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Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-performing models lack transparency in their dataset curation and model development processes, posing an obstacle to the development of fully open language models. In this paper, we identify three core data-related challenges that must be addressed to advance open-source language models. These include (1) transparency in model development, including the data curation process, (2) access to large quantities of high-quality data, and (3) availability of artifacts and metadata for dataset curation and analysis. To address these challenges, we release RedPajama-V1, an open reproduction of the LLaMA training dataset. In addition, we release RedPajama-V2, a massive web-only dataset consisting of raw, unfiltered text data together with quality signals and metadata. Together, the RedPajama datasets comprise over 100 trillion tokens spanning multiple domains and with their quality signals facilitate the filtering of data, aiming to inspire the development of numerous new datasets. To date, these datasets have already been used in the training of strong language models used in production, such as Snowflake Arctic, Salesforce's XGen and AI2's OLMo. To provide insight into the quality of RedPajama, we present a series of analyses and ablation studies with decoder-only language models with up to 1.6B parameters. Our findings demonstrate how quality signals for web data can be effectively leveraged to curate high-quality subsets of the dataset, underscoring the potential of RedPajama to advance the development of transparent and high-performing language models at scale., Comment: 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmarks

We highlight the potential benefits of a synergistic use of SKAO and ESO facilities for galaxy evolution studies, focusing on the role that ESO spectroscopic surveys can play in supporting next-generation radio continuum and atomic hydrogen (HI) surveys. More specifically we illustrate the role that currently available or soon to be operational ESO multiplex spectrographs can play for three classes of projects: large/deep redshift survey campaigns, integral field unit/Atacama Large Millimeter/submillimeter Array (IFU/ALMA) surveys of selected regions of sky, and IFU/ALMA follow-ups of selected samples. We conclude with some general recommendations for an efficient joint exploitation of ESO-SKAO surveys., Comment: 6 pages, 4 figures. Based on the discussion outcomes of the splinter session "Galaxies and Galaxy Evolution" of the 2023 "Coordinated Surveys of the Southern Sky" workshop, Garching b. Munchen, February 27 - March 3, 2023

Artificial intelligence (AI) for fluid mechanics has become attractive topic. High-fidelity data is one of most critical issues for the successful applications of AI in fluid mechanics, however, it is expensively obtained or even inaccessible. This study proposes a high-efficient data forward generation method from the partial differential equations (PDEs). Specifically, the solutions of the PDEs are first generated either following a random field (e.g. Gaussian random field, GRF, computational complexity O(NlogN), N is the number of spatial points) or physical laws (e.g. a kind of spectra, computational complexity O(NM), M is the number of modes), then the source terms, boundary conditions and initial conditions are computed to satisfy PDEs. Thus, the data pairs of source terms, boundary conditions and initial conditions with corresponding solutions of PDEs can be constructed. A Poisson neural network (Poisson-NN) embedded in projection method and a wavelet transform convolutional neuro network (WTCNN) embedded in multigrid numerical simulation for solving incompressible Navier-Stokes equations is respectively proposed. The feasibility of generated data for training Poisson-NN and WTCNN is validated. The results indicate that even without any DNS data, the generated data can train these two models with excellent generalization and accuracy. The data following physical laws can significantly improve the convergence rate, generalization and accuracy than that generated following GRF.

We consider a simple but infinite class of staked links known as bongles. We provide necessary and sufficient conditions for these bongles to be hyperbolic. Then, we prove that all balanced hyperbolic $n$-bongles have the same volume and the corresponding volume is an upper bound on the volume of any hyperbolic $n$-bongle for $n$ even. Moreover, all hyperbolic $n$-bongles have volume strictly less than $5n(1.01494\dots)$. We also include explicit volume calculations for all hyperbolic 3-bongles through 6-bongles., Comment: 16 pages, 11 figures

The proxy pattern is a well-known design pattern with numerous use cases in several sectors of the software industry. As such, the use of the proxy pattern is also a common approach in the development of complex decentralized applications (DApps) on the Ethereum blockchain. Despite the importance of proxy contracts, little is known about (i) how their prevalence changed over time, (ii) the ways in which developers integrate proxies in the design of DApps, and (iii) what proxy types are being most commonly leveraged by developers. This study bridges these gaps through a comprehensive analysis of Ethereum smart contracts, utilizing a dataset of 50 million contracts and 1.6 billion transactions as of September 2022. Our findings reveal that 14.2% of all deployed smart contracts are proxy contracts. We show that proxy contracts are being more actively used than non-proxy contracts. Also, the usage of proxy contracts in various contexts, transactions involving proxy contracts, and adoption of proxy contracts by users have shown an upward trend over time, peaking at the end of our study period. They are either deployed through off-chain scripts or on-chain factory contracts, with the former and latter being employed in 39.1% and 60.9% of identified usage contexts in turn. We found that while the majority (67.8%) of proxies act as an interceptor, 32.2% enables upgradeability. Proxy contracts are typically (79%) implemented based on known reference implementations with 29.4% being of type ERC-1167, a class of proxies that aims to cheaply reuse and clone contracts' functionality. Our evaluation shows that our proposed behavioral proxy detection method has a precision and recall of 100% in detecting active proxies. Finally, we derive a set of practical recommendations for developers and introduce open research questions to guide future research on the topic.

