescence detector-based measurement of $X_{max}$ with the specific features of the surface detector signal through a parameter related to the air shower muon content, derived from the universality of the air shower development. No evidence of a statistically significant signal due to photon primaries was found using data collected in about 12 years of operation. Thus, upper bounds to the integral photon flux have been set using a detailed calculation of the detector exposure, in combination with a data-driven background estimation. The derived 95% confidence level upper limits are 0.0403, 0.01113, 0.0035, 0.0023, and 0.0021 km$^{-2}$ sr$^{-1}$ yr$^{-1}$ above 1, 2, 3, 5, and 10 EeV, respectively, leading to the most stringent upper limits on the photon flux in the EeV range. Compared with past results, the upper limits were improved by about 40% for the lowest energy threshold and by a factor 3 above 3 EeV, where no candidates were found and the expected background is negligible. The presented limits can be used to probe the assumptions on chemical composition of ultra-high energy cosmic rays and allow for the constraint of the mass and lifetime phase space of super-heavy dark matter particles., Comment: 19 pages, 22 figures

We report an investigation of the mass composition of cosmic rays with energies from 3 to 100 EeV (1 EeV=$10^{18}$ eV) using the distributions of the depth of shower maximum $X_\mathrm{max}$. The analysis relies on ${\sim}50,000$ events recorded by the Surface Detector of the Pierre Auger Observatory and a deep-learning-based reconstruction algorithm. Above energies of 5 EeV, the data set offers a 10-fold increase in statistics with respect to fluorescence measurements at the Observatory. After cross-calibration using the Fluorescence Detector, this enables the first measurement of the evolution of the mean and the standard deviation of the $X_\mathrm{max}$ distributions up to 100 EeV. Our findings are threefold: (1.) The evolution of the mean logarithmic mass towards a heavier composition with increasing energy can be confirmed and is extended to 100 EeV. (2.) The evolution of the fluctuations of $X_\mathrm{max}$ towards a heavier and purer composition with increasing energy can be confirmed with high statistics. We report a rather heavy composition and small fluctuations in $X_\mathrm{max}$ at the highest energies. (3.) We find indications for a characteristic structure beyond a constant change in the mean logarithmic mass, featuring three breaks that are observed in proximity to the ankle, instep, and suppression features in the energy spectrum., Comment: submitted to Phys. Rev. D, 28 pages, 18 figures, 5 tables

We present measurements of the atmospheric depth of the shower maximum $X_\mathrm{max}$, inferred for the first time on an event-by-event level using the Surface Detector of the Pierre Auger Observatory. Using deep learning, we were able to extend measurements of the $X_\mathrm{max}$ distributions up to energies of 100 EeV ($10^{20}$ eV), not yet revealed by current measurements, providing new insights into the mass composition of cosmic rays at extreme energies. Gaining a 10-fold increase in statistics compared to the Fluorescence Detector data, we find evidence that the rate of change of the average $X_\mathrm{max}$ with the logarithm of energy features three breaks at $6.5\pm0.6~(\mathrm{stat})\pm1~(\mathrm{sys})$ EeV, $11\pm 2~(\mathrm{stat})\pm1~(\mathrm{sys})$ EeV, and $31\pm5~(\mathrm{stat})\pm3~(\mathrm{sys})$ EeV, in the vicinity to the three prominent features (ankle, instep, suppression) of the cosmic-ray flux. The energy evolution of the mean and standard deviation of the measured $X_\mathrm{max}$ distributions indicates that the mass composition becomes increasingly heavier and purer, thus being incompatible with a large fraction of light nuclei between 50 EeV and 100 EeV., Comment: submitted to Phys. Rev. Lett., 10 pages, 3 figures, 1 table

