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Identifying cognitive impairment within electronic health records (EHRs) is crucial not only for timely diagnoses but also for facilitating research. Information about cognitive impairment often exists within unstructured clinician notes in EHRs, but manual chart reviews are both time-consuming and error-prone. To address this issue, our study evaluates an automated approach using zero-shot GPT-4o to determine stage of cognitive impairment in two different tasks. First, we evaluated the ability of GPT-4o to determine the global Clinical Dementia Rating (CDR) on specialist notes from 769 patients who visited the memory clinic at Massachusetts General Hospital (MGH), and achieved a weighted kappa score of 0.83. Second, we assessed GPT-4o's ability to differentiate between normal cognition, mild cognitive impairment (MCI), and dementia on all notes in a 3-year window from 860 Medicare patients. GPT-4o attained a weighted kappa score of 0.91 in comparison to specialist chart reviews and 0.96 on cases that the clinical adjudicators rated with high confidence. Our findings demonstrate GPT-4o's potential as a scalable chart review tool for creating research datasets and assisting diagnosis in clinical settings in the future., Comment: Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 7 pages

JWST observations have uncovered a new population of red, compact objects at high redshifts dubbed 'Little Red Dots' (LRDs), which typically show broad emission lines and are thought to be dusty Active Galactic Nuclei (AGN). Some of their other features, however, challenge the AGN explanation, such as prominent Balmer breaks and extremely faint or even missing metal high-ionization lines, X-ray, or radio emission, including in deep stacks. Time variability is a unique test of AGN activity. Here, we exploit the $z=7.045$ multiply-imaged LRD A2744-QSO1, which offers a unique test of variability due to lensing-induced time delays between the three images spanning 22 yr (2.7 yr in the rest-frame), to investigate its photometric and spectroscopic variability. We find the equivalent widths (EWs) of the broad H$\alpha$ and H$\beta$ lines, which are independent of magnification and other systematics, to exhibit significant variations, up to $18\pm3$ % for H$\alpha$ and up to $22\pm8$ % in H$\beta$, on a timescale of 875 d (2.4 yr) in the rest-frame. This suggests that A2744-QSO1 is indeed an AGN. We find no significant photometric variability beyond the limiting systematic uncertainties, so it currently cannot be determined whether the EW variations are due to line-flux or continuum variability. These results are consistent with a typical damped random walk (DRW) variability model for an AGN like A2744-QSO1 ($M_{\mathrm{BH}}=4\times10^7 \mathrm{M}_{\odot}$) given the sparse sampling of the light-curve with the available data. Our results therefore support the AGN interpretation of this LRD, and highlight the need for further photometric and spectroscopic monitoring in order to build a detailed and reliable light-curve., Comment: Submitted to A&A. v2 updated to match the submitted version

The national forecasting competition WxChallenge, brainchild of Brad Illston at the University of Oklahoma in 2005, has become a cherished institution played across the United States each year. Participants include students, faculty, alumni, and industry professionals. However, forecasts are given as scalar values without expression of uncertainty, probabilities being a keystone of meteorological forecasting today, and previous attempts to add probabilistic elements to WxChallenge have failed partly due to challenges in making probability forecasting accessible to all, and inability to combine scores with different units while also appropriately rewarding forecasts using proper scoring rules. Much of the competition's maintenance relies on dedicated volunteers, highlighting need for more automation. Hence I propose three new features: (1) automated forecast problems based on morning ensemble guidance, forming prediction baselines, thresholds over which the players demonstrate skill in their later forecast; (2) a spread betting game, where the players allocate 100 confidence credits to the over-under for exceeding a percentile (e.g., 50pc) threshold of a variable (e.g., maximum temperature) derived from the ensemble baseline; and (3) a game where players distribute 100 confidence credits across bins of a continuous variable (e.g., accumulated precipitation) approximating a probability mass function. Forecasts are evaluated using Shannon information gained over the baseline forecast, yielding additive units of bits that allow score combinations of different variables and units. Information gain parallels the Brier Score and is likewise a sound measure of skill due its punishment of hedging. This proposal objective is to augment WxChallenge with two new probabilistic games that are accessible, scientifically sound, enjoyable, and optional., Comment: Manuscript is 10 pages and 2 figures. Originally submitted and rejected from Bulletin of American Meteorological Society. Future resubmission is expected

