Skin cancer is the most common malignancy in the world. Automated skin cancer detection would significantly improve early detection rates and prevent deaths. To help with this aim, a number of datasets have been released which can be used to train Deep Learning systems - these have produced impressive results for classification. However, this only works for the classes they are trained on whilst they are incapable of identifying skin lesions from previously unseen classes, making them unconducive for clinical use. We could look to massively increase the datasets by including all possible skin lesions, though this would always leave out some classes. Instead, we evaluate Siamese Neural Networks (SNNs), which not only allows us to classify images of skin lesions, but also allow us to identify those images which are different from the trained classes - allowing us to determine that an image is not an example of our training classes. We evaluate SNNs on both dermoscopic and clinical images of skin lesions. We obtain top-1 classification accuracy levels of 74.33% and 85.61% on clinical and dermoscopic datasets, respectively. Although this is slightly lower than the state-of-the-art results, the SNN approach has the advantage that it can detect out-of-class examples. Our results highlight the potential of an SNN approach as well as pathways towards future clinical deployment.

对现代学术界的不幸反映，出版后通常会忽略代码 - 错误修复 /维护没有学术的“荣誉”。代码通常不可用，或者（如果有）包含错误，不完整或依赖于过时 /不可用的库。这对可重复性和一般科学进步有重大影响。神经体系结构搜索（NAS）也不例外，在可重复性方面进行了一些先前的工作。但是，我们认为这些不考虑长期可重复性问题。因此，我们建议使用长期NAS可重复性的清单。我们根据常见的NAS方法评估清单，同时提出如何回顾性使这些方法更长期可重复。

了解物种的丰富是迈向理解其长期可持续性的第一步和我们可能对其的影响。生态学家使用相机陷阱来远程调查，用于存在特定的动物物种。以前的研究表明，可以训练深度学习模型，以便在相机陷阱图像内自动检测和分类动物，具有高度的信心。然而，培训这些模型的能力是依赖于拥有足够高质量训练数据的依赖性。当动物很少罕见或数据集是不存在的？该研究提出了一种使用颈部珍稀动物的图像的方法（专注于苏格兰野猫队）来生成训练数据集。我们探讨与在野生收集的数据应用时培训的普遍存在培训的模型相关的挑战。该研究是以生态学家在规划/工程中的需求的语境。在其他研究中之后，该项目建立了对象检测的集合，然后使用不同的图像操纵和类结构化技术来测试的对象检测，图像分割和图像分类模型来鼓励模型泛化。在苏格兰野猫队的背景下，研究得出结论，捕获在囚禁图像上的模型不能使用现有技术来推广到野生摄像机陷阱图像。然而，基于两级模型Wildcat与Wildcat的最终模型表演实现了81.6％的总精度得分，并且在测试集中的野猫准确度得分为54.8％，其中仅包含野猫队的1％的图像。这表明使用囚禁图像是可行的，具有进一步的研究。这是第一个研究，该研究试图基于囚禁数据生成培训集，并在规划/工程中的生态学家的背景下探讨这些模型的发展。

文本分类长期以来一直是自然语言处理中的主食（NLP），其中包含跨越各种区域的应用，如情绪分析，推荐系统和垃圾邮件检测。通过如此强大的解决方案，它通常很诱人，因为当您握住锤子时，将其用作所有NLP问题的Go-tool，一切都看起来像钉子。然而，我们在这里争辩说，使用分类目前正在解决的许多任务实际上是被挖掘成一个分类模具，如果我们相反，如果我们将它们解决作为排名问题，我们不仅改善了模型，而且我们达到了更好的性能。我们提出了一种新颖的端到端排名方法，该方法包括负责产生一对文本序列的表示的变压器网络，该文本序列又传递到基于的上下文聚合网络中输出用于确定对序列的排序到序列的序列的汇总网络。有关相关性的一些概念。我们对公开可用数据集进行了多项实验，并调查使用分类常进行解决的问题的排名。在一个实验的实验中，在偏斜的情绪分析数据集中，将排名结果转换为分类标签，对最先进的文本分类产生了大约22％的改进，证明了文本在某些情况下对文本分类进行了效果。

