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Chemical abundances of stellar streams can be used to determine the nature of a stream's progenitor. Here we study the progenitor of the recently discovered Leiptr stellar stream, which was previously suggested to be a tidally disrupted halo globular cluster. We obtain high-resolution spectra of five red giant branch stars selected from the Gaia DR2 STREAMFINDER catalog with Magellan/MIKE. One star is a clear non-member. The remaining four stars display chemical abundances consistent with those of a low-mass dwarf galaxy: they have a low mean metallicity, $\langle{\rm[Fe/H]}\rangle = -2.2$; they do not all have identical metallicities; and they display low [$\alpha$/Fe] $\sim 0$ and [Sr/Fe] and [Ba/Fe] $\sim -1$. This pattern of low $\alpha$ and neutron-capture element abundances is only found in intact dwarf galaxies with stellar mass $\lesssim 10^5 M_\odot$. Although more data are needed to be certain, Leiptr's chemistry is consistent with being the lowest-mass dwarf galaxy stream without a known intact progenitor, possibly in the mass range of ultra-faint dwarf galaxies. Leiptr thus preserves a record of one of the lowest-mass early accretion events into the Milky Way., Comment: 18 pages, 12 figures, 8 tables, to be submitted to The Open Journal of Astrophysics

Analysing stellar parameters and abundances from nearly one million Gaia DR3 Radial Velocity Spectrometer (RVS) spectra poses challenges due to the limited spectral coverage (restricted to the infrared Ca II triplet) and variable signal-to-noise ratios of the data. To address this, we use The Cannon, a data-driven method, to transfer stellar parameters and abundances from the GALAH Data Release 4 (DR4; R ~ 28,000) catalogue to the lower resolution Gaia DR3 RVS spectra (R ~ 11,500). Our model, trained on 14,484 common targets, predicts parameters such as Teff, log g, and [Fe/H], along with several other elements across approximately 800,000 Gaia RVS spectra. We utilise stars from open and globular clusters present in the Gaia RVS catalogue to validate our predicted mean [Fe/H] with high precision (~0.02-0.10 dex). Additionally, we recover the bimodal distribution of [Ti/Fe] versus [Fe/H], reflecting the high and low alpha-components of Milky Way disk stars, demonstrating The Cannon's capability for accurate stellar abundance determination from medium-resolution Gaia RVS spectra. The methodologies and resultant catalogue presented in this work highlight the remarkable potential of the RVS dataset, which by the end of the Gaia mission will comprise spectra of over 200 million stars., Comment: Submitted to MNRAS, 16 pages, 15 figures

The precision measurement of the tritium $\beta$-decay spectrum performed by the KATRIN experiment provides a unique way to search for general neutrino interactions (GNI). All theoretical allowed GNI terms involving neutrinos are incorporated into a low-energy effective field theory, and can be identified by specific signatures in the measured tritium $\beta$-spectrum. In this paper an effective description of the impact of GNI on the $\beta$-spectrum is formulated and the first constraints on the effective GNI parameters are derived based on the 4 Mio. electrons collected in the second measurement campaign of KATRIN in 2019. In addition, constraints on selected types of interactions are investigated, thereby exploring the potential of KATRIN to search for more specific new physics cases, including a right-handed W boson, a charged Higgs or leptoquarks.

Connect Four is a two-player game where each player attempts to be the first to create a sequence of four of their pieces, arranged horizontally, vertically, or diagonally, by dropping pieces into the columns of a grid of width seven and height six, in alternating turns. Mis\`ere Connect Four is played by the same rules, but with the opposite objective: do not connect four. This paper announces that Mis\`ere Connect Four is solved: perfect play by both sides leads to a second-player win. More generally, this paper also announces that Mis\`ere Connect $k$ played on a $w \times h$ board is also solved, but the outcome depends on the game's parameters $k$, $w$, and $h$, and may be a first-player win, a second-player win, or a draw. These results are constructive, meaning that we provide explicit strategies, thus enabling readers to impress their friends and foes alike with provably optimal play in the mis\`ere form of a table-top game for children., Comment: 12 pages, 4 figures, 2 tables, and a handful of proofs

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using virtual instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.

Term normalization is the process of mapping a term from free text to a standardized concept and its machine-readable code in an ontology. Accurate normalization of terms that capture phenotypic differences between patients and diseases is critical to the success of precision medicine initiatives. A large language model (LLM), such as GPT-4o, can normalize terms to the Human Phenotype Ontology (HPO), but it may retrieve incorrect HPO IDs. Reported accuracy rates for LLMs on these tasks may be inflated due to imbalanced test datasets skewed towards high-frequency terms. In our study, using a comprehensive dataset of 268,776 phenotype annotations for 12,655 diseases from the HPO, GPT-4o achieved an accuracy of 13.1% in normalizing 11,225 unique terms. However, the accuracy was unevenly distributed, with higher-frequency and shorter terms normalized more accurately than lower-frequency and longer terms. Feature importance analysis, using SHAP and permutation methods, identified low-term frequency as the most significant predictor of normalization errors. These findings suggest that training and evaluation datasets for LLM-based term normalization should balance low- and high-frequency terms to improve model performance, particularly for infrequent terms critical to precision medicine.

