Search

Background: Veterinary clinical narratives remain a largely untapped resource for addressing complex diseases. Here we compare the ability of a large language model (ChatGPT) and a previously developed regular expression (RegexT) to identify overweight body condition scores (BCS) in veterinary narratives. Methods: BCS values were extracted from 4,415 anonymised clinical narratives using either RegexT or by appending the narrative to a prompt sent to ChatGPT coercing the model to return the BCS information. Data were manually reviewed for comparison. Results: The precision of RegexT was higher (100%, 95% CI 94.81-100%) than the ChatGPT (89.3%; 95% CI82.75-93.64%). However, the recall of ChatGPT (100%. 95% CI 96.18-100%) was considerably higher than that of RegexT (72.6%, 95% CI 63.92-79.94%). Limitations: Subtle prompt engineering is needed to improve ChatGPT output. Conclusions: Large language models create diverse opportunities and, whilst complex, present an intuitive interface to information but require careful implementation to avoid unpredictable errors., Comment: Supplementary Material: The data that support the findings of this study are available in the ancillary files of this submission. 5 pages, 2 figures (textboxes)

We study the problem of decentralized interferometric imaging over networks, where agents have access to a subset of local radar measurements and can compute pair-wise correlations with their neighbors. We propose a primal-dual distributed algorithm named Distributed Generalized Wirtinger Flow (DGWF). We use the theory of low rank matrix recovery to show when the interferometric imaging problem satisfies the Regularity Condition, which implies the Polyak-Lojasiewicz inequality. Moreover, we show that DGWF converges geometrically for smooth functions. Numerical simulations for single-scattering radar interferometric imaging demonstrate that DGWF can achieve the same mean-squared error image reconstruction quality as its centralized counterpart for various network connectivity and size., Comment: 6 pages, 3 figures, accepted to IFAC 2022 Conference on Networked Systems (NecSys22)

With the arrival of a number of wide-field snapshot image-plane radio transient surveys, there will be a huge influx of images in the coming years making it impossible to manually analyse the datasets. Automated pipelines to process the information stored in the images are being developed, such as the LOFAR Transients Pipeline, outputting light curves and various transient parameters. These pipelines have a number of tuneable parameters that require training to meet the survey requirements. This paper utilises both observed and simulated datasets to demonstrate different machine learning strategies that can be used to train these parameters. The datasets used are from LOFAR observations and we process the data using the LOFAR Transients Pipeline; however the strategies developed are applicable to any light curve datasets at different frequencies and can be adapted to different automated pipelines. These machine learning strategies are publicly available as Python tools that can be downloaded and adapted to different datasets (https://github.com/AntoniaR/TraP_ML_tools)., Comment: Astronomy & Computing Accepted, 25 pages, 20 figures

We model the intermediate-mass black hole HLX-1, using the Hubble Space Telescope, XMM-Newton and Swift. We quantify the relative contributions of a bluer component, function of X-ray irradiation, and a redder component, constant and likely coming from an old stellar population. We estimate a black hole mass of about (2^{+2}_{-1}) x 10^4 M_{sun}, a spin parameter a/M ~ 0.9 for moderately face-on view, and a peak outburst luminosity of about 0.3 times the Eddington luminosity. We discuss the discrepancy between the characteristic sizes inferred from the short X-ray timescale (R ~ a few 10^{11} cm) and from the optical emitter (R sqrt[cos theta] ~ 2.2 x 10^{13} cm). One possibility is that the optical emitter is a circumbinary disk; however, we disfavour this scenario because it would require a very small donor star. A more plausible scenario is that the disk is large but only the inner annuli are involved in the X-ray outburst. We propose that the recurrent outbursts are caused by an accretion-rate oscillation driven by wind instability in the inner disk. We argue that the system has a long-term-average accretion rate of a few percent Eddington, just below the upper limit of the low/hard state; a wind-driven oscillation can trigger transitions to the high/soft state, with a recurrence period of ~1 year (much longer than the binary period, which we estimate as ~10 days). The oscillation that dominated the system in the last decade is now damped such that the accretion rate no longer reaches the level required to trigger a transition. Finally, we highlight similarities between disk winds in HLX-1 and in the Galactic black hole V404 Cyg., Comment: 22 pages, 8 figures, accepted for publication in MNRAS

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ~92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ~95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid., Comment: Accepted for publication in Astrophysical Journal