We explore the evolving relationship between galaxies and their dark matter halos from $z \sim 0.1$ to $z \sim 12$ using mass-limited angular clustering measurements in the 0.54 deg$^2$ of the COSMOS-Web survey. This study provides the first measurements of the mass-limited two-point correlation function at $z \ge 10$ and a consistent analysis spanning 13.4 Gyr of cosmic history, setting new benchmarks for future simulations and models. Using a halo occupation distribution (HOD) framework, we derive characteristic halo masses and the stellar-to-halo mass relationship (SHMR) across redshifts and stellar mass bins. Our results first indicate that HOD models fit data at $z \ge 2.5$ best when incorporating a non-linear scale-dependent halo bias, boosting clustering at non-linear scales (r = 10-100 kpc). We find that galaxies at z > 10.5 with $\log(M_\star / M_\odot) \ge 8.85$ are hosted by halos with $M_{\rm h} \sim 10^{10.5}\,M_\odot$, achieving a star formation efficiency (SFE) $M_\star / (f_b M_{\rm h}) $ up to 1 dex higher than at $z \le 1$. The high galaxy bias at $z \ge 8$ suggests that these galaxies reside in massive halos with intrinsic high SFE. Our SHMR evolves significantly with redshift, starting high at $z \ge 10.5$, decreasing until $z \sim 2 - 3$, then increasing again until the present. Current simulations fail to reproduce both massive high-$z$ galaxies and this evolution, while semi-empirical models linking SFE to halo mass, accretion rates, and redshift align with our findings. We propose that $z > 8$ galaxies experience bursty star formation without significant feedback altering their growth, driving the rapid growth of massive galaxies observed by JWST. Over time, increasing feedback efficiency and exponential halo growth suppress star formation. At $z \sim 2 - 3$ and after, halo growth slows down while star formation continues, supported by gas reservoirs in halos., Comment: Submitted to Astronomy & Astrophysics

Metallicity is a crucial tracer of galaxy evolution, providing insights into gas accretion, star formation, and feedback. At high redshift, these processes reveal how early galaxies assembled and enriched their interstellar medium. In this work, we present rest-frame optical spectroscopy of 12 massive ($\log(M_*/\mathrm{M_{\odot}})>9$) galaxies at $z\sim 6$-$8$ from the REBELS ALMA large program, observed with JWST NIRSpec/IFU in the prism mode. These observations span emission lines from [OII]$\lambda$3727,9 to [SII]$\lambda$6716,31, providing key information on nebular dust attenuation, ionisation states, and chemical abundances. We find lower O32 ratios (average $\sim3.7$) and [OIII]$\lambda$5007 equivalent widths (average ${EW_{[OIII]}}\sim390$\AA) than are generally found in existing large spectroscopic surveys at $z>6$, indicating less extreme ionising conditions. Strong-line diagnostics suggest that these systems are some of the most metal-rich galaxies observed at $z>6$ (average $Z_{\mathrm{gas}}\sim 0.4 Z_{\odot}$), including sources with near-solar oxygen abundances, in line with their high stellar masses (average $\log{M_*/\mathrm{M_{\odot}}}\sim9.5$). Supplementing with literature sources at lower masses, we investigate the mass-metallicity and fundamental metallicity relations (MZR and FMR, respectively) over a 4 dex stellar mass range at $63$ studies., Comment: Submitted to MNRAS, 22 pages

Globular clusters (GCs) are some of the oldest bound structures in the Universe, holding clues to the earliest epochs of star formation and galaxy assembly. However, accurate age measurements of ancient clusters are challenging due to the age-metallicity degeneracy. Here, we report the discovery of 42 compact stellar systems within the 'Relic', a massive, quiescent galaxy at $z=2.53$. The Relic resides in an over-density behind the Abell 2744 cluster, with a prominent tidal tail extending towards two low-mass companions. Using deep data from the UNCOVER/MegaScience JWST Surveys, we find that clusters formed in age intervals ranging from 8 Myr up to $\sim2$ Gyr, suggesting a rich formation history starting at $z\sim10$. While the cluster-based star formation history is broadly consistent with the high past star formation rates derived from the diffuse host galaxy light, one potential discrepancy is a tentative $\sim2-3\times$ higher rate in the cluster population for the past Gyr. Taken together with the spatial distribution and low inferred metallicities of these young-to-intermediate age clusters, we may be seeing direct evidence for the accretion of star clusters in addition to their early in-situ formation. The cluster masses are high, $\sim10^6-10^7~M_{\odot}$, which may explain why we are able to detect them around this likely post-merger galaxy. Overall, the Relic clusters are consistent with being precursors of the most massive present-day GCs. This unique laboratory enables the first connection between long-lived, high-redshift clusters and local stellar populations, offering insights into the early stages of GC evolution and the broader processes of galaxy assembly., Comment: 19 pages, 7 figures, submitted to Astrophysical Journal on January 9, 2025 (comments welcome!)

The Great Salt Lake (Utah, USA) is reducing in size, which raises several ecological concerns, including the effect of an increasing area of dry playa exposed by the retreating lake. This study focuses solely on concerns about the toxicity of metals in the dust blowing off the playa. Although considerable efforts have been made to understand aeolian dust in urban areas along the Wasatch Front, located just east and south of the Great Salt Lake, there is still a need to consolidate existing research and to conduct a compositional analysis of the dust found in these urban areas. We investigated the dust reaching urban monitoring sites around the Great Salt Lake that are managed by the Utah Division of Air Quality. By analyzing historical data, we found that the decrease in the Great Salt Lake's surface area has not led to a statistically significant increase in dust events in urban areas. Windrose plots align with prior research, indicating that heightened dust levels in urban areas coincide with winds originating from the south or west, passing over identified playas and deserts such as the Milford Flats, Sevier Dry Lake, Tule Dry Lake, Great Salt Lake Desert, Dugway Proving Grounds, and the West Desert of Utah. Metallic compositional analysis of urban dust was used to evaluate potential health risks associated with the dust using the hazard quotient, air regional screening levels, and cancer risk methods. This analysis revealed no significant increase in concentrations of toxic metals. However, this is not to preclude a risk of dust-related health concerns, especially due to pre-existing arsenic and lead levels.