Software applications that run on a blockchain platform are known as DApps. DApps are built using smart contracts, which are immutable after deployment. Just like any real-world software system, DApps need to receive new features and bug fixes over time in order to remain useful and secure. However, Ethereum lacks native solutions for post-deployment smart contract maintenance, requiring developers to devise their own methods. A popular method is known as the upgradeability proxy contract (UPC), which involves implementing the proxy design pattern (as defined by the Gang of Four). In this method, client calls first hit a proxy contract, which then delegates calls to a certain implementation contract. Most importantly, the proxy contract can be reconfigured during runtime to delegate calls to another implementation contract, effectively enabling application upgrades. For researchers, the accurate detection of UPCs is a strong requirement in the understanding of how exactly real-world DApps are maintained over time. For practitioners, the accurate detection of UPCs is crucial for providing application behavior transparency and enabling auditing. In this paper, we introduce UPC Sentinel, a novel three-layer algorithm that utilizes both static and dynamic analysis of smart contract bytecode to accurately detect active UPCs. We evaluated UPC Sentinel using two distinct ground truth datasets. In the first dataset, our method demonstrated a near-perfect accuracy of 99%. The evaluation on the second dataset further established our method's efficacy, showing a perfect precision rate of 100% and a near-perfect recall of 99.3%, outperforming the state of the art. Finally, we discuss the potential value of UPC Sentinel in advancing future research efforts., Comment: Accepted for publication in Empirical Software Engineering

Earth-like planets in the circumstellar habitable zone (HZ) may have dramatically different climate outcomes depending on their spin-orbit parameters, altering their habitability for life as we know it. We present a suite of 93 ROCKE-3D general circulation models (GCMs) for planets with the same surface conditions and average annual insolation as Earth, but with a wide range of rotation periods, obliquities, orbital eccentricities, and longitudes of periastra. Our habitability metric $f_\mathrm{HZ}$ is calculated based on the temperature and precipitation in each model across grid cells over land. Latin Hypercube Sampling (LHS) aids in sampling all 4 of the spin-orbit parameters with a computationally feasible number of GCM runs. Statistical emulation then allows us to model $f_\mathrm{HZ}$ as a smooth function with built-in estimates of statistical uncertainty. We fit our emulator to an initial set of 46 training runs, then test with an additional 46 runs at different spin-orbit values. Our emulator predicts the directly GCM-modeled habitability values for the test runs at the appropriate level of accuracy and precision. For orbital eccentricities up to 0.225, rotation period remains the primary driver of the fraction of land that remains above freezing and with precipitation above a threshold value. For rotation periods greater than $\sim 20$ days, habitability drops significantly (from $\sim 70$% to $\sim 20$%), driven primarily by cooler land temperatures. Obliquity is a significant secondary factor for rotation periods less than $\sim 20$ Earth days, with a factor of two impact on habitability that is maximized at intermediate obliquity., Comment: Accepted for publication in the Astrophysical Journal

We present a structural analysis of 521 galaxy candidates at 6.5 < z < 12.5, with $SNR > 10\sigma$ in the F444W filter, taken from the EPOCHS v1 sample, consisting of uniformly reduced deep JWST NIRCam data, covering the CEERS, JADES GOOD-S, NGDEEP, SMACS0723, GLASS and PEARLS surveys. We use standard software to fit single S\'ersic models to each galaxy in the rest-frame optical and extract their parametric structural parameters (S\'ersic index, half-light radius and axis-ratio), and \texttt{Morfometryka} to measure their non-parametric concentration and asymmetry parameters. We find a wide range of sizes for these early galaxies, but with a strong galaxy-size mass correlation up to $z \sim 12$ such that galaxy sizes continue to get progressively smaller in the high-redshift regime, following $R_{e} = 2.74 \pm 0.49 \left( 1 + z \right) ^{-0.79 \pm 0.08}$ kpc. Using non-parametric methods we find that galaxy merger fractions, classified through asymmetry parameters, at these redshifts remain consistent with those in literature, maintaining a value of $f_{m} \sim 0.12 \pm 0.07$ showing little dependence with redshift when combined with literature at $z > 4$. We find that galaxies which are smaller in size also appear rounder, with an excess of high axis-ratio objects. Finally, we artificially redshift a subsample of our objects to determine how robust the observational trends we see are, determining that observed trends are due to real evolutionary effects, rather than being a consequence of redshift effects., Comment: 35 pages, 13 figures. Submitted to ApJ on 19/12/2024. Comments to corresponding author welcome at lewi.westcott@manchester.ac.uk