Large language models (LLMs) have shown improved accuracy in phenotype term normalization tasks when augmented with retrievers that suggest candidate normalizations based on term definitions. In this work, we introduce a simplified retriever that enhances LLM accuracy by searching the Human Phenotype Ontology (HPO) for candidate matches using contextual word embeddings from BioBERT without the need for explicit term definitions. Testing this method on terms derived from the clinical synopses of Online Mendelian Inheritance in Man (OMIM), we demonstrate that the normalization accuracy of a state-of-the-art LLM increases from a baseline of 62.3% without augmentation to 90.3% with retriever augmentation. This approach is potentially generalizable to other biomedical term normalization tasks and offers an efficient alternative to more complex retrieval methods., Comment: Submitted to Frontiers in Digital Health

Stage IV large scale structure surveys are promising probes of gravity on cosmological scales. Due to the vast model-space in the modified gravity literature, model-independent parameterisations represent useful and scalable ways to test extensions of $\Lambda$CDM. In this work we use a recently validated approach of computing the non-linear $3\times 2$pt observables in modified gravity models with a time-varying effective gravitational constant $\mu$ and a gravitational slip $\eta$ that is binned in redshift to produce Fisher forecasts for an LSST Y10-like survey. We also include in our modelling an effective nulling scheme for weak-lensing by applying the BNT transformation that localises the weak-lensing kernel enabling well-informed scale cuts. We show that the combination of improved non-linear modelling and better control of the scales that are modelled/cut yields high precision constraints on the cosmological and modified gravity parameters. We find that 4 redshift bins for $\mu$ of width corresponding to equal incremental $\Lambda$CDM growth is optimal given the state-of-the-art modelling and show how the BNT transformation can be used to mitigate the impact of small-scale systematic effects, such as baryonic feedback., Comment: 27 pages, 12 figures A few typos corrected and a couple of small changes made to the text to improve presentation of results, added missing reference. Comments welcome!

Analyzing stop-and-go waves at the scale of miles and hours of data is an emerging challenge in traffic research. In the past, datasets were of limited scale and could be easily analyzed by hand or with rudimentary methods to identify a very limited set of traffic waves present within the data. This paper introduces an automatic and scalable stop-and-go wave identification method capable of capturing wave generation, propagation, dissipation, as well as bifurcation and merging, which have previously been observed only very rarely. Using a concise and simple critical-speed based definition of a stop-and-go wave, the proposed method identifies all wave boundaries that encompass spatio-temporal points where vehicle speed is below a chosen critical speed. The method is built upon a graph-based representation of the spatio-temporal points associated with stop-and-go waves, specifically wave front (start) points and wave tail (end) points, and approaches the solution as a graph component identification problem. The method is implemented in Python and demonstrated on a large-scale dataset, I-24 MOTION INCEPTION. New insights revealed from this demonstration with emerging phenomena include: (a) we demonstrate that waves do generate, propagate, and dissipate at a scale (miles and hours) and ubiquity never observed before; (b) wave fronts and tails travels at a consistent speed for a critical speed between 10-20 mph, with propagation variation across lanes, where wave speed on the outer lane are less consistent compared to those on the inner lane; (c) wave fronts and tails propagate at different speeds; (d) wave boundaries capture rich and non-trivial wave topologies, highlighting the complexity of waves.

We present a detailed study of the MASTER OT J030227.28+191754.5 outburst in 2021-2022, reaching an amplitude of 10.2 mag and a duration of 60 d. The detections of (1) the double-peaked optical emission lines, and (2) the early and ordinary superhumps, established that MASTER OT J030227.28+191754.5 is an extremely energetic WZ Sge-type dwarf nova (DN). Based on the superhump observations, we obtained its orbital period and mass ratio as 0.05986(1) d and 0.063(1), respectively. These are within a typical range of low-mass-ratio DNe. According to the binary parameters derived based on the thermal-tidal instability model, our analyses showed that (1) the standard disk model requires an accretion rate $\simeq$ 10$^{20}$ g s$^{-1}$ to explain its peak optical luminosity and (2) large mass was stored in the disk at the outburst onset. These cannot be explained solely by the impact of its massive ($\gtrsim$ 1.15 M$_\odot$) primary white dwarf implied by Kimura et al. (2023). Instead, we propose that the probable origin of this enormously energetic DN outburst is the even lower quiescence viscosity than other WZ Sge-type DNe. This discussion is qualitatively valid for most possible binary parameter spaces unless the inclination is low ($\lesssim 40^\circ$) enough for the disk to be bright explaining the outburst amplitude. Such low inclinations, however, would not allow detectable amplitude of early superhumps in the current thermal-tidal instability model. The optical spectra at outburst maximum showed the strong emission lines of Balmer, He I, and He II series whose core is narrower than $\sim 800$ km s$^{-1}$. Considering its binary parameters, a Keplerian disk cannot explain this narrow component, but the presumable origin is disk winds., Comment: 23 pages, 13 figures, 2 tables. Accepted by PASJ. Part of the online supplemental information is included

According to the "hard-steps" model, the origin of humanity required "successful passage through a number of intermediate steps" (so-called "hard" or "critical" steps) that were intrinsically improbable with respect to the total time available for biological evolution on Earth. This model similarly predicts that technological life analogous to human life on Earth is "exceedingly rare" in the universe. Here, we critically reevaluate the core assumptions of the hard-steps model in light of recent advances in the Earth and life sciences. Specifically, we advance a potential alternative model where there are no hard steps, and evolutionary novelties (or singularities) required for human origins can be explained via mechanisms outside of intrinsic improbability. Furthermore, if Earth's surface environment was initially inhospitable not only to human life, but also to certain key intermediate steps in human evolution (e.g., the origin of eukaryotic cells, multicellular animals), then the "delay" in the appearance of humans can be best explained through the sequential opening of new global environmental windows of habitability over Earth history, with humanity arising relatively quickly once the right conditions were established. In this co-evolutionary (or geobiological) scenario, humans did not evolve "early" or "late" with respect to the total lifespan of the biosphere, but "on time."