Photo-identification (photo-id) is one of the main non-invasive capture-recapture methods utilised by marine researchers for monitoring cetacean (dolphin, whale, and porpoise) populations. This method has historically been performed manually resulting in high workload and cost due to the vast number of images collected. Recently automated aids have been developed to help speed-up photo-id, although they are often disjoint in their processing and do not utilise all available identifying information. Work presented in this paper aims to create a fully automatic photo-id aid capable of providing most likely matches based on all available information without the need for data pre-processing such as cropping. This is achieved through a pipeline of computer vision models and post-processing techniques aimed at detecting cetaceans in unedited field imagery before passing them downstream for individual level catalogue matching. The system is capable of handling previously uncatalogued individuals and flagging these for investigation thanks to catalogue similarity comparison. We evaluate the system against multiple real-life photo-id catalogues, achieving mAP@IOU[0.5] = 0.91, 0.96 for the task of dorsal fin detection on catalogues from Tanzania and the UK respectively and 83.1, 97.5% top-10 accuracy for the task of individual classification on catalogues from the UK and USA.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

宽带音频波形评估网络（Wawenets）是直接在宽带音频波形上运行的卷积神经网络，以便对这些波形进行评估。在目前的工作中，这些评估赋予了电信语音的素质（例如嘈杂，清晰度，整体语音质量）。 Wawenets是无引用网络，因为它们不需要他们评估的波形的``参考''（原始或未经证实的）版本。我们最初的Wawenet出版物引入了四个Wawenets，并模拟了已建立的全参考语音质量或清晰度估计算法的输出。我们已经更新了Wawenet架构，以提高效率和有效性。在这里，我们提出了一个密切跟踪七个不同质量和可理解性值的单一Wawenet。我们创建了第二个网络，该网络还跟踪四个主观语音质量维度。我们提供第三个网络，专注于公正的质量分数并达到很高的共识。这项工作用13种语言利用了334小时的演讲，超过200万个全参考目标值和超过93,000个主观意见分数。我们还解释了Wawenets的操作，并使用信号处理的语言确定其操作的关键：Relus从战略上将光谱信息从非DC组件移动到DC组件中。 96输出信号的直流值在96-D潜在空间中定义了一个向量，然后将该向量映射到输入波形的质量或清晰度值。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

We develop a rigorous mathematical analysis of zero-shot learning with attributes. In this setting, the goal is to label novel classes with no training data, only detectors for attributes and a description of how those attributes are correlated with the target classes, called the class-attribute matrix. We develop the first non-trivial lower bound on the worst-case error of the best map from attributes to classes for this setting, even with perfect attribute detectors. The lower bound characterizes the theoretical intrinsic difficulty of the zero-shot problem based on the available information -- the class-attribute matrix -- and the bound is practically computable from it. Our lower bound is tight, as we show that we can always find a randomized map from attributes to classes whose expected error is upper bounded by the value of the lower bound. We show that our analysis can be predictive of how standard zero-shot methods behave in practice, including which classes will likely be confused with others.

多对象跟踪（MOT）中的当前方法依赖于经历可预测的运动的独立对象轨迹，以有效地跟踪大量对象。诸如挥发物体运动和不完美检测的对抗性条件创造了一个具有挑战性的跟踪景观，其中建立的方法可以产生不充分的结果。开发了多个假设超图（MHHT）以在噪声检测中执行相互依存物体中的MOT。该方法通过超图扩展了传统的多假假设跟踪（MHT）以模拟相关对象运动，允许在具有挑战性的情况下进行鲁棒跟踪。MHHT应用于在胚胎C.杆状内的后期胚胎发生期间进行缝电池跟踪。