In 1960, J.F. Adams introduced secondary cohomology operations that are defined on cohomology elements on which sufficiently many Steenrod algebra elements vanish. This led to his theorem on singly generated cohomology rings, which in turn led to his celebrated resolution of the Hopf invariant one problem. Here we advertise a conjecture that would extend Adams' result and prove it in a special case., Comment: 14 pages

Optically dark dusty star-forming galaxies (DSFGs) play an essential role in massive galaxy formation at early cosmic time, however their nature remains elusive. Here we present a detailed case study of all the baryonic components of a $z=4.821$ DSFG, XS55. Selected from the ultra-deep COSMOS-XS 3GHz map with a red SCUBA-2 450$\mu$m/850$\mu$m colour, XS55 was followed up with ALMA 3mm line scans and spectroscopically confirmed to be at $z=4.821$ via detections of the CO(5-4) and [CI](1-0) lines. JWST/NIRCam imaging reveals that XS55 is a F150W-dropout with red F277W/F444W colour, and a complex morphology: a compact central component embedded in an extended structure with a likely companion. XS55 is tentatively detected in X-rays with both Chandra and XMM-Newton, suggesting an active galactic nucleus (AGN) nature. By fitting a panchromatic SED spanning NIR to radio wavelengths, we revealed that XS55 is a massive main-sequence galaxy with a stellar mass of $M_\ast=(5\pm1)\times10^{10}\,{\rm M_\odot}$ and a star formation rate of ${\rm SFR}=540\pm177~{\rm M_\odot\,yr^{-1}}$. The dust of XS55 is optically thick in the far infrared (FIR) with a surprisingly cold dust temperature of $T_{\rm dust}=33\pm2\,{\rm K}$, making XS55 one of the coldest DSFGs at $z>4$ known to date. This work unveils the nature of a radio-selected F150W-dropout, suggesting the existence of a population of DSFGs hosting active black holes embedded in optically thick dust., Comment: 11 pages, 7 figures, accepted in Astronomy & Astrophysics

We study the rest-frame optical and ultraviolet morphology of galaxies in the first billion years of the Universe. Using JWST data from the UNCOVER and MegaScience surveys targeting the lensing cluster Abell 2744 we present multi-band morphological measurements for a sample of 995 galaxies selected using 20-band NIRCam photometry and 35 using NIRSpec Prism spectroscopy over the redshift range of $4

Lagrangian tracer particle trajectories for invariant solutions of the Navier-Stokes equations confined to the three-dimensional geometry of plane Couette flow are studied. Treating the Eulerian velocity field of an invariant solution as a dynamical system, the transport of these passive scalars along Lagrangian flow trajectories reveals a rich repertoire of different types of motion that can occur, including stagnation points, for which there is no fluid movement, and invariant tori, which obstruct chaotic mixing across the full volume of the plane Couette flow minimal cell. We determine the stability of these stagnation points, along with their stable and unstable manifolds, and find heteroclinic connections between them. These topological features produce a skeleton that shapes the passive tracer flow for a turbulent fluid, providing a first step to elucidating Lagrangian particle transport and mixing in three-dimensional Navier-Stokes turbulent flows., Comment: 18 pages, 13 figures

We present a detailed exploration of the most optically-luminous Little Red Dot ($L_{H\alpha}=10^{44}$erg/s, $L_V=10^{45}$erg/s, F444W=22AB) found to date. Located in the Abell 2744 field, source A744-45924 was observed by NIRSpec/PRISM with ultradeep spectroscopy reaching SNR$\sim$100pix$^{-1}$, high-resolution 3-4 micron NIRCam/Grism spectroscopy, and NIRCam Medium Band imaging. The NIRCam spectra reveal high rest-frame EW $W_{H\alpha,0,broad}>800$\r{A}, broad H$\alpha$ emission (FWHM$\sim$4500 km/s), on top of narrow, complex absorption. NIRSpec data show exceptionally strong rest-frame UV to NIR Fe II emission ($W_{FeII-UV,0}\sim$340\r{A}), N IV]$\lambda\lambda$1483,1486 and N III]$\lambda$1750, and broad NIR O I $\lambda$8446 emission. The spectra unambiguously demonstrate a broad-line region associated with an inferred $M_{BH}\sim10^9M_\odot$ supermassive black hole embedded in dense gas, which might explain a non-detection in ultradeep Chandra X-ray data (>$10\times$ underluminous relative to broad $L_{H\alpha}$). Strong UV Nitrogen lines suggest supersolar N/O ratios due to rapid star formation or intense radiation near the AGN. The continuum shows a clear Balmer break at rest-frame 3650\r{A}, which cannot be accounted for by an AGN power-law alone. A stellar population model produces an excellent fit with a reddened Balmer break and implying a massive ($M_*\sim8\times10^{10}M_\odot$), old $\sim$500 Myr, compact stellar core, among the densest stellar systems known ($\rho\sim3\times10^6M_\odot$/pc$^2$ for $R_{e,opt}=70\pm10$ pc), and AGN emission with extreme intrinsic EW $W_{H\alpha,0}\gg$1000\r{A}. However, although high $M_*$ and $M_{BH}$ are supported by evidence of an overdensity containing 40 galaxies at $z=4.41-4.51$, deep high-resolution spectroscopy is required to confirm stellar absorption and rule out that dense gas around the AGN causes the Balmer break instead., Comment: 28 pages,10 figures, submitted to ApJ