In recent years, there has been an increasing research interest in the application of remote sensing technology to highly aerated flows, which is because this technology holds the ultimate promise to enable safe and accurate measurements of real-word air-water flows in natural and human made environments. Despite the increasing number of publications, some fundamental questions, such as ``what do we measure'' or ``what can we measure'', have not been answered conclusively. In this study, we hypothesize that laser distance sensors are able to measure the concentration of entrapped air, which we demonstrate using two seminal air-water flow types, namely a submerged hydraulic jump and flows down a stepped spillway. By converting our free-surface signals into time series of instantaneous air concentrations, we also show that a dual laser triangulation setup enables the extraction of basic air-water flow parameters of the upper flow region, comprising interface count rates, interfacial velocities, and turbulence levels, while we acknowledge that some sensor characteristics, such as beam diameters, can lead to measurement biases. Overall, this study represents a major advancement in the remote measurement of air-water flow properties. Future collective research effort is required to overcome remaining challenges., Comment: 31 pages, 10 figures

The projected sensitivity of the effective electron neutrino-mass measurement with the KATRIN experiment is below 0.3 eV (90 % CL) after five years of data acquisition. The sensitivity is affected by the increased rate of the background electrons from KATRIN's main spectrometer. A special shifted-analysing-plane (SAP) configuration was developed to reduce this background by a factor of two. The complex layout of electromagnetic fields in the SAP configuration requires a robust method of estimating these fields. We present in this paper a dedicated calibration measurement of the fields using conversion electrons of gaseous $^\mathrm{83m}$Kr, which enables the neutrino-mass measurements in the SAP configuration., Comment: 19 pages, 11 figures

High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This study evaluates the automation of phenotyping of clinical summaries from the Online Mendelian Inheritance in Man (OMIM) database using large language models. Due to their rich phenotype data, these summaries can be surrogates for physician notes. We conduct a performance comparison of GPT-4 and GPT-3.5-Turbo. Our results indicate that GPT-4 surpasses GPT-3.5-Turbo in identifying, categorizing, and normalizing signs, achieving concordance with manual annotators comparable to inter-rater agreement. Despite some limitations in sign normalization, the extensive pre-training of GPT-4 results in high performance and generalizability across several phenotyping tasks while obviating the need for manually annotated training data. Large language models are expected to be the dominant method for automating high-throughput phenotyping of clinical text., Comment: Submitted to IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Houston TX

Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technologies have improved the ability to capture detailed traffic dynamics, yet such systems remain scarce and costly. In contrast, conventional traffic sensors are widely deployed but suffer from relatively coarse-grain data resolution, potentially impeding accurate analysis of stop-and-go waves. This article explores whether generative AI models can enhance the resolution of conventional traffic sensor to approximate the quality of high-fidelity observations. We present a novel approach using a conditional diffusion denoising model, designed to reconstruct fine-grained traffic speed field from radar-based conventional sensors via iterative refinement. We introduce a new dataset, I24-WaveX, comprising 132 hours of data from both low and high-fidelity sensor systems, totaling over 2 million vehicle miles traveled. Our approach leverages this dataset to formulate the traffic measurement enhancement problem as a spatio-temporal super-resolution task. We demonstrate that our model can effectively reproduce the patterns of stop-and-go waves, achieving high accuracy in capturing these critical traffic dynamics. Our results show promising advancements in traffic data enhancement, offering a cost-effective way to leverage existing low spatio-temporal resolution sensor networks for improved traffic analysis and management. We also open-sourced our trained model and code to facilitate further research and applications.

Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and errors in event identification and reporting make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Nashville Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75% of crashes. Our dataset and all preprocessing code needed to get started are publicly released at https://vu.edu/ft-aed/ to facilitate future research.

High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential to gaining value from electronic health records (EHR) in the support of precision medicine. Despite technological advances, high-throughput phenotyping remains a challenge. This study compares three computational approaches to high-throughput phenotyping: a Large Language Model (LLM) incorporating generative AI, a Natural Language Processing (NLP) approach utilizing deep learning for span categorization, and a hybrid approach combining word vectors with machine learning. The approach that implemented GPT-4 (a Large Language Model) demonstrated superior performance, suggesting that Large Language Models are poised to be the preferred method for high-throughput phenotyping of physician notes., Comment: Submitted to AMIA Annual Symposium 2024, San Francisco CA

