The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM), a physics-based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE-GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long-Short Term Memory neural networks to perform time-series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE-GCM ROPE has similar error to previous linear approaches while improving storm-time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE-GCM ROPE can capture the position resulting from TIE-GCM density with < 5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7 - 18 km.
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自1970年代初以来,已经开发并改进了质谱仪和不连贯的散射雷达(MSIS)模型家族。 MSI的最新版本是海军研究实验室(NRL)MSIS 2.0经验大气模型。 NRLMSIS 2.0提供物种密度,质量密度和温度估计作为位置和空间天气条件的功能。长期以来,MSIS模型一直是研究和运营社区中的大气模型的流行选择,但与许多模型一样,并未提供不确定性估计。在这项工作中,我们开发了基于机器学习(ML)的外层温度模型,该模型可与NRLMSIS 2.0一起使用,以相对于高保真卫星密度估计值校准其。我们的模型(称为MSIS-UQ)没有提供点估计,而是输出一个分布,该分布将使用称为校准误差评分的度量进行评估。我们表明,MSIS-UQ的DEMIAS nRLMSIS 2.0导致模型和卫星密度之间的差异减少25%,并且比太空力量的高精度卫星阻力模型更接近卫星密度。我们还通过生成物种密度,质量密度和温度的高度曲线来显示模型的不确定性估计功能。这明确证明了外层温度概率如何影响NRLMSIS 2.0内的密度和温度曲线。另一项研究显示,相对于单独的NRLMSIS 2.0,迅速过冷的能力提高了,从而增强了它可以捕获的现象。
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机器学习(ML)通常被视为一种黑盒回归技术,无法提供相当大的科学见解。 ML模型是通用函数近似器,如果正确使用,则可以提供与用于拟合的地面数据集有关的科学信息。 ML比参数模型的好处是,没有预定义的基础函数限制可以建模的现象。在这项工作中,我们在三个数据集上开发了ML模型:太空环境技术(SET)高精度卫星阻力模型(HASDM)密度数据库,这是Jacchia-Bowman 2008经验热层密度模型(JB2008),Jacchia-Bowman 2008经验的空间端段匹配数据集,以及具有挑战性的Minisatellite有效载荷(Champ)的加速度计衍生的密度数据集。将这些ML模型与海军研究实验室质谱仪和不相互分的散射雷达(NRLMSIS 2.0)模型进行比较,以研究中热层中传感后冷却的存在。我们发现NRLMSIS 2.0和JB2008-ML都不能说明后冷却,因此在强烈的地磁风暴(例如2003年万圣节风暴)之后的时期内表现不佳。相反,HASDM-ML和Champ-ML确实显示了传感后冷却的证据,表明这种现象存在于原始数据集中。结果表明,根据位置和暴风雨强度,速度1-3天的密度降低可能会发生1--3天。
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机器学习(ML)近年来往往应用于太空天气(SW)问题。 SW起源于太阳能扰动,包括由此产生的复杂变化,它们导致太阳和地球之间的系统。这些系统紧密耦合并不太了解。这为熟练的模型创造了具有关于他们预测的信心的知识。这种动态系统的一个例子是热层,地球上层大气的中性区域。我们无法预测其在低地球轨道中对象的卫星拖拽和碰撞操作的背景下具有严重的影响。即使使用(假设)完美的驾驶员预测,我们对系统的不完全知识也会导致往往是不准确的中性质量密度预测。正在进行持续努力来提高模型准确性,但密度模型很少提供不确定性的估计。在这项工作中,我们提出了两种技术来开发非线性ML模型以预测热散,同时提供校准的不确定性估计:蒙特卡罗(MC)丢失和直接预测概率分布,既使用预测密度(NLPD)损耗函数的负对数。我们展示了在本地和全局数据集上培训的模型的性能。这表明NLPD为这两种技术提供了类似的结果,但是直接概率方法具有更低的计算成本。对于在集合HASDM密度数据库上回归的全局模型,我们在具有良好校准的不确定性估计的独立测试数据上实现11%的错误。使用原位校准密度数据集,这两种技术都提供了13%的测试误差。 CHAMP模型(独立数据)占测试所有预测间隔的完美校准的2%。该模型也可用于获得具有给定时期的不确定性的全局预测。
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The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.
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Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.
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Electronic Health Records (EHRs) hold detailed longitudinal information about each patient's health status and general clinical history, a large portion of which is stored within the unstructured text. Temporal modelling of this medical history, which considers the sequence of events, can be used to forecast and simulate future events, estimate risk, suggest alternative diagnoses or forecast complications. While most prediction approaches use mainly structured data or a subset of single-domain forecasts and outcomes, we processed the entire free-text portion of EHRs for longitudinal modelling. We present Foresight, a novel GPT3-based pipeline that uses NER+L tools (i.e. MedCAT) to convert document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events such as disorders, medications, symptoms and interventions. Since large portions of EHR data are in text form, such an approach benefits from a granular and detailed view of a patient while introducing modest additional noise. On tests in two large UK hospitals (King's College Hospital, South London and Maudsley) and the US MIMIC-III dataset precision@10 of 0.80, 0.81 and 0.91 was achieved for forecasting the next biomedical concept. Foresight was also validated on 34 synthetic patient timelines by 5 clinicians and achieved relevancy of 97% for the top forecasted candidate disorder. Foresight can be easily trained and deployed locally as it only requires free-text data (as a minimum). As a generative model, it can simulate follow-on disorders, medications and interventions for as many steps as required. Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk estimation, virtual trials and clinical research to study the progression of diseases, simulate interventions and counterfactuals, and for educational purposes.
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Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.
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The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.
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Testing the significance of a variable or group of variables $X$ for predicting a response $Y$, given additional covariates $Z$, is a ubiquitous task in statistics. A simple but common approach is to specify a linear model, and then test whether the regression coefficient for $X$ is non-zero. However, when the model is misspecified, the test may have poor power, for example when $X$ is involved in complex interactions, or lead to many false rejections. In this work we study the problem of testing the model-free null of conditional mean independence, i.e. that the conditional mean of $Y$ given $X$ and $Z$ does not depend on $X$. We propose a simple and general framework that can leverage flexible nonparametric or machine learning methods, such as additive models or random forests, to yield both robust error control and high power. The procedure involves using these methods to perform regressions, first to estimate a form of projection of $Y$ on $X$ and $Z$ using one half of the data, and then to estimate the expected conditional covariance between this projection and $Y$ on the remaining half of the data. While the approach is general, we show that a version of our procedure using spline regression achieves what we show is the minimax optimal rate in this nonparametric testing problem. Numerical experiments demonstrate the effectiveness of our approach both in terms of maintaining Type I error control, and power, compared to several existing approaches.
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