The linear stability of miscible displacement for radial source flow at infinite P\'eclet number in a Hele-Shaw cell is calculated theoretically. The axisymmetric self-similar flow is shown to be unstable to viscous fingering if the viscosity ratio $m$ between ambient and injected fluids exceeds $3\over2$ and to be stable if $m<{3\over2}$. If $1

Correctness proofs for floating point programs are difficult to verify. To simplify the task, a similar, but less complex system, known as logarithmic arithmetic can be used. The Boyer-Moore Theorem Prover, NQTHM, mechanically verified the correctness of a simple implementation of logarithmic arithmetic. It also verified some useful theorems about accumulated relative error bounds for addition, multiplication and division in this logarithmic number system. These theorems were used to verify a program that approximates e^x using a truncated Taylor series. Axioms that characterize the finite precision of the logarithmic system using a rational base, b, were shown by the prover to be satisfiable for any choice of 1 < b < 2. The prover verified the correctness of a function for converting an arbitrary rational value to a logarithmic representation. It also verified that multiplication and division implementations produce exact results for exact inputs, and that addition implementation produces a result as accurate as possible for exact inputs. When these operations are used in combination by a program, such as evaluating a polynomial, the relative error increases in a way that can be bounded by simple expressions, referred to here as tolerances. Several mechanically verified theorems about tolerances allow us to construct mechanically verified proofs about logarithmic arithmetic programs. Although similar to interval arithmetic, tolerances are especially suited to logarithmic arithmetic., Comment: 21 pages, Originally a 1994 technical report from University of Wyoming and Computational Logic, Inc

Differential equations are derived which show how generalized Euler vector representations of the Euler rotation axis and angle for a rigid body evolve in time; the Euler vector is also known as a rotation vector or axis-angle vector. The solutions can exhibit interesting rotational features in this non-abstract, visualizable setting, including spinor-like behavior and quasiperiodicity. The equations are well-behaved at zero, reducing to the simple infinitesimal case there. One of them is equivalent to a known quaternion differential equation. The simple geometric derivation does not depend on Euler's rotation theorem, and yields a proof of Euler's theorem using only infinitesimal motions. With mild regularity conditions on the angular velocity function, there is a continuous evolution of the normalized axis and angle for all time. Dynamical systems properties are discussed, and numerical solutions are used to investigate them when the angular velocity is itself rotating, and the Euler vector trajectory traces out a torus-like shape, with a strobe plot that densely fills in a closed curve.

The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new catalysts is currently limited by the gap between what AI-accelerated computational models predict and what experimental studies produce. To make progress, large and diverse experimental datasets are needed that are reproducible and tested at industrially-relevant conditions. We address these needs by utilizing a comprehensive high-throughput characterization and experimental pipeline to create the Open Catalyst Experiments 2024 (OCX24) dataset. The dataset contains 572 samples synthesized using both wet and dry methods with X-ray fluorescence and X-ray diffraction characterization. We prepared 441 gas diffusion electrodes, including replicates, and evaluated them using zero-gap electrolysis for carbon dioxide reduction (CO$_2$RR) and hydrogen evolution reactions (HER) at current densities up to $300$ mA/cm$^2$. To find correlations with experimental outcomes and to perform computational screens, DFT-verified adsorption energies for six adsorbates were calculated on $\sim$20,000 inorganic materials requiring 685 million AI-accelerated relaxations. Remarkably from this large set of materials, a data driven Sabatier volcano independently identified Pt as being a top candidate for HER without having any experimental measurements on Pt or Pt-alloy samples. We anticipate the availability of experimental data generated specifically for AI training, such as OCX24, will significantly improve the utility of computational models in selecting materials for experimental screening., Comment: 38 pages, 22 figures

