流媒体数据中对异常的实时检测正在受到越来越多的关注，因为它使我们能够提高警报，预测故障并检测到整个行业的入侵或威胁。然而，很少有人注意比较流媒体数据（即在线算法）的异常检测器的有效性和效率。在本文中，我们介绍了来自不同算法家族（即基于距离，密度，树木或投影）的主要在线检测器的定性合成概述，并突出了其构建，更新和测试检测模型的主要思想。然后，我们对在线检测算法的定量实验评估以及其离线对应物进行了彻底的分析。检测器的行为与不同数据集（即元功能）的特征相关，从而提供了对其性能的元级分析。我们的研究介绍了文献中几个缺失的见解，例如（a）检测器对随机分类器的可靠性以及什么数据集特性使它们随机执行； （b）在线探测器在何种程度上近似离线同行的性能； （c）哪种绘制检测器的策略和更新原始图最适合检测仅在数据集的功能子空间中可见的异常； （d）属于不同算法家族的探测器的有效性与效率之间的权衡是什么； （e）数据集的哪些特定特征产生在线算法以胜过所有其他特征。

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.

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet and CIFAR, little research has considered real world applications, in particular in the medical domain. Our research shows that, contrary to previous claims, robustness of chest x-ray classification is much harder to evaluate and leads to very different assessments based on the dataset, the architecture and robustness metric. We argue that previous studies did not take into account the peculiarity of medical diagnosis, like the co-occurrence of diseases, the disagreement of labellers (domain experts), the threat model of the attacks and the risk implications for each successful attack. In this paper, we discuss the methodological foundations, review the pitfalls and best practices, and suggest new methodological considerations for evaluating the robustness of chest xray classification models. Our evaluation on 3 datasets, 7 models, and 18 diseases is the largest evaluation of robustness of chest x-ray classification models.

3D gaze estimation is most often tackled as learning a direct mapping between input images and the gaze vector or its spherical coordinates. Recently, it has been shown that pose estimation of the face, body and hands benefits from revising the learning target from few pose parameters to dense 3D coordinates. In this work, we leverage this observation and propose to tackle 3D gaze estimation as regression of 3D eye meshes. We overcome the absence of compatible ground truth by fitting a rigid 3D eyeball template on existing gaze datasets and propose to improve generalization by making use of widely available in-the-wild face images. To this end, we propose an automatic pipeline to retrieve robust gaze pseudo-labels from arbitrary face images and design a multi-view supervision framework to balance their effect during training. In our experiments, our method achieves improvement of 30% compared to state-of-the-art in cross-dataset gaze estimation, when no ground truth data are available for training, and 7% when they are. We make our project publicly available at https://github.com/Vagver/dense3Deyes.

In recent years, graph neural networks (GNNs) have emerged as a promising tool for solving machine learning problems on graphs. Most GNNs are members of the family of message passing neural networks (MPNNs). There is a close connection between these models and the Weisfeiler-Leman (WL) test of isomorphism, an algorithm that can successfully test isomorphism for a broad class of graphs. Recently, much research has focused on measuring the expressive power of GNNs. For instance, it has been shown that standard MPNNs are at most as powerful as WL in terms of distinguishing non-isomorphic graphs. However, these studies have largely ignored the distances between the representations of nodes/graphs which are of paramount importance for learning tasks. In this paper, we define a distance function between nodes which is based on the hierarchy produced by the WL algorithm, and propose a model that learns representations which preserve those distances between nodes. Since the emerging hierarchy corresponds to a tree, to learn these representations, we capitalize on recent advances in the field of hyperbolic neural networks. We empirically evaluate the proposed model on standard node and graph classification datasets where it achieves competitive performance with state-of-the-art models.

Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.

加强学习（RL）通常需要将问题分解为子任务，并在这些任务上构成学习的行为。 RL中的组成性有可能创建与其他系统功能接口的模块化子任务单元。但是，生成的组成模型需要表征成分特征鲁棒性的最小假设。我们使用分类观点为RL的\ emph {组成理论}开发了一个框架。鉴于组成性的分类表示，我们研究了足够的条件，在这些条件下，逐行学习与总体学习相同的最佳政策。特别是，我们的方法引入了类别$ \ mathsf {MDP} $，其对象是马尔可夫决策过程（MDPS），用作任务模型。我们表明$ \ Mathsf {MDP} $接收天然的构图操作，例如某些纤维产品和求职。这些操作在RL中具有明确的组成现象，并统一了现有的结构，例如在复合MDP中刺破危险状态并结合了状态行动对称性。我们还通过引入Zig-Zag图的语言来建模顺序任务完成，该图是在$ \ Mathsf {MDP} $中立即应用曲调操作的立即应用。

最近，已经努力将信号阶段和时机（SPAT）消息标准化。这些消息包含所有信号交叉方法的信号相时机。因此，这些信息可用于有效的运动计划，从而导致更多均匀的交通流量和均匀的速度轮廓。尽管努力为半活化的信号控制系统提供了可靠的预测，但预测完全驱动控制的信号相时仍具有挑战性。本文提出了使用聚合的流量信号和循环检测器数据的时间序列预测框架。我们利用最先进的机器学习模型来预测未来信号阶段的持续时间。线性回归（LR），随机森林（RF）和长期内存（LSTM）神经网络的性能是针对天真基线模型进行评估的。结果基于瑞士苏黎世的全面信号控制系统的经验数据集表明，机器学习模型的表现优于常规预测方法。此外，基于树木的决策模型（例如RF）的表现最佳，其准确性满足实用应用要求。

昼夜节律是哺乳动物（例如睡眠，代谢，稳态，情绪变化等）各种重要生理和行为过程的中心。已经表明，这种节奏来自位于上核（SCN）中的神经元网络的自维持的生物分子振荡。在正常情况下，由于视网膜的信号，该网络仍然同步到昼夜周期。这些神经元振荡与外部光信号的未对准会破坏众多生理功能，并对健康和福祉造成持久的损失。在这项工作中，我们研究了现代的计算神经科学模型，以确定昼夜节律对不同频率和占空比外部光信号的限制。我们采用无基质方法来定位各种驾驶条件的高维模型的周期性稳态。我们的算法管道可以实现分叉图的数值延续和构建W.R.T.强制参数。我们在计算中探讨了昼夜节律网络中异质性的影响，以及矫正治疗性干预措施（例如药物分子长期的）的效果。最后，我们采用无监督的学习来构建数据驱动的嵌入空间来表示神经元异质性。

用于配置虚拟化基站（VBS）的开放无线接入网络（O-RAN）的设计对网络运营商来说至关重要。此任务具有挑战性，因为优化VBS调度程序需要了解参数的知识，这些参数是不稳定且要求提前获得的。在本文中，我们提出了一种在线学习算法，用于平衡VBS的性能和能耗。该算法在不可预见的条件下（例如非平稳交通和网络状态）提供了性能保证，并且忽略了VBS操作配置文件。我们以最通用的形式研究了该问题，并证明所提出的技术即使在快速变化的环境中也能达到次线性遗憾（即零平均最佳差距）。通过使用现实世界数据和各种跟踪驱动的评估，我们的发现表明，与最先进的基准相比，VB的功耗最高可节省74.3％。