The fact that neutrinos carry a non-vanishing rest mass is evidence of physics beyond the Standard Model of elementary particles. Their absolute mass bears important relevance from particle physics to cosmology. In this work, we report on the search for the effective electron antineutrino mass with the KATRIN experiment. KATRIN performs precision spectroscopy of the tritium $\beta$-decay close to the kinematic endpoint. Based on the first five neutrino-mass measurement campaigns, we derive a best-fit value of $m_\nu^{2} = {-0.14^{+0.13}_{-0.15}}~\mathrm{eV^2}$, resulting in an upper limit of $m_\nu < {0.45}~\mathrm{eV}$ at 90 % confidence level. With six times the statistics of previous data sets, amounting to 36 million electrons collected in 259 measurement days, a substantial reduction of the background level and improved systematic uncertainties, this result tightens KATRIN's previous bound by a factor of almost two., Comment: 61 pages, 20 figures, 2 tables

Bulk heterojunction (BHJ) organic solar cells have made remarkable inroads towards 20% efficiency, yet nonradiative recombination losses ({\Delta}Vnr) remain high compared to silicon and perovskite photovoltaics. Interfaces buried within BHJ blends hold the key to recombination losses but access to their energetic landscape underpinning charge transfer (CT) states and their disorder remain elusive. Here, we reveal the energetic landscape and CT state manifold of modern BHJs with both spatial and energetic resolutions and link the offset between singlet (ES1) and CT energy (ES1-CT) and interfacial energetic disorder with {\Delta}Vnr. We do so by locally mapping the energy distributions of modern PM6-based BHJs with IT4F, Y6 and PC71BM acceptors and combine it, for the first time, with sensitive EQE measurements, to visualize and quantify donor (D) and acceptor (A) energetics at interfaces and associated them with CT states within a modified Marcus framework. A key new ability is the identification of the specific BHJ interfaces associated with the CT manifold, including where the lowest energy CT states reside. Moreover, we quantify energy levels and electronic disorders directly at these and other interfaces and connect these contributions to the energy losses. We delineate the influences of S1-CT offset and interfacial energetic disorder on {\Delta}Vnr across morphologically varied BHJs. Our results show both factors influencing energy losses in different ways. We demonstrate that PM6:Y6 can achieve low {\Delta}Vnr by forming a nominally sharp D/A interface with exceptionally low interfacial disorder via judicious processing combined with a low S1 to CT offset. This provides a design rule to minimize {\Delta}Vnr for modern NFAs: sharp D/A interfaces with low S1 to CT offset exhibiting minimal interfacial disorder., Comment: Manuscript: 30 pages, 4 figures, 2 tables Supplementary information: 41 pages, 24 figures, 7 tables

The Parameterised Post-Newtonian (PPN) approach is the default framework for performing precision tests of gravity in nearby astrophysical systems. In recent works we have extended this approach for cosmological applications, and in this paper we use observations of the anisotropies in the Cosmic Microwave Background to constrain the time variation of the PPN parameters $\alpha$ and $\gamma$ between last scattering and the present day. We find their time-averages over cosmological history should be within $\sim 20\%$ of their values in GR, with $\bar{\alpha}=0.89^{+0.08}_{-0.09}$ and $\bar{\gamma}=0.90^{+0.07}_{-0.08}$ at the $68\%$ confidence level. We also constrain the time derivatives of these parameters, and find that their present-day values should be within a factor of two of the best Solar System constraints. Many of these results have no counter-part from Solar System observations, and are entirely new constraints on the gravitational interaction. In all cases, we find that the data strongly prefer $\bar{\alpha}\simeq \bar{\gamma}$, meaning that observers would typically find local gravitational physics to be compatible with GR, despite considerable variation of $\alpha$ and $\gamma$ being allowed over cosmic history. This study lays the groundwork for future precision tests of gravity that combine observations made over all cosmological and astrophysical scales of length and time., Comment: Comments welcome

Two mechanisms that have been used to study the evolution of cooperative behavior are altruistic punishment, in which cooperative individuals pay additional costs to punish defection, and multilevel selection, in which competition between groups can help to counteract individual-level incentives to cheat. Boyd, Gintis, Bowles, and Richerson have used simulation models of cultural evolution to suggest that altruistic punishment and pairwise group-level competition can work in concert to promote cooperation, even when neither mechanism can do so on its own. In this paper, we formulate a PDE model for multilevel selection motivated by the approach of Boyd and coauthors, modeling individual-level birth-death competition with a replicator equation based on individual payoffs and describing group-level competition with pairwise conflicts based on differences in the average payoffs of the competing groups. Building off of existing PDE models for multilevel selection with frequency-independent group-level competition, we use analytical and numerical techniques to understand how the forms of individual and average payoffs can impact the long-time ability to sustain altruistic punishment in group-structured populations. We find several interesting differences between the behavior of our new PDE model with pairwise group-level competition and existing multilevel PDE models, including the observation that our new model can feature a non-monotonic dependence of the long-time collective payoff on the strength of altruistic punishment. Going forward, our PDE framework can serve as a way to connect and compare disparate approaches for understanding multilevel selection across the literature in evolutionary biology and anthropology., Comment: 79 pages, 17 figures v2; Updated version of Section 5 with corrected versions of Figures 5.1 and 5.5, as well as new subsection and figure added to describe multilevel dynamics for Tullock contest function (Figure 5.5 in Section 5.3)

The layered van der Waals material CrCl3 exhibits very strongly bound ligand field excitons that control optoelectronic applications and are connected with magnetic ordering by virtue of their d-orbital origin. Time-resolved photoluminescence of these exciton populations at room temperature shows that their relaxation is dominated by exciton-exciton annihilation and that the spontaneous decay lifetime is very long. These observations allow the rough quantification of the exciton annihilation rate constant and contextualization in light of a recent theory of universal scaling behavior of the annihilation process.