The practical applications of Wasserstein distances (WDs) are constrained by their sample and computational complexities. Sliced-Wasserstein distances (SWDs) provide a workaround by projecting distributions onto one-dimensional subspaces, leveraging the more efficient, closed-form WDs for one-dimensional distributions. However, in high dimensions, most random projections become uninformative due to the concentration of measure phenomenon. Although several SWD variants have been proposed to focus on \textit{informative} slices, they often introduce additional complexity, numerical instability, and compromise desirable theoretical (metric) properties of SWD. Amidst the growing literature that focuses on directly modifying the slicing distribution, which often face challenges, we revisit the classical Sliced-Wasserstein and propose instead to rescale the 1D Wasserstein to make all slices equally informative. Importantly, we show that with an appropriate data assumption and notion of \textit{slice informativeness}, rescaling for all individual slices simplifies to \textbf{a single global scaling factor} on the SWD. This, in turn, translates to the standard learning rate search for gradient-based learning in common machine learning workflows. We perform extensive experiments across various machine learning tasks showing that the classical SWD, when properly configured, can often match or surpass the performance of more complex variants. We then answer the following question: "Is Sliced-Wasserstein all you need for common learning tasks?"

In this paper, the authors propose TriBERTa, a supervised entity resolution system that utilizes a pre-trained large language model and a triplet loss function to learn representations for entity matching. The system consists of two steps: first, name entity records are fed into a Sentence Bidirectional Encoder Representations from Transformers (SBERT) model to generate vector representations, which are then fine-tuned using contrastive learning based on a triplet loss function. Fine-tuned representations are used as input for entity matching tasks, and the results show that the proposed approach outperforms state-of-the-art representations, including SBERT without fine-tuning and conventional Term Frequency-Inverse Document Frequency (TF-IDF), by a margin of 3 - 19%. Additionally, the representations generated by TriBERTa demonstrated increased robustness, maintaining consistently higher performance across a range of datasets. The authors also discussed the importance of entity resolution in today's data-driven landscape and the challenges that arise when identifying and reconciling duplicate data across different sources. They also described the ER process, which involves several crucial steps, including blocking, entity matching, and clustering., Comment: 22 pages and 12 figures

The role of internal and environmental factors in the star formation activity of galaxies is still a matter of debate, particularly at higher redshifts. Leveraging the most recent release of the COSMOS catalog, COSMOS2020, and density measurements from our previous study we disentangle the impact of environment and stellar mass on the star formation rate (SFR), and specific SFR (sSFR) of a sample of $\sim 210,000$ galaxies within redshift range $0.4< z < 4$ and present our findings in three cosmic epochs: 1) out to $z\sim 1$, the average SFR and sSFR decline at extremely dense environments and high mass end of the distribution which is mostly due to the presence of the massive quiescent population; 2) at $1

We present flux measurements of Uranus observed at phase angles of 43.9{\deg}, 44.0{\deg}, and 52.4{\deg} by the Multispectral Visible Imaging Camera (MVIC) on the New Horizons spacecraft during 2023, 2010, and 2019, respectively. New Horizons imaged Uranus at a distance of about 24-70 AU (2023) in four color filters, with bandpasses of 400-550 nm, 540-700 nm, 780-975 nm, and 860-910 nm. High-phase-angle observations are of interest for studying the energy balance of Uranus, constraining the atmospheric scattering behavior, and understanding the planet as an analog for ice giant exoplanets. The new observations from New Horizons provide access to a wider wavelength range and different season compared to previous observations from both Voyager spacecraft. We performed aperture photometry on the New Horizons observations of Uranus to obtain its brightness in each photometric band. The photometry suggests that Uranus may be darker than predicted by a Lambertian phase curve in the Blue and Red filters. Comparison to simultaneous low-phase Hubble WFC3 and ground-based community-led observations indicates a lack of large-scale features at full-phase that would introduce variation in the rotational light curve. The New Horizons reflectance in the Blue (492 nm) and Red (624 nm) filters does not exhibit statistically significant variation and is consistent with the expected error bars. These results place new constraints on the atmospheric model of Uranus and its reflectivity. The observations are analogous to those from future exoplanet direct-imaging missions, which will capture unresolved images of exoplanets at partial phases. These results will serve as a "ground-truth" with which to interpret exo-ice giant data., Comment: Accepted for publication in the Planetary Science Journal, 11 pages, 6 figures

Communicating forecast uncertainty effectively is a persistent challenge in predictive endeavours such as weather forecasting. This paper explores the application of possibility theory as a complementary approach to traditional probability in risk communication. Unlike probability, possibility theory allows for the representation of uncertain events as ranges of potential, distinguished by degrees of plausibility possibility and certainty necessity. Using a simplified fuzzy-logic inference system, we generate possibility forecasts of ozone-concentration forecasts from meteorological inputs. Observations are pre-processed as degrees of an adjective, e.g., 1 m/s wind speed may belong to the adjective "calm" at degree 0.8 (perhaps represented with an adverb as "substantially"). Aggregation of all rule activations yields a possibility distribution over the output range. As possibility is an upper bound of probability, possibilities provides most value for risk-averse stakeholders sensitive to the event of interest, especially near the event's predictability horizon when there is most uncertainty to remove. By setting the forecast challenge as the possibility of an event, we effectively extend the predictability horizon: trading some specificity for detection of fainter signals of the event's future occurrence. Possibility theory appreciates uncertainty's dual nature: inherent randomness (aleatoric) and knowledge deficiency (epistemic). While the unfamiliar nature of the theory requires more accessible language before operational use and public communication, possibility offers substantial practical benefits for assessment of uncertainty at the edge of predictability limits for vulnerable users., Comment: 9 Figures, 15 Pages, unsubmitted as of 28 Oct 2024