Rydberg atoms are used in a wide range of applications due to their peculiar properties like strong dipolar and van der Waals interactions. The choice of Rydberg state has a huge impact on the strength and angular dependence of the interactions, and so a detailed understanding of the underlying processes and resulting properties of the interactions is therefore key to select the most suitable states for experiments. We study the van der Waals interactions in alkali atoms in detail and highlight the structures which allow an understanding and exploitation of the various interaction properties. A particular theme is the identification of F\"orster resonances with $n_1 \neq n_2$, which offer interaction potentials with a wide range of properties that make them particularly interesting for experimental applications. A second theme is a focus on the underlying structures that shape the angular dependency and sign of the interactions. This understanding -- instead of brute-force calculations -- allows for a much simpler and more systematic search for suitable pair states. These insights can be used for the selection of tailored interaction potentials subject to experimental constraints and requirements. We use rubidium as an example species in this work and also provide data for cesium and pair states that are coupled via two- or three-photon transitions, i.e. up to F states, in the appendix., Comment: 6 pages (5 figures) plus appendix (16 pages, 33 figures)

The origins of life is a question that continues to intrigue scientists across disciplines. One theory - the iron-sulphur theory - suggests that reactions essential to the synthesis of biological materials got their catalytic 'spark' from mineral surfaces such as iron pyrite, commonly known as fool's gold. Additionally, the binding affinity of the ligands synthesised in this 'surface metabolism' acted as an early version of natural selection: more strongly-binding ligands were accumulated into further autocatalytic reactions and the aggregation of complex biological materials. Ligand-receptor binding is thus fundamental to the origins of life. In this paper, we use the iron-sulphur theory as a lens through which to review ligand-receptor interactions as they are more commonly understood today. In particular we focus on the electron tunnelling theory of receptor activation that has emerged from research into quantum biology. We revisit criticism against this theory, particularly the lack of evidence for electron transfer in receptors, to see what insights might be offered by ligand-receptor interactions mediated by iron pyrite at the origins of life. What emerges from this comparison is the central importance of redox activity in receptors, in particular with respect to the recurring presence of the disulphide bond. While the paper is a speculative exercise, we conclude that conductivity in biomolecules, particularly the selective conductivity conferred by appropriate ligand-receptor binding, is a powerful tool for understanding diverse phenomena such as pharmacological potency and viral infection. As such it deserves further investigation.

Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.

Stellar streams from disrupted globular clusters are dynamically cold structures that are sensitive to perturbations from dark matter subhalos, allowing them in principle to trace the dark matter substructure in the Milky Way. We model, within the context of $\Lambda$CDM, the likelihood of dark matter subhalos to produce a significant feature in a GD-1-like stream and analyze the properties of such subhalos. We generate a large number of realizations of the subhalo population within a Milky Way mass host halo, accounting for tidal stripping and dynamical friction, using the semi-analytic code SatGen. The subhalo distributions are combined with a GD-1-like stream model, and the impact of subhalos that pass close to the stream are modeled with Gala. We find that subhalos with masses in the range $5\times 10^6 M_{\odot} - 10^8 M_{\odot}$ at the time of the stream-subhalo encounter, corresponding to masses of about $4 \times 10^7 M_{\odot} - 8 \times 10^8 M_{\odot}$ at the time of infall, are the likeliest to produce gaps in a GD-1-like stream. We find that gaps occur on average $\sim$1.8 times per realization of the host system. These gaps have typical widths of $\sim(7 - 27)$ deg and fractional underdensities of $\sim (10 - 30)\%$, with larger gaps being caused by more-massive subhalos. The stream-subhalo encounters responsible for these have impact parameters $(0.1 - 1.5)$ kpc and relative velocities $\sim(170 - 410)$ km/s. For a larger host-halo mass, the number of subhalos increases, as do their typical velocities, inducing a corresponding increase in the number of significant stream-subhalo encounters., Comment: 20 pages, 10 figures

We present a detailed investigation into the abundance and morphology of high redshift quenched galaxies at $3 < z < 7$ using James Webb Space Telescope data in the NEP, CEERS and JADES fields. Within these fields, we identify 90 candidate passive galaxies using specific star formation rates modelled with the BAGPIPES SED fitting code, which is more effective at identifying recently quenched systems than the classical UVJ method. With this sample of galaxies, we find number densities broadly consistent with other works and a rapidly evolving passive fraction of high mass galaxies ($\log_{10}{(M_{\star}/M_{\odot})} > $ 9.5) between $3 < z < 5$. We find that the fraction of galaxies with low star formation rates and mass 9.5 $ < \log_{10}{(M_{\star}/M_{\odot})} < $ 10.5 decreases from $\sim$25% at $3 < z < 4$ to $\sim$2% at $5 < z < 7$. Our passive sample of galaxies is shown to exhibit more compact light profiles compared to star-forming counterparts and some exhibit traces of AGN activity through detections in either the X-ray or radio. At the highest redshifts ($z > 6.5$) passive selections start to include examples of 'little red dots' which complicates any conclusions until their nature is better understood., Comment: 24 pages, 5 tables, 11 figures, submitted to MNRAS

We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosmic-ray arrival direction distribution in the Southern Hemisphere with unprecedented statistical accuracy covering more than a full period length of a solar cycle. Improvements in Monte Carlo event simulation and better handling of year-to-year differences in data processing significantly reduce systematic uncertainties below the level of statistical fluctuations compared to the previously published results. We confirm the observation of a change in the angular structure of the cosmic-ray anisotropy between 10 TeV and 1 PeV, more specifically in the 100-300 TeV energy range.