To get distances, Type Ia Supernovae magnitudes are corrected for their correlation with lightcurve width and colour. Here we investigate how this standardisation is affected by the SN environment, with the aim to reduce scatter and improve standardisation. We first study the SN Ia stretch distribution, as well as its dependence on environment, as characterised by local and global (g-z) colour and stellar mass. We then look at the standardisation parameter $\alpha$, which accounts for the correlation between residuals and stretch, along with its environment dependence and linearity. We finally compute magnitude offsets between SNe in different astrophysical environments after colour and stretch standardisation, aka steps. This analysis is made possible due to the unprecedented statistics of the ZTF SN Ia DR2 volume-limited sample. The stretch distribution exhibits a bimodal behaviour, as previously found in literature. However, we find the distribution means to decrease with host stellar mass at a 9.0$\sigma$ significance. We demonstrate, at the 14.3$\sigma$ level, that the stretch-magnitude relation is non-linear, challenging the usual linear stretch-residuals relation. Fitting for a broken-$\alpha$ model, we indeed find two different slopes between stretch regimes ($x_1<-0.49\pm0.06$): $\alpha_{low}=0.28\pm0.01$ and $\alpha_{high}=0.09\pm0.01$, a $\Delta_{\alpha}=-0.19\pm0.01$ difference. As the relative proportion of SNe Ia in the high-/low-stretch modes evolves with redshift and environment, this implies that a linear $\alpha$ also evolves with redshift and environment. Concerning the environmental magnitude offset $\gamma$, we find it to be greater than 0.14 mag regardless of the considered environmental tracer used (local or global colour and stellar mass), all measured at the $\geq 6\sigma$ level, increased to $\sim0.18\pm0.01$ mag when accounting for the stretch-non linearity., Comment: 15 pages, 9 figures, submitted to Astronomy and Astrophysics

The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from preference data. This involves building datasets where each element is a quadruplet composed of a prompt, two independent responses (completions of the prompt) and a human preference between the two independent responses, yielding a preferred and a dis-preferred response. Such data is typically scarce and expensive to collect. On the other hand, \emph{single-trajectory} datasets where each element is a triplet composed of a prompt, a response and a human feedback is naturally more abundant. The canonical element of such datasets is for instance an LLM's response to a user's prompt followed by a user's feedback such as a thumbs-up/down. Consequently, in this work, we propose DRO, or \emph{Direct Reward Optimisation}, as a framework and associated algorithms that do not require pairwise preferences. DRO uses a simple mean-squared objective that can be implemented in various ways. We validate our findings empirically, using T5 encoder-decoder language models, and show DRO's performance over selected baselines such as Kahneman-Tversky Optimization (KTO). Thus, we confirm that DRO is a simple and empirically compelling method for single-trajectory policy optimisation.

In this paper we describe our search for galaxy protocluster candidates at $4.5< z < 10$ and explore the environmental and physical properties of their member galaxies identified through JWST wide-field surveys within the CEERS, JADES, and PEARLS NEP-TDF fields. Combining with HST data, we identify 2948 robust $z>4.5$ candidates within an area of 185.4 arcmin$^2$. We determine nearest neighbour statistics and galaxy environments. We find that high-$z$ galaxies in overdense environments exhibit higher star formation activity compared to those in underdense regions. Galaxies in dense environments have a slightly increased SFR at a given mass compared with galaxies in the lower density environments. At the high mass end we also find a gradual flattening of the $M_{\star}$-SFR slope. We find that galaxies in high-density regions often have redder UV slopes than those in low-density regions, suggesting more dust extinction, weaker Lyman-alpha emission and / or a higher damped Lyman-alpha absorption. We also find that the mass-size relation remains consistent and statistically similar across all environments. Furthermore, we quantitatively assess the probability of a galaxy belonging to a protocluster candidate. In total, we identified 26 overdensities at $z=5-7$ and estimate their dark matter halo masses. We find that all protocluster candidates could evolve into clusters with $M_{\rm halo} > 10^{14}M_{\odot}$ at $z = 0$, thereby supporting the theoretical and simulation predictions of cluster formation. Notably, this marks an early search for protocluster candidates in JWST wide field based on photometric data, providing valuable candidates to study cosmic structure formation at the early stages., Comment: 23 pages, 14 figures and 7 table, submitted to MNRAS

