必须密切监控网络物理系统（CPS），以识别并潜在地缓解其常规操作期间出现的新兴问题。但是，他们通常产生的多元时间序列数据可能很复杂，可以理解和分析。虽然正式的产品文档通常会提供诊断建议的示例数据图，但属性，关键阈值和数据交互的纯粹多样性可能会使非专家们不知所措，他们随后从讨论论坛中寻求帮助来解释其数据日志。深度学习模型，例如长期记忆（LSTM）网络，可用于自动化这些任务，并提供对实时多元数据流中检测到的各种异常的明确解释。在本文中，我们介绍了RESAM，该过程是一项需求过程，该过程旨在整合领域专家，讨论论坛和正式产品文档的知识，以以时间序列属性的形式发现和指定需求和设计定义，这些属性有助于构建有效的深度学习异常检测器。我们提出了一个基于针对小型无空天空系统的飞行控制系统的案例研究，并证明其使用指导有效的异常检测模型的构建，同时还为解释性提供了基本支持。 RESAM与开放或关闭的在线论坛为日志分析提供讨论支持的域相关。

Accurate short-term traffic prediction plays a pivotal role in various smart mobility operation and management systems. Currently, most of the state-of-the-art prediction models are based on graph neural networks (GNNs), and the required training samples are proportional to the size of the traffic network. In many cities, the available amount of traffic data is substantially below the minimum requirement due to the data collection expense. It is still an open question to develop traffic prediction models with a small size of training data on large-scale networks. We notice that the traffic states of a node for the near future only depend on the traffic states of its localized neighborhoods, which can be represented using the graph relational inductive biases. In view of this, this paper develops a graph network (GN)-based deep learning model LocaleGN that depicts the traffic dynamics using localized data aggregating and updating functions, as well as the node-wise recurrent neural networks. LocaleGN is a light-weighted model designed for training on few samples without over-fitting, and hence it can solve the problem of few-sample traffic prediction. The proposed model is examined on predicting both traffic speed and flow with six datasets, and the experimental results demonstrate that LocaleGN outperforms existing state-of-the-art baseline models. It is also demonstrated that the learned knowledge from LocaleGN can be transferred across cities. The research outcomes can help to develop light-weighted traffic prediction systems, especially for cities lacking historically archived traffic data.

准确的实时流量预测对于智能运输系统（ITS）至关重要，它是各种智能移动应用程序的基石。尽管该研究领域以深度学习为主，但最近的研究表明，开发新模型结构的准确性提高正变得边缘。取而代之的是，我们设想可以通过在具有不同数据分布和网络拓扑的城市之间转移“与预测相关的知识”来实现改进。为此，本文旨在提出一个新型的可转移流量预测框架：域对抗空间 - 颞网（DASTNET）。 Dastnet已在多个源网络上进行了预训练，并通过目标网络的流量数据进行了微调。具体而言，我们利用图表表示学习和对抗域的适应技术来学习域不变的节点嵌入，这些嵌入式嵌入将进一步合并以建模时间流量数据。据我们所知，我们是第一个使用对抗性多域改编来解决网络范围的流量预测问题的人。 Dastnet始终优于三个基准数据集上的所有最新基线方法。训练有素的dastnet应用于香港的新交通探测器，并且在可用的探测器可用时（一天之内）可以立即（在一天之内）提供准确的交通预测。总体而言，这项研究提出了一种增强交通预测方法的替代方法，并为缺乏历史流量数据的城市提供了实际含义。

事物互联网（物联网）和人工智能（AI）的快速进步催化了智能城市的自适应交通信号控制系统（ATCS）的开发。特别是，深度增强学习（DRL）方法产生最先进的性能，并且具有很大的实际应用潜力。在现有的基于DRL的ATC中，受控信号从附近车辆收集交通状态信息，然后可以基于收集的信息确定最佳动作（例如，切换阶段）。 DRL模型完全“信任”该车辆正在向信号发送真实信息，使ATC易受伪造信息的对抗攻击。鉴于此，本文首次制定了一种新颖的任务，其中一组车辆可以协同地发送伪造的信息，以“欺骗”基于DRL的ATC，以节省他们的总旅行时间。为了解决拟议的任务，我们开发了由道路状语编码器，车辆解释器和通信机制组成的通用和有效的车辆斗争框架。我们采用我们的方法来攻击建立的基于DRL的ATC，并证明拼拼载的总行程时间可以通过合理数量的学习剧集显着减少，并且如果拼的车辆的数量增加，勾结效果将减小。此外，还提供了对基于DRL的ATC的实际部署的见解和建议。研究结果可以帮助提高ATC的可靠性和鲁棒性，并更好地保护智能移动系统。

Prior work has shown that it is possible to expand pretrained Masked Language Models (MLMs) to new languages by learning a new set of embeddings, while keeping the transformer body frozen. Despite learning a small subset of parameters, this approach is not compute-efficient, as training the new embeddings requires a full forward and backward pass over the entire model. In this work, we propose mini-model adaptation, a compute-efficient alternative that builds a shallow mini-model from a fraction of a large model's parameters. New language-specific embeddings can then be efficiently trained over the mini-model, and plugged into the aligned large model for rapid cross-lingual transfer. We explore two approaches to learn mini-models: MiniJoint, which jointly pretrains the primary model and the mini-model using a single transformer with a secondary MLM head at a middle layer; and MiniPost, where we start from a regular pretrained model and build a mini-model by extracting and freezing a few layers and learning a small number of parameters on top. Experiments on XNLI, MLQA and PAWS-X show that mini-model adaptation matches the performance of the standard approach using up to 2.4x less compute.