We introduce ChartA11y, an app developed to enable accessible 2-D visualizations on smartphones for blind users through a participatory and iterative design process involving 13 sessions with two blind partners. We also present a design journey for making accessible touch experiences that go beyond simple auditory feedback, incorporating multimodal interactions and multisensory data representations. Together, ChartA11y aimed at providing direct chart accessing and comprehensive chart understanding by applying a two-mode setting: a semantic navigation framework mode and a direct touch mapping mode. By re-designing traditional touch-to-audio interactions, ChartA11y also extends to accessible scatter plots, addressing the under-explored challenges posed by their non-linear data distribution. Our main contributions encompass the detailed participatory design process and the resulting system, ChartA11y, offering a novel approach for blind users to access visualizations on their smartphones.

The evolution of galaxy sizes in different wavelengths provides unique insights on galaxy build-up across cosmic epochs. Such measurements can now finally be done at $z>3$ thanks to the exquisite spatial resolution and multi-wavelength capability of the JWST. With the public data from the CEERS, PRIMER-UDS, and PRIMER-COSMOS surveys, we measure the sizes of $\sim 3500$ star-forming galaxies at $3 \leqslant z<9$, in 7 NIRCam bands using the multi-wavelength model fitting code GalfitM. The size-mass relation is measured in four redshift bins, across all NIRCam bands. We find that, the slope and intrinsic scatter of the rest-optical size-mass relation are constant across this redshift range and consistent with previous HST-based studies at low-z. When comparing the relations across different wavelengths, the average rest-optical and rest-UV relations are consistent with each other up to $z=6$, but the intrinsic scatter is largest in rest-UV wavelengths compared to rest-optical and redder bands. This behaviour is independent of redshift and we speculate that it is driven by bursty star-formation in $z>4$ galaxies. Additionally, for $3\leqslant z<4$ star-forming galaxies at $\rm M_* > 10^{10} M_{\odot}$, we find smaller rest-$\rm 1\rm\,\mu m$ sizes in comparison to rest-optical (and rest-UV) sizes, suggestive of colour gradients. When comparing to simulations, we find agreement over $\rm M_* \approx 10^{9} - 10^{10} M_{\odot}$ but beyond this mass, the observed size-mass relation is significantly steeper. Our results show the power of JWST/NIRCam to provide new constraints on galaxy formation models., Comment: 14 pages, 9 figures, submitted to A&A

JWST has revealed an abundance of compact, red objects at $z\approx5-8$ dubbed "little red dots" (LRDs), whose SEDs display a faint blue UV continuum followed by a steep rise in the optical. Despite extensive study of their characteristic V-shaped SEDs, the nature of LRDs remains unknown. We present a new analysis of the NIRSpec/PRISM spectrum of A2744-QSO1, a triply imaged LRD at $z=7.04$ from the UNCOVER survey. The spectrum shows a strong Balmer break and broad Balmer emission lines, both of which are difficult to explain with models invoking exclusively AGN or stellar contributions. Our fiducial model decomposes the spectrum into a post-starburst galaxy dominating the UV-optical continuum and a reddened AGN being sub-dominant at all wavelength and contributing at $\sim20\%$ level. However, our most credible model infers a stellar mass of $M_\star\approx 4\times10^9\,\mathrm{M_\odot}$ within a radius of $r_\mathrm{e}<30\,$pc, driving its central density to the highest among observations to date. This high central density could be explained if A2744-QSO-1 is the early-forming core of a modern-day massive elliptical galaxy that later puffed up via the inside-out growth channel. The models also necessitate an unusually steep dust law to preserve the strong break strength, though this steepness may be explained by a deficit of large dust grains. It is also probable that these challenges reflect our ignorance of A2744-QSO1's true nature. Future variability and reverberation mapping studies could help disentangle the galaxy and AGN contribution to the continuum, and deeper redder observations could also unveil the dust properties in LRDs., Comment: 19 pages, 7 figures, 2 tables. Submitted to ApJ. Comments are welcome

