拖延是任务的非理性延迟，是在线学习中的普遍情况。潜在的负面后果包括更高的辍学风险，增加压力和情绪减少。由于学习管理系统和学习分析的增加，可以检测到这种行为的指标，从而预测未来的拖延和其他扩张行为。但是，关注此类预测的研究很少。此外，几乎不存在涉及不同类型的预测指标和预测性能之间的比较的研究。在这项研究中，我们旨在通过分析多个机器学习算法的性能来填补这些研究空白，以预测具有两类预测指标的高等教育环境中在线作业的延迟或及时提交：基于主观的，基于问卷的变量和目标，客观，客观，客观，目标，客观，客观，客观，客观，从学习管理系统中提取的基于日志数据的指标。结果表明，具有客观预测变量的模型始终优于主观预测指标的模型，并且两种变量类型的组合表现稍好一些。对于这三个选项中的每一个，一种不同的方法盛行（主观，贝叶斯多层次模型的梯度增强机器，共同预测指标的随机森林）。我们得出的结论是，在学习管理系统中实施此类模型之前，应仔细注意预测变量和算法。

The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these representations, in order to explain the neural network's decisions, is an active and multifaceted research field. To gain a deeper understanding of a central aspect of this field, we have performed a targeted review focusing on research that aims to associate internal representations with human understandable concepts. In doing this, we added a perspective on the existing research by using primarily deductive nomological explanations as a proposed taxonomy. We find this taxonomy and theories of causality, useful for understanding what can be expected, and not expected, from neural network explanations. The analysis additionally uncovers an ambiguity in the reviewed literature related to the goal of model explainability; is it understanding the ML model or, is it actionable explanations useful in the deployment domain?

Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain solely in the field of document classification. With recent performance improvements, supervised few-shot methods, combined with a simple topic extraction method pose a significant challenge to unsupervised topic modeling methods. Our research shows that supervised few-shot learning, combined with a simple topic extraction method, can outperform unsupervised topic modeling techniques in terms of generating coherent topics, even when only a few labeled documents per class are used.

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.

We design and implement an adaptive machine learning equalizer that alternates multiple linear and nonlinear computational layers on an FPGA. On-chip training via gradient backpropagation is shown to allow for real-time adaptation to time-varying channel impairments.

Pre-trained protein language models have demonstrated significant applicability in different protein engineering task. A general usage of these pre-trained transformer models latent representation is to use a mean pool across residue positions to reduce the feature dimensions to further downstream tasks such as predicting bio-physics properties or other functional behaviours. In this paper we provide a two-fold contribution to machine learning (ML) driven drug design. Firstly, we demonstrate the power of sparsity by promoting penalization of pre-trained transformer models to secure more robust and accurate melting temperature (Tm) prediction of single-chain variable fragments with a mean absolute error of 0.23C. Secondly, we demonstrate the power of framing our prediction problem in a probabilistic framework. Specifically, we advocate for the need of adopting probabilistic frameworks especially in the context of ML driven drug design.

Much computer vision research has focused on natural images, but technical documents typically consist of abstract images, such as charts, drawings, diagrams, and schematics. How well do general web search engines discover abstract images? Recent advancements in computer vision and machine learning have led to the rise of reverse image search engines. Where conventional search engines accept a text query and return a set of document results, including images, a reverse image search accepts an image as a query and returns a set of images as results. This paper evaluates how well common reverse image search engines discover abstract images. We conducted an experiment leveraging images from Wikimedia Commons, a website known to be well indexed by Baidu, Bing, Google, and Yandex. We measure how difficult an image is to find again (retrievability), what percentage of images returned are relevant (precision), and the average number of results a visitor must review before finding the submitted image (mean reciprocal rank). When trying to discover the same image again among similar images, Yandex performs best. When searching for pages containing a specific image, Google and Yandex outperform the others when discovering photographs with precision scores ranging from 0.8191 to 0.8297, respectively. In both of these cases, Google and Yandex perform better with natural images than with abstract ones achieving a difference in retrievability as high as 54\% between images in these categories. These results affect anyone applying common web search engines to search for technical documents that use abstract images.

基于文本的游戏提供了一个具有挑战性的测试床，以评估语言理解，多步骤解决和常识性推理的虚拟代理。但是，速度是当前基于文本的游戏的主要局限性，主要是由于使用旧工具，以每秒300个步骤的限制。在这项工作中，我们介绍了TextWorldExpress，这是三个常见文本游戏基准的高性能实现，将模拟吞吐量增加了大约三个数量级，在常见桌面硬件上每秒超过一百万步。这大大降低了实验运行时，大约有一天可以进行十亿步尺度的实验。

简介白质超强度（WMHS）的自动分割是磁共振成像（MRI）神经影像分析的重要步骤。流体减弱的反转恢复（FLAIR加权）是MRI对比度，对于可视化和量化WMHS，这是脑小血管疾病和阿尔茨海默氏病（AD）特别有用的。临床MRI方案迁移到三维（3D）FLAIR加权的采集，以在所有三个体素维度中实现高空间分辨率。当前的研究详细介绍了深度学习工具的部署，以使自动化的WMH分割和表征从获得的3D Flair加权图像作为国家广告成像计划的一部分获得。 DDI研究中的642名参与者（283名男性，平均年龄：（65.18 +/- 9.33）年）中的材料和方法，在五个国家收集地点进行了培训和验证两个内部网络。在642名参与者的内部数据和一个外部数据集中，对三个模型进行了测试，其中包含来自国际合作者的29个情况。这些测试集进行了独立评估。使用了五个已建立的WMH性能指标与地面真理人体分割进行比较。测试的三个网络的结果，3D NNU-NET具有最佳性能，平均骰子相似性系数得分为0.78 +/- 0.10，其性能优于内部开发的2.5D模型和SOTA DEEP DEEP BAYESIAN网络。结论MRI协议中3D Flair加权图像的使用越来越多，我们的结果表明，WMH分割模型可以在3D数据上进行训练，并产生与无需更高的或更好的无需先进的WMH分割性能用于包括T1加权图像系列。

机器学习中的超参数（ML）受到了相当多的关注，并且高参数调整已被视为ML管道中的重要一步。但是，调整有多么有用？虽然先前已经进行了较小的实验，但本文中，我们进行了大规模研究，特别是涉及26毫升算法，250个数据集（回归以及二进制和多项式分类），6个得分指标和28,857,600算法运行。分析结果我们得出的结论是，对于许多ML算法，我们不应该期望平均而言，高参数调整会获得可观的收益，但是，可能有一些数据集的默认超参数性能差，而后者对于某些算法而言是比其他算法更真实的。通过定义一个组合算法的累积统计数据的单个HP_SCORE值，我们能够对预期从高参数调整到预期获得最低收益的26毫升算法进行排名的26毫升算法。我们认为，这样的研究可能会为ML从业者提供整体服务。