In fMRI, capturing brain activation during a task is dependent on how quickly k-space arrays are obtained. Acquiring full k-space arrays, which are reconstructed into images using the inverse Fourier transform (IFT), that make up volume images can take a considerable amount of scan time. Under-sampling k-space reduces the acquisition time, but results in aliased, or "folded," images. GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) is a parallel imaging technique that yields full images from subsampled arrays of k-space. GRAPPA uses localized interpolation weights, which are estimated per-scan and fixed over time, to fill in the missing spatial frequencies of the subsampled k-space. Hence, we propose a Bayesian approach to GRAPPA (BGRAPPA) where space measurement uncertainty are assessed from the a priori calibration k-space arrays. The prior information is utilized to estimate the missing spatial frequency values from the posterior distribution and reconstruct into full field-of-view images. Our BGRAPPA technique successfully reconstructed both a simulated and experimental single slice image with less artifacts, reduced noise leading to an increased signal-to-noise ratio (SNR), and stronger power of task detection., Comment: 19 pages, 15 figures (23 pages and 19 figures including the supplementary material)

In hafnia-based thin-film ferroelectric devices, chemical phenomena during growth and processing such as oxygen vacancy formation and interfacial reactions appear to strongly affect device performance. However, the nanoscale structure, chemistry, and electrical potentials in these devices are not fully known, making it difficult to understand their influence on device properties. Here, we directly image the composition and electrostatic potential with nanometer resolution in the cross section of a nanocrystalline W / Hf$_{0.5}$Zr$_{0.5}$O$_{2-\delta}$ (HZO) / W ferroelectric capacitor using multimodal electron microscopy. This reveals a 1.4 nm wide tungsten sub-oxide interfacial layer formed at the bottom interface during fabrication which introduces a potential dip and leads to asymmetric switching fields. Additionally, the measured inner potential in HZO is consistent with the presence of about 20% oxygen vacancies and a negative built-in potential in HZO. These chemical and electrostatic details are important to characterize and tune to achieve high performance ferroelectric devices., Comment: 20 pages, 3 figures, 1 table, 3 supporting figures

We present the design of a portable coronagraph, CATEcor, that incorporates a novel "shaded truss" style of external occultation and serves as a proof-of-concept for that family of coronagraphs. The shaded truss design style has the potential for broad application in various scientific settings. We conceived CATEcor itself as a simple instrument to observe the corona during the darker skies available during a partial solar eclipse, or for students or interested amateurs to detect the corona under ideal non-eclipsed conditions. CATEcor is therefore optimized for simplicity and accessibility to the public. It is implemented using an existing dioptric telescope and an adapter rig that mounts in front of the objective lens, restricting the telescope aperture and providing external occultation. The adapter rig, including occulter, is fabricated using fusion deposition modeling (FDM; colloquially "3D printing"), greatly reducing cost. The structure is designed to be integrated with moderate care and may be replicated in a university or amateur setting. While CATEcor is a simple demonstration unit, the design concept, process, and trades are useful for other more sophisticated coronagraphs in the same general family, which might operate under normal daytime skies outside the annular-eclipse conditions used for CATEcor., Comment: 27pp; 12 figures; accepted to Solar Physics

Stellar mergers and accretion events have been crucial in shaping the evolution of the Milky Way (MW). These events have been dynamically identified and chemically characterised using red giants and main-sequence stars. RR Lyrae (RRL) variables can play a crucial role in tracing the early formation of the MW since they are ubiquitous, old (t$\ge$10 Gyr) low-mass stars and accurate distance indicators. We exploited Data Release 3 of the GALAH survey to identify 78 field RRLs suitable for chemical analysis. Using synthetic spectra calculations, we determined atmospheric parameters and abundances of Fe, Mg, Ca, Y, and Ba. Most of our stars exhibit halo-like chemical compositions, with an iron peak around [Fe/H]$\approx -$1.40, and enhanced Ca and Mg content. Notably, we discovered a metal-rich tail, with [Fe/H] values ranging from $-$1 to approximately solar metallicity. This sub-group includes almost ~1/4 of the sample, it is characterised by thin disc kinematics and displays sub-solar $\alpha$-element abundances, marginally consistent with the majority of the MW stars. Surprisingly, they differ distinctly from typical MW disc stars in terms of the s-process elements Y and Ba. We took advantage of similar data available in the literature and built a total sample of 535 field RRLs for which we estimated kinematical and dynamical properties. We found that metal-rich RRLs (1/3 of the sample) likely represent an old component of the MW thin disc. We also detected RRLs with retrograde orbits and provided preliminary associations with the Gaia-Sausage-Enceladus, Helmi, Sequoia, Sagittarius, and Thamnos stellar streams., Comment: Accepted for publication in MNRAS. 29 pages, 20 figures

Hyperpolarized (HP) 13C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13C MRI studies. We then show where the majority of these can be fit into existing DICOM Attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13C MRI data storage that will support future multi-site trials, research studies and technical developments of this imaging technique.