For nearly a century and a half the Stokes phenomenon had been perceived as a discontinuous change in the asymptotic representation of a function. In 1989 Berry demonstrated how it is possible to smooth out this discontinuity in broad classes of problems with the prefactor for the exponentially small contribution that is being switched on/off taking the universal form of an error function. Following pioneering work of Berk {\it et al.} \cite{BNR82} and the Japanese school of formally exact asymptotics \cite{Aokietal1994,AKT01}, the concept of the higher-order Stokes phenomenon was introduced in \cite{HLO04} and \cite{CM05}, whereby the ability for the exponentially small terms to cause a Stokes phenomenon may change, depending on the values of parameters in the problem, corresponding to the associated singularities in the Borel plane transitioning between different Riemann sheets. Until now, the higher-order Stokes phenomenon has also been treated as a discontinuous event. In this paper we show how the higher-order Stokes phenomenon is, in fact, also smooth and occurs universally with a prefactor that takes the form of a new special function, based on a Gaussian convolution of an error function that gives rise to a rich structure. We provide a rigorous derivation of the result, with examples spanning the gamma function, a second-order nonlinear ODE and the telegraph equation, giving rise to a ghost-like smooth contribution that is present in the vicinity of a Stokes line, but which rapidly tends to zero on either side. We also include a rigorous derivation and example of the effect of the smoothed higher-order Stokes phenomenon on the individual terms in the asymptotic series, where the additional contributions appear prefactored by an error function., Comment: 49 pages, 15 figures

Access to the potential energy Hessian enables determination of the Gibbs free energy, and certain approaches to transition state search and optimization. Here, we demonstrate that off-the-shelf pretrained Open Catalyst Project (OCP) machine learned potentials (MLPs) determine the Hessian with great success (58 cm$^{-1}$ mean absolute error (MAE)) for intermediates adsorbed to heterogeneous catalyst surfaces. This enables the use of OCP models for the aforementioned applications. The top performing model, with a simple offset correction, gives good estimations of the vibrational entropy contribution to the Gibbs free energy with an MAE of 0.042 eV at 300 K. The ability to leverage models to capture the translational entropy was also explored. It was determined that 94% of randomly sampled systems had a translational entropy greater than 0.1 eV at 300 K. This underscores the need to go beyond the harmonic approximation to consider the entropy introduced by adsorbate translation, which increases with temperature. Lastly, we used MLP determined Hessian information for transition state search and found we were able to reduce the number of unconverged systems by 65% to 93% overall convergence, improving on the baseline established by CatTSunami., Comment: 43 pages, 8 figures

Recent advances in deep learning have inspired numerous works on data-driven solutions to partial differential equation (PDE) problems. These neural PDE solvers can often be much faster than their numerical counterparts; however, each presents its unique limitations and generally balances training cost, numerical accuracy, and ease of applicability to different problem setups. To address these limitations, we introduce several methods to apply latent diffusion models to physics simulation. Firstly, we introduce a mesh autoencoder to compress arbitrarily discretized PDE data, allowing for efficient diffusion training across various physics. Furthermore, we investigate full spatio-temporal solution generation to mitigate autoregressive error accumulation. Lastly, we investigate conditioning on initial physical quantities, as well as conditioning solely on a text prompt to introduce text2PDE generation. We show that language can be a compact, interpretable, and accurate modality for generating physics simulations, paving the way for more usable and accessible PDE solvers. Through experiments on both uniform and structured grids, we show that the proposed approach is competitive with current neural PDE solvers in both accuracy and efficiency, with promising scaling behavior up to $\sim$3 billion parameters. By introducing a scalable, accurate, and usable physics simulator, we hope to bring neural PDE solvers closer to practical use., Comment: Published at ICLR 2025. Github: http://github.com/anthonyzhou-1/ldm_pdes

Low latency models are critical for real-time speech enhancement applications, such as hearing aids and hearables. However, the sub-millisecond latency space for resource-constrained hearables remains underexplored. We demonstrate speech enhancement using a computationally efficient minimum-phase FIR filter, enabling sample-by-sample processing to achieve mean algorithmic latency of 0.32 ms to 1.25 ms. With a single microphone, we observe a mean SI-SDRi of 4.1 dB. The approach shows generalization with a DNSMOS increase of 0.2 on unseen audio recordings. We use a lightweight LSTM-based model of 644k parameters to generate FIR taps. We benchmark that our system can run on low-power DSP with 388 MIPS and mean end-to-end latency of 3.35 ms. We provide a comparison with baseline low-latency spectral masking techniques. We hope this work will enable a better understanding of latency and can be used to improve the comfort and usability of hearables.

Analyzing and effectively communicating the efficacy and toxicity of treatment is the basis of risk benefit analysis (RBA). More efficient and objective tools are needed. We apply Chauhan Weighted Trajectory Analysis (CWTA) to perform RBA with superior objectivity, power, and clarity. We used CWTA to perform 1000-fold simulations of RCTs using ordinal endpoints for both treatment efficacy and toxicity. RCTs were simulated with 1:1 allocation at defined sample sizes and hazard ratios. We studied the simplest case of 3 levels each of toxicity and efficacy and the general case of the advanced cancer trial, with efficacy graded by five RECIST 1.1 health statuses and toxicity by the six-point CTCAE scale (6 x 5 matrix). The latter model was applied to a real-world dose escalation phase I trial in advanced cancer. Simulations in both the 3 x 3 and the 6 x 5 advanced cancer matrix confirmed that drugs with both superior efficacy and toxicity profiles synergize for greater statistical power with CWTA-RBA. The CWTA-RBA 6 x 5 matrix reduced sample size requirements over CWTA efficacy-only analysis. Application to the dose finding phase I clinical trial provided objective, statistically significant validation for the selected dose. CWTA-RBA, by incorporating both drug efficacy and toxicity, provides a single test statistic and plot that analyzes and effectively communicates therapeutic risks and benefits. CWTA-RBA requires fewer patients than CWTA efficacy-only analysis when the experimental drug is both more effective and less toxic. CWTA-RBA facilitates the objective and efficient assessment of new therapies throughout the drug development pathway. Furthermore, several advantages over competing tests in communicating risk-benefit will assist regulatory review, clinical adoption, and understanding of therapeutic risks and benefits by clinicians and patients alike.