We report on the coherent creation, control and read-out of a single photonic qutrit in a Rydberg ensemble. In each measurement, an optical photon is stored as a Rydberg polariton through electromagnetically induced transparency. Employing two microwave fields, the polariton is driven into an arbitrary superposition of three collective states, each encoded in a Rydberg state. The collective state is mapped into a photonic time-bin qutrit with the microwave field and read out sequentially. The complete sequence, including preparation, control, and read-out, is less than 1.8~$\mu$s, which mitigates decoherence significantly. We measure the coherence of the qutrit with non-destructive Ramsey interferometry, which is preferable for quantum information processing, and find good quantitative agreement with the theoretical model. The ability to write, process and read out the single photonic qutrit on microsecond time scales with microwave coupled Rydberg states demonstrates the coherent connectivity among the high Hilbert space of the qutrit.Our study is an important step in exploring qutrit based quantum information processes and quantum simulation of topological physics with microwave coupled Rydberg atom ensembles., Comment: 7 pages, 4 figures

We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the host star reaches the main sequence in 1 Gyr and that planet c lies close to the inner boundary of the habitable zone. We find the magma ocean stage can last up to 50 Myr, remove 8 Earth-oceans of water, and leave up to 2000 bars of oxygen in the atmosphere. However, if the planet forms with a hydrogen envelope as small as 0.1 Earth-masses, no water will be lost during the star's pre-main sequence phase from thermal escape processes. We find that the planets are unlikely to be in a 3:1 mean motion resonance and that both planets tidally circularize within 0.5 Gyr when tidal dissipation is held constant. However, if tidal dissipation is a function of mantle temperature and rheology, then we find that planet c's orbit may require more than 7 Gyr to circularize, during which time tidal heating may reach hundreds of terawatts. We thus conclude that the habitability of planet c depends most strongly on the initial volatile content and internal properties, but no data yet preclude the viability of an active biosphere on the planet., Comment: 16 pages, 9 figures, accepted to PSJ

The common theory is that human pilot's performance degrades when responsible for an increased number of uncrewed aircraft systems (UAS). This theory was developed in the early 2010's for ground robots and not highly autonomous UAS. It has been shown that increasing autonomy can mitigate some performance impacts associated with increasing the number of UAS. Overall, the Oregon State University-Wing collaboration seeks to understand what factors negatively impact a pilot's ability to maintain responsibility and control over an assigned set of active UAS. The Phase I evaluation establishes baseline data focused on the number of UAS and the number of nests increase. This evaluation focuses on nominal operations as well as crewed aircraft encounters and adverse weather changes. The results demonstrate that the pilots were actively engaged and had very good situation awareness. Manipulation of the conditions did not result in any significant differences in overall workload. The overall results debunk the theory that increasing the number of UAS is detrimental to pilot's performance., Comment: 45 pages, 10 figures, 21 tables

Consider a large system of $N$ Brownian motions in $\R ^d$ fixed on a time interval $[0,\beta]$ with symmetrized initial and terminal conditions, under the influence of a trap potential. Such systems describe systems of bosons at positive temperatures confined in a spatial domain. We describe the large $N$ behavior of the averaged path (that is, their empirical path measure) and its connection with a well known optimal transport problem formulated by Erwin Schr\"odinger. We also explore the asymptotic behavior of the Brownian motions in terms of Large Deviations. In particular, the rate function that governs the mean of occupation measures turns out to be the well-known Donsker-Varadhan rate function. We therefore prove a simple formula for the large $N$ asymptotic of the symmetrized trace of $e^{-\beta \Hcal_N}$, where $\Hcal_N$ is an $N$ particle Hamilton operator in a trap

Liver transplant can be a life-saving procedure for patients with end-stage liver disease. With the introduction of modern immunosuppressive therapies, short-term survival has significantly improved. However, long-term survival has not substantially improved in decades. Consequently, causes of death are now more likely to be due to the toxicities and side-effects of long-term immunosuppression rather than rejection. In order to study the balance of immunosuppression and rejection, we developed the first mechanistic mathematical model of liver transplant and immune system dynamics. We determined key cells and interactions in the model using literature information; we then used sensitivity analysis to determine key pathways driving the health status of the transplanted liver. We found that dynamics related to cytotoxic T cells and IL-2, in addition to the liver itself, are key determinants of liver graft injury. This has significant implications for the use of tests to monitor patients, and therapeutic strategies to prevent or treat liver transplantation rejection. Future work to collect appropriate data and parametrize the model would be valuable in improving our understanding of the dynamics of this system. We also note that our model could be tailored to model transplant of other organs., Comment: 34 pages, 5 figures