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.

The stellar rotation has an essential role in modifying the structure of the star and, therefore, the way these different interplays arise. On the other hand, changes in orbits impact the star's rotation and its evolution. The evolution of the star's rotation accounts for the angular momentum exchange with the planet and follows the effects of the internal transport of angular momentum and metallicity. Several models in the literature have aimed to find a theoretical way to study these interactions between the planet's orbital evolution and the star's rotation. Our work is a promising attempt to investigate these interactions from a model based on a new statistical approach. To this end, we propose a ``tidal interaction index'' that carries all the parameters of the star-planet system that can affect the transport of angular momentum and, consequently, the evolution of stellar rotation. This index is similar to the ``magnetic braking index'' whose most successful value equals 3, which expresses the seminal Skumunich law. Our model is computed for masses of the host star less than the Kraft limit for three orbital-rotation period regimes and the semi-major axis less than 1 AU. We consider planets with masses between 0.4M$_{\oplus}$ and 20M$_{\rm J}$ with orbital periods between 0.3 and 225 days. We show that the tidal index $q$ segregated by stellar mass without wind magnetic braking during the main-sequence phase is strongly anti-correlated with planetary mass. Finally, we conclude that in cases where planets retain less than 84\% of the total angular momentum within the system, the magnetic braking mechanism proves to be more effective than tidal interactions, irrespective of whether the planets' angular momentum surpasses that of the host star., Comment: 21 pages, 2 tables, submitted to Icarus

In the cold dark matter paradigm, our Galaxy is predicted to contain >10000 dark matter subhaloes in the $10^5-10^8M_\odot$ range which should be completely devoid of stars. Stellar streams are sensitive to the presence of these subhaloes, which can create small-scale features in streams if they pass closely enough. Modelling these encounters can therefore, potentially recover the subhalo's properties. In this work, we demonstrate this for streams generated in numerical simulations, modelled on eccentric orbits in a realistic Milky Way potential, which includes the Large Magellanic Cloud and the subhalo itself. We focus on a mock model of the ATLAS-Aliqa Uma stream and inject a $10^7 M_\odot$ subhalo, creating a similar discontinuous morphology to current observations. We then explore how well subhalo properties are recovered using mock stream observations, consisting of no observational errors, as well as assuming realistic observational setups. These setups include present day style observations, and what will be possible with 4MOST and Gaia DR5 in the future. We show that we can recover all parameters describing the impact even with uncertainties matching existing data, including subhalo positions, velocities, mass and scale radius. Modelling the subhalo on an orbit instead of assuming an impulse approximation, we greatly reduce the degeneracy between subhalo mass and velocity seen in previous works. However, we find a slight bias in the subhalo mass (~0.1 dex). This demonstrates that we should be able to reliably extract the properties of subhaloes with stellar streams in the near future., Comment: 13 pages, 14 figures. Submitted to MMRAS. Comments welcome!

We present results of a dual eclipse expedition to observe the solar corona from two sites during the annular solar eclipse of 2023 October 14, using a novel coronagraph designed to be accessible for amateurs and students to build and deploy. The coronagraph "CATEcor" builds on the standardized eclipse observing equipment developed for the Citizen CATE 2024 experiment. The observing sites were selected for likelihood of clear observations, for historic relevance (near the Climax site in the Colorado Rocky Mountains), and for centrality to the annular eclipse path (atop Sandia Peak above Albuquerque, New Mexico). The novel portion of CATEcor is an external occulter assembly that slips over the front of a conventional dioptric telescope, forming a "shaded-truss" externally occulted coronagraph. CATEcor is specifically designed to be easily constructed in a garage or "makerspace" environment. We successfully observed some bright features in the solar corona to an altitude of approximately 2.25 R$_\odot$ during the annular phases of the eclipse. Future improvements to the design, in progress now, will reduce both stray light and image artifacts; our objective is to develop a design that can be operated successfully by amateur astronomers at sufficient altitude even without the darkened skies of a partial or annular eclipse., Comment: Accepted by Solar Physics

Dimension reduction is increasingly applied to high-dimensional biomedical data to improve its interpretability. When datasets are reduced to two dimensions, each observation is assigned an x and y coordinates and is represented as a point on a scatter plot. A significant challenge lies in interpreting the meaning of the x and y axes due to the complexities inherent in dimension reduction. This study addresses this challenge by using the x and y coordinates derived from dimension reduction to calculate class and feature centroids, which can be overlaid onto the scatter plots. This method connects the low-dimension space to the original high-dimensional space. We illustrate the utility of this approach with data derived from the phenotypes of three neurogenetic diseases and demonstrate how the addition of class and feature centroids increases the interpretability of scatter plots., Comment: Submitted to 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

The Interstellar Boundary Explorer (IBEX) satellite collects data on energetic neutral atoms (ENAs) that provide insight into the heliosphere, the region surrounding our solar system and separating it from interstellar space. IBEX collects information on these particles and on extraneous ``background'' particles. While IBEX records how and when the different particles are observed, it does not distinguish between heliospheric ENA particles and incidental background particles. To address this issue, all IBEX data has historically been manually labeled as ``good'' ENA data, or ``bad'' background data. This manual culling process is incredibly time-intensive and contingent on subjective, manually-induced decision thresholds. In this paper, we develop a three-stage automated culling process, called LOTUS, that uses random forests to expedite and standardize the labelling process. In Stage 1, LOTUS uses random forests to obtain probabilities of observing true ENA particles on a per-observation basis. In Stage 2, LOTUS aggregates these probabilities to obtain predictions within small windows of time. In Stage 3, LOTUS refines these predictions. We compare the labels generated by LOTUS to those manually generated by the subject matter expert. We use various metrics to demonstrate that LOTUS is a useful automated process for supplementing and standardizing the manual culling process.

