Comparison of industry payments to psychiatrists and psychiatric advanced practice clinicians in the USA, 2021: A cross-sectional study

Objectives: To compare industry payment patterns among US psychiatrists and psychiatric advanced practice clinicians (APCs) and determine how scope of practice laws has influenced these patterns. Design: Cross-sectional study. Setting: This study used the publicly available US Centers for Medicare and Medicaid Services Sunshine Act Open Payment database and the National Plan and Provider Enumeration System (NPPES) database for the year 2021. Participants: All psychiatrists and psychiatric APCs (subdivided into nurse practitioners (NPs) and clinical nurse specialists (CNSs)) included in either database. Primary and secondary outcome measures: Number and percentage of clinicians receiving industry payments and value of payments received. Total payments and number of transactions by type of payment, payment source and clinician type were also evaluated. Results: A total of 85 053 psychiatric clinicians (61 011 psychiatrists (71.7%), 21 895 NPs (25.7%), 2147 CNSs (2.5%)) were reviewed; 16 240 (26.6%) psychiatrists received non-research payment from industry, compared with 10 802 (49.3%) NPs and 231 (10.7%) CNSs (p United States Dollars (US) $) 100 (33.9$ 1000 (5.3% vs 4.1%; IRR, 1.29 (1.20 to 1.38); p US$ 10 000 (0.4% vs 1.0%; IRR, 0.39 (0.31 to 0.49); p Conclusions: Psychiatric NPs were nearly two times as likely to receive industry payments as psychiatrists, while psychiatric CNSs were less than half as likely to receive payment. Stricter scope of practice laws increases the likelihood of psychiatric NPs receiving payment, the opposite of what was found in a recent specialty agnostic study.</p

DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales

ChatGPT-like models have revolutionized various applications in artificial intelligence, from summarization and coding to translation, matching or even surpassing human performance. However, the current landscape lacks an accessible, efficient, and cost-effective end-to-end RLHF (Reinforcement Learning with Human Feedback) training pipeline for these powerful models, particularly when training at the scale of billions of parameters. This paper introduces DeepSpeed-Chat, a novel system that democratizes RLHF training, making it accessible to the AI community. DeepSpeed-Chat offers three key capabilities: an easy-to-use training and inference experience for ChatGPT-like models, a DeepSpeed-RLHF pipeline that replicates the training pipeline from InstructGPT, and a robust DeepSpeed-RLHF system that combines various optimizations for training and inference in a unified way. The system delivers unparalleled efficiency and scalability, enabling training of models with hundreds of billions of parameters in record time and at a fraction of the cost. With this development, DeepSpeed-Chat paves the way for broader access to advanced RLHF training, even for data scientists with limited resources, thereby fostering innovation and further development in the field of AI.Comment: 14 pages, 7 figure

Simulating image coaddition with the Nancy Grace Roman Space Telescope: I. Simulation methodology and general results

The upcoming Nancy Grace Roman Space Telescope will carry out a wide-area survey in the near infrared. A key science objective is the measurement of cosmic structure via weak gravitational lensing. Roman data will be undersampled, which introduces new challenges in the measurement of source galaxy shapes; a potential solution is to use linear algebra-based coaddition techniques such as Imcom that combine multiple undersampled images to produce a single oversampled output mosaic with a desired "target" point spread function (PSF). We present here an initial application of Imcom to 0.64 square degrees of simulated Roman data, based on the Roman branch of the Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) simulation. We show that Imcom runs successfully on simulated data that includes features such as plate scale distortions, chip gaps, detector defects, and cosmic ray masks. We simultaneously propagate grids of injected sources and simulated noise fields as well as the full simulation. We quantify the residual deviations of the PSF from the target (the "leakage"), as well as noise properties of the output images; we discuss how the overall tiling pattern as well as Moir\'e patterns appear in the final leakage and noise maps. We include appendices on interpolation algorithms and the interaction of undersampling with image processing operations that may be of broader applicability. The companion paper ("Paper II") explores the implications for weak lensing analyses.Comment: 28 pages, 19 figures, matches version accepted by MNRA

Simulating image coaddition with the Nancy Grace Roman Space Telescope: II. Analysis of the simulated images and implications for weak lensing