Cyber-attacks pose a security threat to military command and control networks, Intelligence, Surveillance, and Reconnaissance (ISR) systems, and civilian critical national infrastructure. The use of artificial intelligence and autonomous agents in these attacks increases the scale, range, and complexity of this threat and the subsequent disruption they cause. Autonomous Cyber Defence (ACD) agents aim to mitigate this threat by responding at machine speed and at the scale required to address the problem. Sequential decision-making algorithms such as Deep Reinforcement Learning (RL) provide a promising route to create ACD agents. These algorithms focus on a single objective such as minimizing the intrusion of red agents on the network, by using a handcrafted weighted sum of rewards. This approach removes the ability to adapt the model during inference, and fails to address the many competing objectives present when operating and protecting these networks. Conflicting objectives, such as restoring a machine from a back-up image, must be carefully balanced with the cost of associated down-time, or the disruption to network traffic or services that might result. Instead of pursing a Single-Objective RL (SORL) approach, here we present a simple example of a multi-objective network defence game that requires consideration of both defending the network against red-agents and maintaining critical functionality of green-agents. Two Multi-Objective Reinforcement Learning (MORL) algorithms, namely Multi-Objective Proximal Policy Optimization (MOPPO), and Pareto-Conditioned Networks (PCN), are used to create two trained ACD agents whose performance is compared on our Multi-Objective Cyber Defence game. The benefits and limitations of MORL ACD agents in comparison to SORL ACD agents are discussed based on the investigations of this game., Comment: 9 pages, 9 figures

Examining reflected light from exoplanets aids in our understanding of the scattering properties of their atmospheres and will be a primary task of future flagship space- and ground-based telescopes. We introduce an enhanced capability of Planetary Intensity Code for Atmospheric Scattering Observations (PICASO), an open-source radiative transfer model used for exoplanet and brown dwarf atmospheres, to produce reflected light phase curves from three-dimensional atmospheric models. Since PICASO is coupled to the cloud code Virga, we produce phase curves for different cloud condensate species and varying sedimentation efficiencies (fsed) and apply this new functionality to Kepler-7b, a hot Jupiter with phase curve measurements dominated by reflected starlight. We model three different cloud scenarios for Kepler-7b: MgSiO3 clouds only, Mg2SiO4 clouds only, and Mg2SiO4, Al2O3, and TiO2 clouds. All our Virga models reproduce the cloudy region west of the substellar point expected from previous studies, as well as clouds at high latitudes and near the eastern limb, which are primarily composed of magnesium silicates. Al2O3 and TiO2 clouds dominate near the substellar point. We then compare our modeled reflected light phase curves to Kepler observations and find that models with all three cloud condensate species and low sedimentation efficiencies (0.03 - 0.1) match best, though our reflected light phase curves show intensities approximately one-third of those observed by Kepler. We conclude that a better understanding of zonal transport, cloud radiative feedback, and particle scattering properties is needed to further explain the differences between the modeled and observed reflected light fluxes.

From transmission electron microscopy and other laboratory studies of presolar grains, the implicit condensation sequence of carbon-bearing condensates in circumstellar envelopes of carbon stars is (from first to last) TiC-graphite-SiC. We use thermochemical equilibrium condensation calculations and show that the condensation sequence of TiC, graphite, and SiC depends on metallicity in addition to C/O ratio and total pressure. Calculations were performed for a characteristic carbon star ratio of C/O = 1.2 from 1E-10 to 1E-4 bars total pressure and for uniform metallicity variations ranging from 0.01 to 100 times solar elemental abundances. TiC always condenses before SiC, and the carbide condensation temperatures increase with increasing metallicity and total pressure. Graphite, however, can condense in a cooling circumstellar envelope before TiC, between TiC and SiC, or after SiC, depending on the carbon-bearing gas chemistry, which is dependent on metallicity and total pressure. Analytical expressions for the graphite, TiC, and SiC condensation temperatures as functions of metallicity and total pressure are presented. The inferred sequence from laboratory presolar grain studies, TiC-graphite-SiC, is favored under equilibrium conditions at solar and subsolar metallicities between ~1E-5 to 1E-8 bar total pressure within circumstellar envelopes of carbon stars with nominal C/O = 1.2. We also explored the dependence of the sequence at C/O ratios of 1.1 and 3.0 and found that as the C/O ratio increases, the TiC-graphite-SiC region shifts towards higher total pressures and lower metallicities., Comment: 33 pages, 9 figures, 2 tables. Preprint submitted to The Astrophysical Journal, November 2024

The observed star formation rates of galaxies in the Local Universe suggests that they are replenishing their gas reservoir across cosmic time. Cosmological simulations predict that this accretion of fresh gas can occur in a hot or a cold mode, yet the existence of low column density ($\sim10^{17}$ cm$^{-2}$) neutral atomic hydrogen (HI) tracing the cold mode has not been unambiguously confirmed by observations. We present the application of unconstrained spectral stacking to attempt to detect the emission from this HI in the Circum-Galactic Medium (CGM) and Inter-Galactic Medium (IGM) of 6 nearby star forming galaxies from the MHONGOOSE sample for which full-depth observations are available. Our stacking procedure consists of a standard spectral stacking algorithm coupled with a one-dimensional spectral line finder designed to extract reliable signal close to the noise level. In agreement with previous studies, we found that the amount of signal detected outside the HI disk is much smaller than implied by simulations. Furthermore, the column density limit that we achieve via stacking ($\sim10^{17}$ cm$^{-2}$) suggests that direct detection of the neutral CGM/IGM component might be challenging in the future, even with the next generation of radio telescopes., Comment: Accepted for publication in Astronomy & Astrophysics