A large number of studies on Graph Outlier Detection (GOD) have emerged in recent years due to its wide applications, in which Unsupervised Node Outlier Detection (UNOD) on attributed networks is an important area. UNOD focuses on detecting two kinds of typical outliers in graphs: the structural outlier and the contextual outlier. Most existing works conduct experiments based on datasets with injected outliers. However, we find that the most widely-used outlier injection approach has a serious data leakage issue. By only utilizing such data leakage, a simple approach can achieve state-of-the-art performance in detecting outliers. In addition, we observe that most existing algorithms have a performance drop with varied injection settings. The other major issue is on balanced detection performance between the two types of outliers, which has not been considered by existing studies. In this paper, we analyze the cause of the data leakage issue in depth since the injection approach is a building block to advance UNOD. Moreover, we devise a novel variance-based model to detect structural outliers, which outperforms existing algorithms significantly at different injection settings. On top of this, we propose a new framework, Variance-based Graph Outlier Detection (VGOD), which combines our variance-based model and attribute reconstruction model to detect outliers in a balanced way. Finally, we conduct extensive experiments to demonstrate the effectiveness and efficiency of VGOD. The results on 5 real-world datasets validate that VGOD achieves not only the best performance in detecting outliers but also a balanced detection performance between structural and contextual outliers. Our code is available at https://github.com/goldenNormal/vgod-github.

我们根据计算一个扎根于每个顶点的某个加权树的家族而构成的相似性得分提出了一种有效的图形匹配算法。对于两个erd \ h {o} s-r \'enyi图$ \ mathcal {g}（n，q）$，其边缘通过潜在顶点通信相关联，我们表明该算法正确地匹配了所有范围的范围，除了所有的vertices分数外，有了很高的概率，前提是$ nq \ to \ infty $，而边缘相关系数$ \ rho $满足$ \ rho^2> \ alpha \ ailpha \大约0.338 $，其中$ \ alpha $是Otter的树木计数常数。此外，在理论上是必需的额外条件下，可以精确地匹配。这是第一个以显式常数相关性成功的多项式图匹配算法，并适用于稀疏和密集图。相比之下，以前的方法要么需要$ \ rho = 1-o（1）$，要么仅限于稀疏图。该算法的症结是一个经过精心策划的植根树的家族，称为吊灯，它可以有效地从同一树的计数中提取图形相关性，同时抑制不同树木之间的不良相关性。

深度神经网络（DNN）已在脑病变检测和分割中广泛采用。但是，在2D MRI切片中定位小病变是具有挑战性的，需要在3D上下文聚集的粒度和计算复杂性之间取得平衡。在本文中，我们提出了一种新型的视角变压器，以增强MRI特征的提取，以进行更准确的肿瘤检测。首先，所提出的变压器在3D脑扫描中收获了不同位置之间的远程相关性。其次，变压器将一堆切片功能堆叠为多个2D视图，并增强这些特征的视图，该功能大致以有效的方式实现了3D相关计算。第三，我们将提出的变压器模块部署在变压器主链中，该模块可以有效地检测到脑损伤周围的2D区域。实验结果表明，我们提出的观看式变压器在具有挑战性的大脑MRI数据集上对大脑病变检测表现良好。

由于乳腺癌的发生和死亡率很高，乳房X线照片中检测肿块很重要。在乳房X线照片质量检测中，对成对病变对应的建模特别重要。但是，大多数现有方法构建了相对粗糙的对应关系，并且尚未利用对应的监督。在本文中，我们提出了一个新的基于变压器的框架CL-NET，以端到端的方式学习病变检测和成对对应。在CL-NET中，提出了观察性病变检测器来实现跨视图候选者的动态相互作用，而病变接头则采用通信监督来更准确地指导相互作用过程。这两种设计的组合实现了对乳房X线照片的成对病变对应的精确理解。实验表明，CL-NET在公共DDSM数据集和我们的内部数据集上产生最先进的性能。此外，在低FPI制度中，它的表现优于先前的方法。

本文探讨了管状结构提取任务的点集表示。与传统的掩码表示相比，点集表示享有其灵活性和表示能力，这不会受到固定网格作为掩模的限制。受此启发，我们提出了PointCatter，这是管状结构提取任务的分割模型的替代方法。PointCatter将图像分为散射区域，并对每个散点区域预测点。我们进一步提出了基于贪婪的区域的两分匹配算法，以端到端训练网络。我们在四个公共管状数据集上基准测试了点刻表，并且有关管状结构分割和中心线提取任务的广泛实验证明了我们方法的有效性。代码可在https://github.com/zhangzhao2022/pointscatter上找到。