JWST has revealed diverse new populations of high-redshift ($z\sim4-11$) AGN and extreme star-forming galaxies that challenge current models. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. In this paper, we use rest-frame UV emission-line diagnostics to identify AGN candidates and other exceptional ionizing sources, complementing previous studies predominantly focused on broad-line AGN. From a parent sample of 205 $\mathrm{z_{spec}}>3$ UNCOVER galaxies with NIRSpec/PRISM follow-up, we identify 12 C IV, He II, and C III] emitters. Leveraging the combined rest-optical and UV coverage of PRISM, we limit the emission-line model space using the sample's [O III]/H$\beta$ distribution, significantly decreasing the overlap between AGN and star-formation models in the UV diagnostics. We then find that the five He II emitters are the strongest AGN candidates, with further support from two [Ne V] detections and one X-ray detection from Chandra. We cannot robustly quantify the AGN fraction in this sample, but we note that close to 20% of $\mathrm{M_{*}>2\times10^{9}\,M_{\odot}}$ parent sample galaxies are AGN candidates. The lower-mass line emitters, which are consistent with both AGN and star-forming photoionization models, have more compact sizes and higher specific star formation rates than the parent sample. Higher-resolution and deeper data on these UV line emitters should provide much stronger constraints on the obscured AGN fraction at $z > 3$., Comment: Submitted to ApJ

Graph anomaly detection has gained significant attention across various domains, particularly in critical applications like fraud detection in e-commerce platforms and insider threat detection in cybersecurity. Usually, these data are composed of multiple types (e.g., user information and transaction records for financial data), thus exhibiting view heterogeneity. However, in the era of big data, the heterogeneity of views and the lack of label information pose substantial challenges to traditional approaches. Existing unsupervised graph anomaly detection methods often struggle with high-dimensionality issues, rely on strong assumptions about graph structures or fail to handle complex multi-view graphs. To address these challenges, we propose a cluster aware multi-view graph anomaly detection method, called CARE. Our approach captures both local and global node affinities by augmenting the graph's adjacency matrix with the pseudo-label (i.e., soft membership assignments) without any strong assumption about the graph. To mitigate potential biases from the pseudo-label, we introduce a similarity-guided loss. Theoretically, we show that the proposed similarity-guided loss is a variant of contrastive learning loss, and we present how this loss alleviates the bias introduced by pseudo-label with the connection to graph spectral clustering. Experimental results on several datasets demonstrate the effectiveness and efficiency of our proposed framework. Specifically, CARE outperforms the second-best competitors by more than 39% on the Amazon dataset with respect to AUPRC and 18.7% on the YelpChi dataset with respect to AUROC. The code of our method is available at the GitHub link: https://github.com/zhenglecheng/CARE-demo., Comment: Accepted by The Web Conference 2025

Hubble Space Telescope imaging shows that most star-forming galaxies at cosmic noon -- the peak of cosmic star formation history -- appear disk-dominated, leaving the origin of the dense cores in their quiescent descendants unclear. With the James Webb Space Telescope's (JWST) high-resolution imaging to 5 {\mu}m, we can now map the rest-frame near-infrared emission, a much closer proxy for stellar mass distribution, in these massive galaxies. We selected 70 star-forming galaxies with 10$<$log(M)$<$12 and 1.5$<$z$<$3 in the CEERS survey and compare their morphologies in the rest-frame optical to those in the rest-frame near-IR. While the bulk of these galaxies are disk-dominated in 1.5 {\mu}m (rest-frame optical) imaging, they appear more bulge-dominated at 4.4 {\mu}m (rest-frame near-infrared). Our analysis reveals that in massive star-forming galaxies at z$\sim$2, the radial surface brightness profiles steepen significantly, from a slope of $\sim$0.3/dex at 1.5 {\mu}m to $\sim$1.4/dex at 4.4 {\mu}m within radii $<$ 1 kpc. Additionally, we find their total flux contained within the central 1 kpc is approximately 7 times higher in F444W than in F150W. In rest-optical emission, a galaxy's central surface density appears to be the strongest indicator of whether it is quenched or star-forming. Our most significant finding is that at redder wavelengths, the central surface density ratio between quiescent and star-forming galaxies dramatically decreases from $\sim$10 to $\sim$1. This suggests the high central densities associated with galaxy quenching are already in place during the star-forming phase, imposing new constraints on the transition from star formation to quiescence., Comment: 9 pages, 5 figures, published in ApJL