Abundant geomorphological and geochemical evidence of liquid water on the surface of early Mars during the late Noachian and early Hesperian periods needs to be reconciled with a fainter young Sun. While a dense CO2 atmosphere and related warming mechanisms are potential solutions to the early Mars climate problem, further investigation is warranted. Here, we complete a comprehensive survey of the warming potential of all known greenhouse gases and perform detailed calculations for 15 different minor gas species under early Martian conditions. We find that of these 15 species, H2O2, HNO3, NH3, SO2, and C2H4 cause significant greenhouse warming at concentrations of ~0.1 ppmv or greater. However, the most highly effective greenhouse gas species also tend to be more condensable, soluble and vulnerable to photolytic destruction. To provide a reference for future atmospheric evolution and photochemical studies, we have made our warming potential database freely available online., Comment: 14 pages, 12 figures, published in JGR Planets

The precise characterization and mitigation of systematic effects is one of the biggest roadblocks impeding the detection of the fluctuations of cosmological 21cm signals. Missing data in radio cosmological experiments, often due to radio frequency interference (RFI), poses a particular challenge to power spectrum analysis as it could lead to the ringing of bright foreground modes in Fourier space, heavily contaminating the cosmological signals. Here we show that the problem of missing data becomes even more arduous in the presence of systematic effects. Using a realistic numerical simulation, we demonstrate that partially flagged data combined with systematic effects can introduce significant foreground ringing. We show that such an effect can be mitigated through inpainting the missing data. We present a rigorous statistical framework that incorporates the process of inpainting missing data into a quadratic estimator of the 21cm power spectrum. Under this framework, the uncertainties associated with our inpainting method and its impact on power spectrum statistics can be understood. These results are applied to the latest Phase II observations taken by the Hydrogen Epoch of Reionization Array, forming a crucial component in power spectrum analyses as we move toward detecting 21cm signals in the ever more noisy RFI environment., Comment: 25 pages, 11 figures; Replaced to match accepted ApJ version. New version contains small editorial changes throughout in response to referee comments, no changes to results

Let $G$ be a compact, connected metric graph and let $X\subseteq G$ be a subset. If $X$ is sufficiently dense in $G$, we show that the Gromov--Hausdorff distance matches the Hausdorff distance, namely $d_{GH}(G,X)=d_{H}(G,X)$. In a recent study, when the metric graph is the circle $G=S^1$ with circumference $2\pi$, the equality $d_{GH}(S^1,X)=d_{H}(S^1,X)$ was established whenever $d_{GH}(S^1,X)<\frac{\pi}{6}$. Our result for general metric graphs relaxes this hypothesis in the circle case to $d_{GH}(S^1,X)<\frac{\pi}{3}$, and furthermore, we give an example showing that the constant $\frac{\pi}{3}$ is the best possible. We lower bound the Gromov--Hausdorff distance $d_{GH}(G,X)$ by the Hausdorff distance $d_{H}(G,X)$ via a simple topological obstruction: showing a correspondence with too small distortion contradicts the connectedness of $G$. Moreover, for two sufficiently dense subsets $X,Y\subseteq G$, we provide new lower bounds on $d_{GH}(X,Y)$ in terms of the Hausdorff distance $d_{H}(X,Y)$, which is $O(n^2)$-time computable if the subsets have at most $n$ points.

Light-induced phase transitions offer a method to dynamically modulate topological states in bulk complex materials. Yet, next-generation devices demand nanoscale architectures with contact resistances near the quantum limit and precise control over local electronic properties. The layered material WTe$_2$ has gained attention as a likely Weyl semimetal, with topologically protected linear electronic band crossings hosting massless chiral fermions. Here, we demonstrate a topological phase transition facilitated by light-induced shear motion of a single atomic layer at the surface of bulk WTe$_2$, thereby opening the door to nanoscale device concepts. Ultrafast terahertz fields enhanced at the apex of an atomically sharp tip resonantly couple to the key interlayer shear mode of WTe$_2$ via a ferroelectric dipole at the interface, inducing a structural phase transition at the surface to a metastable state. Subatomically resolved differential imaging, combined with hybrid-level density functional theory, reveals a shift of 7 $\pm$ 3 picometres in the top atomic plane. Tunnelling spectroscopy links electronic changes across the phase transition with the electron and hole pockets in the band structure, suggesting a reversible, light-induced annihilation of the topologically-protected Fermi arc surface states in the top atomic layer.