Deep phenotyping is the detailed description of patient signs and symptoms using concepts from an ontology. The deep phenotyping of the numerous physician notes in electronic health records requires high throughput methods. Over the past thirty years, progress toward making high throughput phenotyping feasible. In this study, we demonstrate that a large language model and a hybrid NLP model (combining word vectors with a machine learning classifier) can perform high throughput phenotyping on physician notes with high accuracy. Large language models will likely emerge as the preferred method for high throughput deep phenotyping of physician notes., Comment: Submitted to IEEE EMBS Summer conference 2024

Adiabatic and non-adiabatic electron dynamics have been proposed to explain electron heating across collisionless shocks. We analyze the evolution of the suprathermal electrons across 310 quasi-perpendicular shocks with $1.7

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. We explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest.

On current computer architectures, GMRES' performance can be limited by its communication cost to generate orthonormal basis vectors of the Krylov subspace. To address this performance bottleneck, its $s$-step variant orthogonalizes a block of $s$ basis vectors at a time, potentially reducing the communication cost by a factor of $s$. Unfortunately, for a large step size $s$, the solver can generate extremely ill-conditioned basis vectors, and to maintain stability in practice, a conservatively small step size is used, which limits the performance of the $s$-step solver. To enhance the performance using a small step size, in this paper, we introduce a two-stage block orthogonalization scheme. Similar to the original scheme, the first stage of the proposed method operates on a block of $s$ basis vectors at a time, but its objective is to maintain the well-conditioning of the generated basis vectors with a lower cost. The orthogonalization of the basis vectors is delayed until the second stage when enough basis vectors are generated to obtain higher performance. Our analysis shows the stability of the proposed two-stage scheme. The performance is improved because while the same amount of computation as the original scheme is required, most of the communication is done at the second stage of the proposed scheme, reducing the overall communication requirements. Our performance results with up to 192 NVIDIA V100 GPUs on the Summit supercomputer demonstrate that when solving a 2D Laplace problem, the two-stage approach can reduce the orthogonalization time and the total time-to-solution by the respective factors of up to $2.6\times$ and $1.6\times$ over the original $s$-step GMRES, which had already obtained the respective speedups of $2.1\times$ and $1.8\times$ over the standard GMRES. Similar speedups were obtained for 3D problems and for matrices from the SuiteSparse Matrix Collection., Comment: Accepted for publication in IPDPS'24

Blue Horizontal Branch (BHB) stars, excellent distant tracers for probing the Milky Way's halo density profile, are distinguished in the $(g-r)_0$ vs $(i-z)_0$ color space from another class of stars, blue straggler stars (BSs). We develop a Bayesian mixture model to classify BHB stars using high-precision photometry data from the Dark Energy Survey Data Release 2 (DES DR2). We select $\sim2100$ highly-probable BHBs based on their $griz$ photometry and the associated uncertainties, and use these stars to map the stellar halo over the Galactocentric radial range $20 \lesssim R \lesssim 70$ kpc. After excluding known stellar overdensities, we find that the number density $n_\star$ of BHBs can be represented by a power law density profile $n_\star \propto R^{-\alpha}$ with an index of $\alpha=4.28_{-0.12}^{+0.13}$, consistent with existing literature values. In addition, we examine the impact of systematic errors and the spatial inhomogeneity on the fitted density profile. Our work demonstrates the effectiveness of high-precision $griz$ photometry in selecting BHB stars. The upcoming photometric survey from the Rubin Observatory, expected to reach depths 2-3 magnitudes greater than DES during its 10-year mission, will enable us to investigate the density profile of the Milky Way's halo out to the virial radius, unravelling the complex processes of formation and evolution in our Galaxy., Comment: 22 pages, 18 figures. Submitted AAS Journal. Comments Welcome Code used in this work can be found at: https://github.com/sazabi4/Yu2024_BHB/

The main objective of the connected and automated vehicle (CAV) platoon control problem is to regulate CAVs' position while ensuring stability and accounting for vehicle dynamics. Although this problem has been studied in the literature, existing research has some limitations. This paper presents two new theoretical results that address these limitations: (i) the synthesis of unrealistic high-gain control parameters due to the lack of a systematic way to incorporate the lower and upper bounds on the control parameters, and (ii) the performance sensitivity to the communication delay due to inaccurate Taylor series approximation. To be more precise, taking advantage of the wellknown Pade approximation, this paper proposes a constrained CAV platoon controller synthesis that (i) systematically incorporates the lower and upper bounds on the control parameters, and (ii) significantly improves the performance sensitivity to the communication delay. The effectiveness of the presented results is verified through conducting extensive numerical simulations. The proposed controller effectively attenuates the stop-and-go disturbance -- a single cycle of deceleration followed by acceleration -- amplification throughout the mixed platoon (consisting of CAVs and human-driven vehicles). Modern transportation systems will benefit from the proposed CAV controls in terms of effective disturbance attenuation as it will potentially reduce collisions.

