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。

在本文中，我们为基于非交换代数的代数神经网络（ALGNN）提供稳定性结果。 ALGNN是堆叠的分层结构，每个层都与代数信号模型（ASM）相关联，由代数，矢量空间和同态性。信号被建模为矢量空间的元素，过滤器是代数中的元素，而同态则可以实现过滤器作为混凝土操作员。我们研究了代数过滤器在非交换代数对同态扰动中的稳定性，并提供了保证稳定性的条件。我们表明，轮班运算符和偏移和扰动之间的换向性不会影响稳定体系结构的属性。这提供了一个问题，即转移不变性是否是保证稳定性的卷积体系结构的必要属性。此外，我们表明，尽管非交换代数中过滤器的频率响应在交换代数中与过滤器相对于过滤器表现出很大的差异，但它们的稳定过滤器的衍生物具有相似的行为。

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.

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

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.

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。

创建可以自然与人类互动的代理是人工智能（AI）研究中的共同目标。但是，评估这些互动是具有挑战性的：收集在线人类代理相互作用缓慢而昂贵，但更快的代理指标通常与交互式评估相关。在本文中，我们评估了这些现有评估指标的优点，并提出了一种新颖的评估方法，称为标准化测试套件（STS）。 STS使用从真实人类交互数据中挖掘出的行为方案。代理商请参阅重播方案上下文，接收指令，然后将控制权控制以脱机完成交互。记录这些代理的延续并将其发送给人类注释者以将其标记为成功或失败，并且根据其成功的连续性比例对代理进行排名。最终的ST是自然主义相互作用的快速，控制，可解释的和代表的。总的来说，STS巩固了我们许多标准评估指标中所需的许多值，从而使我们能够加速研究进展，以生产可以自然与人类互动的代理。可以在https://youtu.be/yr1tnggorgq上找到视频。