We conduct a large-scale empirical user study in a live setup to evaluate the acceptance of LLM-generated comments and their impact on the review process. This user study was performed in two organizations, Mozilla (which has its codebase available as open source) and Ubisoft (fully closed-source). Inside their usual review environment, participants were given access to RevMate, an LLM-based assistive tool suggesting generated review comments using an off-the-shelf LLM with Retrieval Augmented Generation to provide extra code and review context, combined with LLM-as-a-Judge, to auto-evaluate the generated comments and discard irrelevant cases. Based on more than 587 patch reviews provided by RevMate, we observed that 8.1% and 7.2%, respectively, of LLM-generated comments were accepted by reviewers in each organization, while 14.6% and 20.5% other comments were still marked as valuable as review or development tips. Refactoring-related comments are more likely to be accepted than Functional comments (18.2% and 18.6% compared to 4.8% and 5.2%). The extra time spent by reviewers to inspect generated comments or edit accepted ones (36/119), yielding an overall median of 43s per patch, is reasonable. The accepted generated comments are as likely to yield future revisions of the revised patch as human-written comments (74% vs 73% at chunk-level)., Comment: 12pages

Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, critical barriers to their widespread use include low signal-to-noise ratio (SNR), generally low image quality, and long scan duration. As these systems can operate in unusual environments, the level and spectral characteristics of the environmental electromagnetic inference (EMI) noise can change substantially across sites and scans, further reducing image quality. Methods for accelerating acquisition and boosting image quality are of critical importance to enable clinically actionable high-quality imaging in these systems. Despite the role that compressed sensing (CS) and artificial intelligence (AI)-based methods have had in improving image quality for high-field MRI, their adoption for low-field imaging is in its infancy, and it is unclear how robust these methods are in low SNR regimes. Here, we investigate and compare leading CS and AI-based methods for image reconstruction from subsampled data and perform a thorough analysis of their performance across a range of SNR values. We compare classical L1-wavelet CS with leading data-driven and model-driven AI methods. Experiments are performed using publicly available datasets and our own low-field and high-field experimental data. Specifically, we apply an unrolled AI network to low-field MRI, and find it outperforms competing reconstruction methods. We prospectively deploy our undersampling methods to accelerate imaging on a 6.5 mT MRI scanner. This work highlights the potential and pitfalls of advanced reconstruction techniques in low-field MRI, paving the way for broader clinical applications.

We address the question of how to connect predictions by hydrodynamic models of how sea lice move in water to observable measures that count the number of lice on each fish in a cage in the water. This question is important for management and regulation of aquacultural practice that tries to maximise food production and minimise risk to the environment. We do this through a simple rule-based model of interaction between sea lice and caged fish. The model is simple: sea lice can attach and detach from a fish. The model has a novel feature, encoding what is known as a master equation producing a time-series of distributions of lice on fish that one might expect to find if a cage full of fish were placed at any given location. To demonstrate how this works, and to arrive at a rough estimate of the interaction rates, we fit a simplified version of the model with three free parameters to publicly available data about an experiment with sentinel cages in Loch Linnhe in Scotland. Our construction, coupled to the hydrodynamic models driven by surveillance data from industrial farms, quantifies the environmental impact as: what would the infection burden look like in a notional cage at any location and how does it change with time?

In this paper we share the experience of the U.S. National Science Foundation (NSF) National Solar Observatory (NSO) scientists, educators and public outreach officers organizing an eclipse viewing party at a sports complex stadium on the US/Mexico border in Eagle Pass, TX in collaboration with educators from Eagle Pass and Uvalde areas. We describe reasons we chose Eagle Pass, contacts we established with the local community, preparations for and activities set up during the eclipse viewing party, the eclipse day on April 8 2024 and lessons learned from organizing our event., Comment: 17 pages, 9 figures, submitted to Bulletin of the AAS: Celebrating the Wonder of Science in the Shadow II

Galaxy mergers are a key driver of galaxy formation and evolution, including the triggering of AGN and star formation to a still unknown degree. We thus investigate the impact of galaxy mergers on star formation and AGN activity using a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields (CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, and MACS-0416), collectively covering an unmasked area of 189 arcmin$^2$. We focuses on star formation rate (SFR) enhancement, AGN fraction, and AGN excess in major merger ($\mu > 1/4$) close-pair samples, defined by $\Delta z < 0.3$ and projected separations $r_p < 100$ kpc, compared to non-merger samples. We find that SFR enhancement occurs only at $r_p < 20$ kpc, with values of $0.25 \pm 0.10$ dex and $0.26 \pm 0.11$ dex above the non-merger medians for $z = [4.5, 6.5]$ and $z = [6.5, 8.5]$, respectively. No other statistically significant enhancements in galaxy sSFR or stellar mass are observed at any projected separation or redshift bin. We also compare our observational results with predictions from the SC-SAM simulation and find no evidence of star formation enhancement in the simulations at any separation range. Finally, we examine the AGN fraction and AGN excess, finding that the fraction of AGNs in AGN-galaxy pairs, relative to the total AGN population, is $3.25^{+1.50}_{-1.06}$ times greater than the fraction of galaxy pairs relative to the overall galaxy population at the same redshift. We find that nearly all AGNs have a companion within 100 kpc and observe an excess AGN fraction in close-pair samples compared to non-merger samples. This excess is found to be $1.26 \pm 0.06$ and $1.34 \pm 0.06$ for AGNs identified via the inferred BPT diagram and photometric SED selection, respectively., Comment: 17 Pages, 7 Figures, Submitted to MNRAS