One challenge for applying current weak lensing analysis tools to the Nancy Grace Roman Space Telescope is that individual images will be undersampled. Our companion paper presented an initial application of Imcom - an algorithm that builds an optimal mapping from input to output pixels to reconstruct a fully sampled combined image - on the Roman image simulations. In this paper, we measure the output noise power spectra, identify the sources of the major features in the power spectra, and show that simple analytic models that ignore sampling effects underestimate the power spectra of the coadded noise images. We compute the moments of both idealized injected stars and fully simulated stars in the coadded images, and their 1- and 2-point statistics. We show that the idealized injected stars have root-mean-square ellipticity errors (1 - 6) x 10-4 per component depending on the band; the correlation functions are >= 2 orders of magnitude below requirements, indicating that the image combination step itself is using a small fraction of the overall Roman 2nd moment error budget, although the 4th moments are larger and warrant further investigation. The stars in the simulated sky images, which include blending and chromaticity effects, have correlation functions near the requirement level (and below the requirement level in a wide-band image constructed by stacking all 4 filters). We evaluate the noise-induced biases in the ellipticities of injected stars, and explain the resulting trends with an analytical model. We conclude by enumerating the next steps in developing an image coaddition pipeline for Roman.Comment: 25 pages, 20 figures. Submitted to MNRA

Improving spatial-resolution in high cone-angle micro-CT by source deblurring

Micro scale computed tomography (CT) can resolve many features in cellular structures, bone formations, minerals properties and composite materials not seen at lower spatial-resolution. Those features enable us to build a more comprehensive model for th

Rapidly-converging multigrid reconstruction of cone-beam tomographic data

In the context of large-angle cone-beam tomography (CBCT), we present a practical iterative reconstruction (IR) scheme designed for rapid convergence as required for large datasets. The robustness of the reconstruction is provided by the “space-filling” source trajectory along which the experimental data is collected. The speed of convergence is achieved by leveraging the highly isotropic nature of this trajectory to design an approximate deconvolution filter that serves as a pre-conditioner in a multi-grid scheme. We demonstrate this IR scheme for CBCT and compare convergence to that of more traditional techniques.This research was supported under the Australian Research Council's Linkage Projects funding scheme (project number LP150101040), in collaboration with FEI

Comparison of industry payments to psychiatrists and psychiatric advanced practice clinicians in the USA, 2021: a cross-sectional study

Objectives To compare industry payment patterns among US psychiatrists and psychiatric advanced practice clinicians (APCs) and determine how scope of practice laws has influenced these patterns.Design Cross-sectional study.Setting This study used the publicly available US Centers for Medicare and Medicaid Services Sunshine Act Open Payment database and the National Plan and Provider Enumeration System (NPPES) database for the year 2021.Participants All psychiatrists and psychiatric APCs (subdivided into nurse practitioners (NPs) and clinical nurse specialists (CNSs)) included in either database.Primary and secondary outcome measures Number and percentage of clinicians receiving industry payments and value of payments received. Total payments and number of transactions by type of payment, payment source and clinician type were also evaluated.Results A total of 85 053 psychiatric clinicians (61 011 psychiatrists (71.7%), 21 895 NPs (25.7%), 2147 CNSs (2.5%)) were reviewed; 16 240 (26.6%) psychiatrists received non-research payment from industry, compared with 10 802 (49.3%) NPs and 231 (10.7%) CNSs (p&lt;0.001) for pairwise comparisons). Psychiatric NPs were significantly more likely to receive industry payments compared with psychiatrists (incidence rate ratio (IRR), 1.85 (95% CI 1.81 to 1.88); p&lt;0.001)). Compared with psychiatrists, NPs were more likely to receive payments of &gt; United States Dollars (US) $) 100 (33.9$ 1000 (5.3% vs 4.1%; IRR, 1.29 (1.20 to 1.38); p&lt;0.001) but less likely to receive &gt; US$ 10 000 (0.4% vs 1.0%; IRR, 0.39 (0.31 to 0.49); p&lt;0.001). NPs in states with ‘reduced’ or ‘restricted’ scope of practice received more frequent payments (reduced: IRR, 1.22 (1.18 to 1.26); restricted: IRR, 1.26 (1.22 to 1.30), both p&lt;0.001).Conclusions Psychiatric NPs were nearly two times as likely to receive industry payments as psychiatrists, while psychiatric CNSs were less than half as likely to receive payment. Stricter scope of practice laws increases the likelihood of psychiatric NPs receiving payment, the opposite of what was found in a recent specialty agnostic study