Different models of dark matter can alter the distribution of mass in galaxy clusters in a variety of ways. However, so can uncertain astrophysical feedback mechanisms. Here we present a Machine Learning method that ''learns'' how the impact of dark matter self-interactions differs from that of astrophysical feedback in order to break this degeneracy and make inferences on dark matter. We train a Convolutional Neural Network on images of galaxy clusters from hydro-dynamic simulations. In the idealised case our algorithm is 80% accurate at identifying if a galaxy cluster harbours collisionless dark matter, dark matter with ${\sigma}_{\rm DM}/m = 0.1$cm$^2/$g or with ${\sigma}_{DM}/m = 1$cm$^2$/g. Whilst we find adding X-ray emissivity maps does not improve the performance in differentiating collisional dark matter, it does improve the ability to disentangle different models of astrophysical feedback. We include noise to resemble data expected from Euclid and Chandra and find our model has a statistical error of < 0.01cm$^2$/g and that our algorithm is insensitive to shape measurement bias and photometric redshift errors. This method represents a new way to analyse data from upcoming telescopes that is an order of magnitude more precise and many orders faster, enabling us to explore the dark matter parameter space like never before., Comment: Accepted Nature Astronomy

We pursue transfer learning to improve classifier accuracy on a target task with few labeled examples available for training. Recent work suggests that using a source task to learn a prior distribution over neural net weights, not just an initialization, can boost target task performance. In this study, we carefully compare transfer learning with and without source task informed priors across 5 datasets. We find that standard transfer learning informed by an initialization only performs far better than reported in previous comparisons. The relative gains of methods using informative priors over standard transfer learning vary in magnitude across datasets. For the scenario of 5-300 examples per class, we find negative or negligible gains on 2 datasets, modest gains (between 1.5-3 points of accuracy) on 2 other datasets, and substantial gains (>8 points) on one dataset. Among methods using informative priors, we find that an isotropic covariance appears competitive with learned low-rank covariance matrix while being substantially simpler to understand and tune. Further analysis suggests that the mechanistic justification for informed priors -- hypothesized improved alignment between train and test loss landscapes -- is not consistently supported due to high variability in empirical landscapes. We release code to allow independent reproduction of all experiments.

Orthogonal arrays are a type of combinatorial design that were developed in the 1940s in the design of statistical experiments. In 1947, Rao proved a lower bound on the size of any orthogonal array, and raised the problem of constructing arrays of minimum size. Kuperberg, Lovett and Peled (2017) gave a non-constructive existence proof of orthogonal arrays whose size is near-optimal (i.e., within a polynomial of Rao's lower bound), leaving open the question of an algorithmic construction. We give the first explicit, deterministic, algorithmic construction of orthogonal arrays achieving near-optimal size for all parameters. Our construction uses algebraic geometry codes. In pseudorandomness, the notions of $t$-independent generators or $t$-independent hash functions are equivalent to orthogonal arrays. Classical constructions of $t$-independent hash functions are known when the size of the codomain is a prime power, but very few constructions are known for an arbitrary codomain. Our construction yields algorithmically efficient $t$-independent hash functions for arbitrary domain and codomain.

Improved two-body and three-body potentials for helium have been used to calculate from first principles the third density and acoustic virial coefficients for both $^4$He and $^3$He. For the third density virial coefficient $C(T)$, uncertainties have been reduced by a factor of 4--5 compared to the previous state of the art; the accuracy of first-principles $C(T)$ now exceeds that of the best experiments by more than two orders of magnitude. The range of calculations has been extended to temperatures as low as 0.5~K. For the third acoustic virial coefficient $\gamma_a(T)$, we applied the Schlessinger Point Method, which can calculate $\gamma_a$ and its uncertainty based on the $C(T)$ data, overcoming some limitations of direct path-integral calculation. The resulting $\gamma_a$ are calculated at temperatures down to 0.5~K; they are consistent with available experimental data but have much smaller uncertainties. The first-principles data presented here will enable improvement of primary temperature and pressure metrology based on gas properties., Comment: 21 pages, 14 figures