Floquet systems are periodically driven systems. In this framework, the system Hamiltonian and associated spectra of interest are modified, giving rise to new quantum phases of matter and nonequilibrium dynamics without static counterparts. Here we experimentally demonstrate a self-induced Floquet system in the interacting Rydberg gas. This originates from the photoionization of thermal Rydberg gases in a static magnetic field. Importantly, by leveraging the Rydberg electromagnetically induced transparency spectrum, we probe the nonequilibrium dynamics in the bistable regime and identify the emergence of a discrete time crystalline phase. Our work fills the experimental gap in the understanding the relation of multistability and dissipative discrete time crystalline phase. In this regard, it constitutes a highly controlled platform for exploring exotic nonequilibrium physics in dissipative interacting systems., Comment: 6 pages, 4 figures

We present the first broadband near- to mid-infrared (3-12 microns) transmission spectrum of the highly-irradiated (T_eq = 981 K) M dwarf rocky planet L 168-9 b (TOI-134 b) observed with the NIRSpec and MIRI instruments aboard JWST. We measure the near-infrared transit depths to a combined median precision of 20 ppm across the three visits in 54 spectroscopic channels with uniform widths of 60 pixels (~0.2 microns wide; R~100), and the mid-infrared transit depths to 61 ppm median precision in 48 wavelength bins (~0.15 microns wide; R~50). We compare the transmission spectrum of L 168-9 b to a grid of 1D thermochemical equilibrium forward models, and rule out atmospheric metallicities of less than 100x solar (mean molecular weights <4 g mol$^{-1}$) to 3-sigma confidence assuming high surface pressure (>1 bar), cloudless atmospheres. Based on photoevaporation models for L 168-9 b with initial atmospheric mass fractions ranging from 2-100%, we find that this planet could not have retained a primordial H/He atmosphere beyond the first 200 Myr of its lifetime. Follow-up MIRI eclipse observations at 15 microns could make it possible to confidently identify a CO2-dominated atmosphere on this planet if one exists., Comment: 19 pages, 11 figures, accepted for publication in The Astronomical Journal

Code translation aims to convert a program from one programming language (PL) to another. This long-standing software engineering task is crucial for modernizing legacy systems, ensuring cross-platform compatibility, enhancing performance, and more. However, automating this process remains challenging due to many syntactic and semantic differences between PLs. Recent studies show that even advanced techniques such as large language models (LLMs), especially open-source LLMs, still struggle with the task. Currently, code LLMs are trained with source code from multiple programming languages, thus presenting multilingual capabilities. In this paper, we investigate whether such multilingual capabilities can be harnessed to enhance code translation. To achieve this goal, we introduce InterTrans, an LLM-based automated code translation approach that, in contrast to existing approaches, leverages intermediate translations across PLs to bridge the syntactic and semantic gaps between source and target PLs. InterTrans contains two stages. It first utilizes a novel Tree of Code Translation (ToCT) algorithm to plan transitive intermediate translation sequences between a given source and target PL, then validates them in a specific order. We evaluate InterTrans with three open LLMs on three benchmarks (i.e., CodeNet, HumanEval-X, and TransCoder) involving six PLs. Results show an absolute improvement between 18.3% to 43.3% in Computation Accuracy (CA) for InterTrans over Direct Translation with 10 attempts. The best-performing variant of InterTrans (with Magicoder LLM) achieved an average CA of 87.3%-95.4% on three benchmarks.

To help MLOps engineers decide which operator to use in which deployment scenario, this study aims to empirically assess the accuracy vs latency trade-off of white-box (training-based) and black-box operators (non-training-based) and their combinations in an Edge AI setup. We perform inference experiments including 3 white-box (i.e., QAT, Pruning, Knowledge Distillation), 2 black-box (i.e., Partition, SPTQ), and their combined operators (i.e., Distilled SPTQ, SPTQ Partition) across 3 tiers (i.e., Mobile, Edge, Cloud) on 4 commonly-used Computer Vision and Natural Language Processing models to identify the effective strategies, considering the perspective of MLOps Engineers. Our Results indicate that the combination of Distillation and SPTQ operators (i.e., DSPTQ) should be preferred over non-hybrid operators when lower latency is required in the edge at small to medium accuracy drop. Among the non-hybrid operators, the Distilled operator is a better alternative in both mobile and edge tiers for lower latency performance at the cost of small to medium accuracy loss. Moreover, the operators involving distillation show lower latency in resource-constrained tiers (Mobile, Edge) compared to the operators involving Partitioning across Mobile and Edge tiers. For textual subject models, which have low input data size requirements, the Cloud tier is a better alternative for the deployment of operators than the Mobile, Edge, or Mobile-Edge tier (the latter being used for operators involving partitioning). In contrast, for image-based subject models, which have high input data size requirements, the Edge tier is a better alternative for operators than Mobile, Edge, or their combination.