Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.

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.

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

Many problems in machine learning involve bilevel optimization (BLO), including hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems consist of two nested sub-problems, called the outer and inner problems, respectively. In practice, often at least one of these sub-problems is overparameterized. In this case, there are many ways to choose among optima that achieve equivalent objective values. Inspired by recent studies of the implicit bias induced by optimization algorithms in single-level optimization, we investigate the implicit bias of gradient-based algorithms for bilevel optimization. We delineate two standard BLO methods -- cold-start and warm-start -- and show that the converged solution or long-run behavior depends to a large degree on these and other algorithmic choices, such as the hypergradient approximation. We also show that the inner solutions obtained by warm-start BLO can encode a surprising amount of information about the outer objective, even when the outer parameters are low-dimensional. We believe that implicit bias deserves as central a role in the study of bilevel optimization as it has attained in the study of single-level neural net optimization.

Machine learning model development and optimisation can be a rather cumbersome and resource-intensive process. Custom models are often more difficult to build and deploy, and they require infrastructure and expertise which are often costly to acquire and maintain. Machine learning product development lifecycle must take into account the need to navigate the difficulties of developing and deploying machine learning models. evoML is an AI-powered tool that provides automated functionalities in machine learning model development, optimisation, and model code optimisation. Core functionalities of evoML include data cleaning, exploratory analysis, feature analysis and generation, model optimisation, model evaluation, model code optimisation, and model deployment. Additionally, a key feature of evoML is that it embeds code and model optimisation into the model development process, and includes multi-objective optimisation capabilities.

图卷积学习导致了各个领域的许多令人兴奋的发现。但是，在某些应用中，传统图不足以捕获数据的结构和复杂性。在这种情况下，多编码自然出现是可以嵌入复杂动力学的离散结构。在本文中，我们开发了有关多编码的卷积信息处理，并引入了卷积多编码神经网络（MGNN）。为了捕获每个多数边缘内外的信息传播的复杂动力学，我们正式化了一个卷积信号处理模型，从而定义了多格画上信号，过滤和频率表示的概念。利用该模型，我们开发了多个学习架构，包括采样程序以降低计算复杂性。引入的体系结构用于最佳无线资源分配和仇恨言语本地化任务，从而比传统的图形神经网络的性能提高了。

我们的目标是评估汽车系统是否更改（即搜索空间或超参数优化）将改善最终模型在生产任务上的性能。但是，我们无法测试生产任务的更改。取而代之的是，我们只能访问有关AutoML系统先前执行的任务的有限描述符，例如数据点或功能的数量。我们还拥有一组开发任务来测试更改，例如，从OpenML取样，没有使用限制。但是，开发和生产任务分布不同，导致我们追求只能改善发展而不是生产的变化。本文提出了一种利用有关汽车生产任务的描述符信息的方法，以选择最相关开发任务的过滤子集。实证研究表明，我们的过滤策略提高了评估与开发不同分布不同的保留任务变更的能力。

了解全文学术文章的关键见解至关重要，因为它使我们能够确定有趣的趋势，洞悉研究和发展，并构建知识图。但是，只有在考虑全文时才可用一些有趣的关键见解。尽管研究人员在简短文档中的信息提取方面取得了重大进展，但从全文学术文献中提取科学实体仍然是一个具有挑战性的问题。这项工作提出了一种称为ENEREX的自动端对端研究实体提取器，用于提取技术集，客观任务，全文学术学术研究文章等技术方面。此外，我们提取了三个新颖的方面，例如源代码，计算资源，编程语言/库中的链接。我们演示了Enerex如何从计算机科学领域的大规模数据集中提取关键见解和趋势。我们进一步测试了多个数据集上的管道，发现ENEREX在最新模型的状态下进行了改进。我们强调了现有数据集的能力如何受到限制，以及enerex如何适应现有知识图。我们还向未来研究的指针进行了详细的讨论。我们的代码和数据可在https://github.com/discoveryanalyticscenter/enerex上公开获取。

研究了生物样品中的小分子，以提供有关疾病状态，环境毒素，天然产品发现和许多其他应用的信息。小分子混合物组成的主要窗口是串联质谱法（MS2），它产生的数据具有高灵敏度和每百万分辨率的部分。我们采用MS2中质量数据的多尺度正弦嵌入，旨在应对MS2数据的完整分辨率学习的挑战。使用这些嵌入，我们为光谱库搜索提供了新的最新模型，这是MS2数据初始评估的标准任务。我们还引入了一项新的任务，从MS2数据中引入了化学性质预测，该预测在高通量MS2实验中具有自然应用，并表明可以在10个化合物中获得平均$ r^2 $ 80 \％，可以在10个化学特性中获得优先级的10个化学性质药化学家。我们使用降低降低技术和具有不同浮点分辨率的实验，以显示从MS2数据学习中多尺度正弦嵌入的重要作用。

无价值运动捕获已成为近年来计算机视觉研究的积极研究领域。其广泛的应用在各种各样的领域中是已知的，包括计算机动画，人类运动分析，生物医学研究，虚拟现实和体育科学。估计人类姿势最近在计算机视觉界中提高了越来越长，但由于不确定性的深度和缺乏合成数据集，这是一个具有挑战性的任务。最近提出了各种方法来解决这个问题，其中许多是基于深度学习。它们主要专注于提高现有基准的性能，具有重要进展，特别是2D图像。基于强大的深度学习技术和最近收集的现实数据集，我们探讨了一个模型，可以完全基于2D图像预测动画的骨架。使用不同的身体形状从易于复杂的不同身体形状产生的不同现实世界数据集生成的帧。实施过程在自己的数据集上使用DeePlabCut来执行许多必要的步骤，然后使用输入帧训练模型。输出是人类运动的动画骨架。复合数据集和其他结果是深层模型的“地面真相”。