在非欧几里得空间上卷积成功之后,在有关图形的各种任务上也验证了相应的合并方法。但是,由于固定的压缩配额和逐步合并设计,这些层次池方法仍然遭受局部结构损害和次优问题的困扰。在这项工作的启发下,我们提出了一种层次的合并方法,即SEP解决这两个问题。具体而言,在不分配特定层的压缩配额的情况下,全局优化算法旨在生成一次集群分配矩阵以一次汇总。然后,我们介绍了在环和网格合成图的重建中先前方法中局部结构损害的例证。除SEP外,我​​们还将分别设计两个分类模型,分别用于图形分类和节点分类。结果表明,SEP在图形分类基准上优于最先进的图形合并方法,并在节点分类上获得了卓越的性能。
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良好的研究努力致力于利用股票预测中的深度神经网络。虽然远程依赖性和混沌属性仍然是在预测未来价格趋势之前降低最先进的深度学习模型的表现。在这项研究中,我们提出了一个新的框架来解决这两个问题。具体地,在将时间序列转换为复杂网络方面,我们将市场价格系列转换为图形。然后,从映射的图表中提取参考时间点和节点权重之间的关联的结构信息以解决关于远程依赖性和混沌属性的问题。我们采取图形嵌入式以表示时间点之间的关联作为预测模型输入。节点重量被用作先验知识,以增强时间关注的学习。我们拟议的框架的有效性通过现实世界股票数据验证,我们的方法在几个最先进的基准中获得了最佳性能。此外,在进行的交易模拟中,我们的框架进一步获得了最高的累积利润。我们的结果补充了复杂网络方法在金融领域的现有应用,并为金融市场中决策支持的投资应用提供了富有识别的影响。
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Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaboration decision-making for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) has been widely used in solving decision-making problems. However, the existing DRL-based methods have been mainly focused on solving the decision-making of a single CAV. Using the existing DRL-based methods in mixed autonomy traffic cannot accurately represent the mutual effects of vehicles and model dynamic traffic environments. To address these shortcomings, this article proposes a graph reinforcement learning (GRL) approach for multi-agent decision-making of CAVs in mixed autonomy traffic. First, a generic and modular GRL framework is designed. Then, a systematic review of DRL and GRL methods is presented, focusing on the problems addressed in recent research. Moreover, a comparative study on different GRL methods is further proposed based on the designed framework to verify the effectiveness of GRL methods. Results show that the GRL methods can well optimize the performance of multi-agent decision-making for CAVs in mixed autonomy traffic compared to the DRL methods. Finally, challenges and future research directions are summarized. This study can provide a valuable research reference for solving the multi-agent decision-making problems of CAVs in mixed autonomy traffic and can promote the implementation of GRL-based methods into intelligent transportation systems. The source code of our work can be found at https://github.com/Jacklinkk/Graph_CAVs.
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我们提出了一种新型的复发图网络(RGN)方法,用于通过学习潜在的复杂随机过程来预测离散标记的事件序列。使用点过程的框架,我们将标记的离散事件序列解释为各种唯一类型的不同序列的叠加。图网络的节点使用LSTM来合并过去的信息,而图形注意力网络(GAT网络)引入了强烈的电感偏见,以捕获这些不同类型的事件之间的相互作用。通过更改自我注意力的机制从过去的事件中参加活动,我们可以从$ \ MATHCAL {O}(n^2)$(事件总数)到$ \ Mathcal的时间和空间复杂性降低{o}(| \ Mathcal {y} |^2)$(事件类型的数量)。实验表明,与最新的基于最新的变压器架构相比,所提出的方法可以提高对数可能具有较低时间和空间复杂性的对数可能具有较低时间和空间复杂性的任务的性能。
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在“知识图”(kgs)的表示领域中,超级关系的事实由主要三重和几个辅助属性描述组成,这被认为比基于三重的事实更全面,更具体。但是,由于代表实体之间的隶属关系的层次结构削弱,因此,单个视图中现有的超相关KG嵌入方法受到限制。为了打破这一限制,我们提出了一个双视性超相关kg(DH-kg)结构,该结构包含实体的超相关实例视图,以及对从实体到共同模型超相关的概念的超相关本体论视图和分层信息。在本文中,我们首先定义了DH-KG上的链接预测和实体键入任务,并根据医疗数据构建了两个DH-KG数据集,即从Wikidata和HTDM中提取的JW44K-6K。此外,我们根据Gran编码器,HGNN和联合学习提出了DH-KG嵌入模型DHGE。实验结果表明,DHGE在DH-KG上的表现优于基线模型。我们还提供了该技术在高血压药物领域中应用的示例。我们的模型和数据集公开可用。
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当前的最佳性能模型用于知识图推理(KGR)将几何学对象或概率分布引入嵌入实体,并将一阶逻辑(fol)查询引入低维矢量空间。它们可以总结为中心尺寸框架(点/框/锥,β/高斯分布等)。但是,它们具有有限的逻辑推理能力。而且很难概括到各种功能,因为中心和大小是一对一的约束,无法具有多个中心或尺寸。为了应对这些挑战,我们相反提出了一个名为“特征逻辑嵌入框架Flex”的新颖的KGR框架,这是第一个KGR框架,它不仅可以真正处理所有运营,包括连词,析取,否定,否定等等,而且还支持各种操作特征空间。具体而言,特征逻辑框架的逻辑部分是基于向量逻辑的,它自然地对所有FOL操作进行了建模。实验表明,FLEX在基准数据集上明显优于现有的最新方法。
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基于两阶段Walsh-Hadamard变换(WHT)的用于水下机器人的卷积神经网络(CNN)基于对象分类,提出了新的高效源特征压缩解决方案。在两阶段过程之后首先通过WHT转换对象图像。变换域张量子具有大值集中在RGB通道中矩阵的左上角。通过观察此属性,将变换域矩阵划分为内部和外部区域。因此,在这项工作中提出了两种新的分区方法:(i)固定内部区域和外部区域的尺寸; (ii)每张图像自适应地调节内部区域和外部区域的大小。提案是用来自美国新泽西州新泽西雷塔河捕获的水下对象数据集进行评估。据证明并验证了提案,有效地减少了基于学习的水下对象分类任务的培训时间,并与竞争方法相比增加了准确性。对象分类是基于视觉的水下机器人的重要组成部分,可以感知环境并自主导航。因此,该方法非常适合于水下机器人应用中的高效基于计算机视觉任务。
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基于深度神经网络的时间序列分类方法很容易在UCR数据集上过度拟合,这是由这些数据集的几次拍摄问题引起的。因此,为了减轻进一步提高准确性的过度拟合现象,我们首先提出标记为IncepionTime(LSTIME)的标记平滑,这与软标签的信息相比,与硬标签相比。接下来,提出了通过LSTIME手动调整软标签,提出了成立时间(KDTIME)的知识蒸馏,以便通过教师模型自动生成软标签。最后,为了纠正来自教师模型的错误预测的软标签,提出了具有成立时间(KDCTIME)的校准的知识蒸馏,在其中包含两个可选的校准策略,即通过重新排序(KDCR)通过翻译(KDCT)和KDC的可选校准策略(KDC)(KDCR )。实验结果表明,KDCTIME的准确性很有前景,而其推理时间比火箭速度快两个数量级,具有可接受的训练时间开销。
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Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.
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The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.
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