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. These signals, composed of magnitude and phase, offer a rich source of information for understanding brain functions. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data. In this paper, we propose a novel fully Bayesian model designed for analyzing single-subject complex-valued fMRI (cv-fMRI) data. Our model, which we refer to as the CV-M&P model, is distinctive in its comprehensive utilization of both magnitude and phase information in fMRI signals, allowing for independent prediction of different types of activation maps. We incorporate Gaussian Markov random fields (GMRFs) to capture spatial correlations within the data, and employ image partitioning and parallel computation to enhance computational efficiency. Our model is rigorously tested through simulation studies, and then applied to a real dataset from a unilateral finger-tapping experiment. The results demonstrate the model's effectiveness in accurately identifying brain regions activated in response to specific tasks, distinguishing between magnitude and phase activation.

Milky Way globular clusters (GCs) display chemical enrichment in a phenomenon called multiple stellar populations (MSPs). While the enrichment mechanism is not fully understood, there is a correlation between a cluster's mass and the fraction of enriched stars found therein. However, present-day GC masses are often smaller than their masses at the time of formation due to dynamical mass loss. In this work, we explore the relationship between mass and MSPs using the stellar stream 300S. We present the chemical abundances of eight red giant branch member stars in 300S with high-resolution spectroscopy from Magellan/MIKE. We identify one enriched star characteristic of MSPs and no detectable metallicity dispersion, confirming that the progenitor of 300S was a globular cluster. The fraction of enriched stars (12.5\%) observed in our 300S stars is less than the 50\% of stars found enriched in Milky Way GCs of comparable present-day mass ($\sim10^{4.5}$\msun). We calculate the mass of 300S's progenitor and compare it to the initial masses of intact GCs, finding that 300S aligns well with the trend between the system mass at formation and enrichment. 300S's progenitor may straddle the critical mass threshold for the formation of MSPs and can therefore serve as a benchmark for the stellar enrichment process. Additionally, we identify a CH star, with high abundances of \textit{s}-process elements, probably accreted from a binary companion. The rarity of such binaries in intact GCs may imply stellar streams permit the survival of binaries that would otherwise be disrupted., Comment: 15 pages, 7 tables, 8 figures. Accepted for publication in MNRAS

We use multi-spacecraft Magnetospheric Multiscale (MMS) observations to investigate electric fields and ion reflection at a non-stationary collisionless perpendicular plasma shock. We identify sub-proton scale (5-10 electron inertial lengths) large-amplitude normal electric fields, balanced by the Hall term ($\mathbf{J} \times \mathbf{B}/ne$), as a transient feature of the shock ramp related to non-stationarity (rippling). The associated electrostatic potential, comparable to the energy of the incident solar wind protons, decelerates incident ions and reflects a significant fraction of protons, resulting in more efficient shock-drift acceleration than a stationary planar shock., Comment: 14 pages, 4 figures, supplementary material

New convergence bounds are presented for weighted, preconditioned, and deflated GMRES for the solution of large, sparse, non-Hermitian linear systems. These bounds are given for the case when the Hermitian part of the coefficient matrix is positive definite, the preconditioner is Hermitian positive definite, and the weight is equal to the preconditioner. The new bounds are a novel contribution in and of themselves. In addition, they are sufficiently explicit to indicate how to choose the preconditioner and the deflation space to accelerate the convergence. One such choice of deflating space is presented, and numerical experiments illustrate the effectiveness of such space.

The oldest stars in the Milky Way (born in the first few billion years) are expected to have a high density in the inner few kpc, spatially overlapping with the Galactic bulge. We use spectroscopic data from the Pristine Inner Galaxy Survey (PIGS) to study the dynamical properties of ancient, metal-poor inner Galaxy stars. We compute distances using StarHorse, and orbital properties in a barred Galactic potential. With this paper, we release the spectroscopic AAT/PIGS catalogue (13 235 stars). We find that most PIGS stars have orbits typical for a pressure-supported population. The fraction of stars confined to the inner Galaxy decreases with decreasing metallicity, but many very metal-poor stars (VMP, [Fe/H] < -2.0) stay confined (~ 60% stay within 5 kpc). The azimuthal velocity v$_\phi$ also decreases between [Fe/H] = -1.0 and -2.0, but is constant for VMP stars (at ~ 40 km/s). The carbon-enhanced metal-poor (CEMP) stars in PIGS appear to have similar orbital properties compared to normal VMP stars. Our results suggest a possible transition between two spheroidal components - a more metal-rich, more concentrated, faster rotating component, and a more metal-poor, more extended and slower/non-rotating component. We propose that the former may be connected to pre-disc in-situ stars (or those born in large building blocks), whereas the latter may be dominated by contributions from smaller galaxies. This is an exciting era where large metal-poor samples, such as in this work (as well as upcoming surveys, e.g., 4MOST), shed light on the earliest evolution of our Galaxy., Comment: Accepted to MNRAS, new: Figures 4, 8 and 9 and Table 2 - Figure 9 shows two inner halo components