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.

Energy storage resources must consider both price uncertainties and their physical operating characteristics when participating in wholesale electricity markets. This is a challenging problem as electricity prices are highly volatile, and energy storage has efficiency losses, power, and energy constraints. This paper presents a novel, versatile, and transferable approach combining model-based optimization with a convolutional long short-term memory network for energy storage to respond to or bid into wholesale electricity markets. We apply transfer learning to the ConvLSTM network to quickly adapt the trained bidding model to new market environments. We test our proposed approach using historical prices from New York State, showing it achieves state-of-the-art results, achieving between 70% to near 90% profit ratio compared to perfect foresight cases, in both price response and wholesale market bidding setting with various energy storage durations. We also test a transfer learning approach by pre-training the bidding model using New York data and applying it to arbitrage in Queensland, Australia. The result shows transfer learning achieves exceptional arbitrage profitability with as little as three days of local training data, demonstrating its significant advantage over training from scratch in scenarios with very limited data availability.

Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction. However, this fast development inevitably made their flaws apparent -- especially in domains of reasoning where understanding the cause-effect relationship is important. One such domain is drug discovery, in which such understanding is required to make sense of data otherwise plagued by spurious correlations. Said spuriousness only becomes worse with the ongoing trend of ever-increasing amounts of data in the life sciences and thereby restricts researchers in their ability to understand disease biology and create better therapeutics. Therefore, to advance the science of drug discovery with AI it is becoming necessary to formulate the key problems in the language of causality, which allows the explication of modelling assumptions needed for identifying true cause-effect relationships. In this attention paper, we present causal drug discovery as the craft of creating models that ground the process of drug discovery in causal reasoning.

Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, rendering the effects of those features on a model's output comprehensible but at the expense of model complexity. We approach interpretability from a new angle: constrain the information about the features without restricting the complexity of the model. Borrowing from information theory, we use the Distributed Information Bottleneck to find optimal compressions of each feature that maximally preserve information about the output. The learned information allocation, by feature and by feature value, provides rich opportunities for interpretation, particularly in problems with many features and complex feature interactions. The central object of analysis is not a single trained model, but rather a spectrum of models serving as approximations that leverage variable amounts of information about the inputs. Information is allocated to features by their relevance to the output, thereby solving the problem of feature selection by constructing a learned continuum of feature inclusion-to-exclusion. The optimal compression of each feature -- at every stage of approximation -- allows fine-grained inspection of the distinctions among feature values that are most impactful for prediction. We develop a framework for extracting insight from the spectrum of approximate models and demonstrate its utility on a range of tabular datasets.

眼科图像可能包含相同的外观病理，这些病理可能导致自动化技术的失败以区分不同的视网膜退行性疾病。此外，依赖大型注释数据集和缺乏知识蒸馏可以限制基于ML的临床支持系统在现实环境中的部署。为了提高知识的鲁棒性和可传递性，需要一个增强的特征学习模块才能从视网膜子空间中提取有意义的空间表示。这样的模块（如果有效使用）可以检测到独特的疾病特征并区分这种视网膜退行性病理的严重程度。在这项工作中，我们提出了一个具有三个学习头的健壮疾病检测结构，i）是视网膜疾病分类的监督编码器，ii）一种无监督的解码器，用于重建疾病特异性的空间信息，iiii iii）一个新的表示模块，用于学习模块了解编码器折叠功能和增强模型的准确性之间的相似性。我们对两个公开可用的OCT数据集的实验结果表明，该模型在准确性，可解释性和鲁棒性方面优于现有的最新模型，用于分布视网膜外疾病检测。

在嘈杂的互联网规模数据集上进行了预测，已对具有广泛的文本，图像和其他模式能力的培训模型进行了大量研究。但是，对于许多顺序决策域，例如机器人技术，视频游戏和计算机使用，公开可用的数据不包含以相同方式训练行为先验所需的标签。我们通过半监督的模仿学习将互联网规模的预处理扩展到顺序的决策域，其中代理通过观看在线未标记的视频来学习行动。具体而言，我们表明，使用少量标记的数据，我们可以训练一个足够准确的反向动力学模型，可以标记一个巨大的未标记在线数据来源 - 在这里，在线播放Minecraft的在线视频 - 然后我们可以从中训练一般行为先验。尽管使用了本地人类界面（鼠标和键盘为20Hz），但我们表明，这种行为先验具有非平凡的零射击功能，并且可以通过模仿学习和加强学习，可以对其进行微调，以进行硬探索任务。不可能通过增强学习从头开始学习。对于许多任务，我们的模型都表现出人类水平的性能，我们是第一个报告可以制作钻石工具的计算机代理，这些工具可以花费超过20分钟（24,000个环境动作）的游戏玩法来实现。

深度学习方法的应用加快了挑战性电流问题的分辨率，最近显示出令人鼓舞的结果。但是，电力系统动力学不是快照，稳态操作。必须考虑这些动力学，以确保这些模型提供的最佳解决方案遵守实用的动力约束，避免频率波动和网格不稳定性。不幸的是，由于其高计算成本，基于普通或部分微分方程的动态系统模型通常不适合在控制或状态估计中直接应用。为了应对这些挑战，本文介绍了一种机器学习方法，以近乎实时近似电力系统动态的行为。该拟议的框架基于梯度增强的物理知识的神经网络（GPINNS），并编码有关电源系统的基本物理定律。拟议的GPINN的关键特征是它的训练能力而无需生成昂贵的培训数据。该论文说明了在单机无限总线系统中提出的方法在预测转子角度和频率的前进和反向问题中的潜力，以及不确定的参数，例如惯性和阻尼，以展示其在一系列电力系统应用中的潜力。

支架结构的构建支持所需的基序，赋予蛋白质功能，显示出对疫苗和酶设计的希望。但是，解决这个主题交易问题的一般解决方案仍然开放。当前的脚手架设计的机器学习技术要么仅限于不切实际的小脚手架（长达20个长度），要么难以生产多种不同的脚手架。我们建议通过E（3） - 等级图神经网络学习各种蛋白质主链结构的分布。我们开发SMCDIFF以有效地从给定主题的条件下从该分布中采样脚手架；我们的算法是从理论上确保从扩散模型中的有条件样品，以大规模计算限制。我们通过与Alphafold2预测的结构保持一致的方式来评估我们设计的骨干。我们表明我们的方法可以（1）最多80个残基的样品支架，以及（2）实现固定基序的结构多样的支架。