Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent low-pass filtering property of GNNs. In general, since the real-world graphs are often a complex mixture of diverse subgraph patterns, learning a universal spectral filter on the graph from the global perspective as in most current works may still suffer from great difficulty in adapting to the variation of local patterns. On the basis of the theoretical analysis on local patterns, we rethink the existing spectral filtering methods and propose the \textbf{\underline{N}}ode-oriented spectral \textbf{\underline{F}}iltering for \textbf{\underline{G}}raph \textbf{\underline{N}}eural \textbf{\underline{N}}etwork (namely NFGNN). By estimating the node-oriented spectral filter for each node, NFGNN is provided with the capability of precise local node positioning via the generalized translated operator, thus discriminating the variations of local homophily patterns adaptively. Meanwhile, the utilization of re-parameterization brings a good trade-off between global consistency and local sensibility for learning the node-oriented spectral filters. Furthermore, we theoretically analyze the localization property of NFGNN, demonstrating that the signal after adaptive filtering is still positioned around the corresponding node. Extensive experimental results demonstrate that the proposed NFGNN achieves more favorable performance.

In recent years, semi-supervised graph learning with data augmentation (DA) is currently the most commonly used and best-performing method to enhance model robustness in sparse scenarios with few labeled samples. Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes. Furthermore, over-squashing of information is caused by the negative curvature that formed by the non-uniformity distribution and strong clustering in complex graph. To address these challenges, this paper presents a novel method named Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation (HG-MDA). For the problem of heterogeneity of information in DA, node and topology augmentation strategies are proposed for the characteristics of heterogeneous graph. And meta-relation-based attention is applied as one of the indexes for selecting augmented nodes and edges. For the problem of over-squashing of information, triangle based edge adding and removing are designed to alleviate the negative curvature and bring the gain of topology. Finally, the loss function consists of the cross-entropy loss for labeled data and the consistency regularization for unlabeled data. In order to effectively fuse the prediction results of various DA strategies, the sharpening is used. Existing experiments on public datasets, i.e., ACM, DBLP, OGB, and industry dataset MB show that HG-MDA outperforms current SOTA models. Additionly, HG-MDA is applied to user identification in internet finance scenarios, helping the business to add 30% key users, and increase loans and balances by 3.6%, 11.1%, and 9.8%.

因果关系的概念在人类认知中起着重要作用。在过去的几十年中，在许多领域（例如计算机科学，医学，经济学和教育）中，因果推论已经得到很好的发展。随着深度学习技术的发展，它越来越多地用于针对反事实数据的因果推断。通常，深层因果模型将协变量的特征映射到表示空间，然后设计各种客观优化函数，以根据不同的优化方法公正地估算反事实数据。本文重点介绍了深层因果模型的调查，其核心贡献如下：1）我们在多种疗法和连续剂量治疗下提供相关指标； 2）我们从时间开发和方法分类的角度综合了深层因果模型的全面概述； 3）我们协助有关相关数据集和源代码的详细且全面的分类和分析。

人类识别是事件检测，人跟踪和公共安全的重要课题。有许多方法提出了人类识别，例如面部识别，人重新识别和步态识别。通常，现有方法主要将查询图像分类为图像库集合（I2i）中的特定标识。这对场景非常有限，其中仅在广泛的视频监控应用程序（A2i或I2a）中提供了查询或属性库集合的文本描述。然而，非常少量的努力已经致力于无模式识别，即，以可扩展的方式识别在库中设置的查询。在这项工作中，我们采取初步尝试，并以可扩展的方式制定这样一种新的无模式人类识别（命名为MFHI）任务作为通用零射击学习模型。同时，它能够通过学习每个身份的鉴别性原型来弥合视觉和语义模态。此外，在视觉模型上强制执行语义引导的空间注意，以获得具有高全局类别级和本地属性级别辨别的表示。最后，我们在两个共同挑战的识别任务中设计和开展广泛的实验，包括面部识别和人员重新识别，证明我们的方法优于一种在无模式人体识别方面的各种最先进的方法。

图像学习和着色是多媒体域中的热点。受到人类的学习能力的启发，在本文中，我们提出了一种具有学习框架的自动着色方法。该方法可以看作是基于典范和基于学习的方法的混合体，并且可以将着色过程和学习过程分解，从而为相同的灰色图像生成各种颜色样式。基于示例的着色方法中的匹配过程可以被视为参数化函数，我们采用大量颜色图像作为训练样本来适合参数。在训练过程中，颜色图像是地面真相，我们通过最小化匹配函数的参数来了解匹配过程的最佳参数。为了处理具有各种组合的图像，引入了全局功能，该功能可用于将图像相对于它们的组成分类，然后分别学习每个图像类别的最佳匹配参数。更重要的是，基于空间一致性的后处理是设计从参考图像中平滑提取的颜色信息以删除匹配错误。进行了广泛的实验以验证该方法的有效性，并与最新的着色算法达到了可比的性能。

在复杂的动态环境中，有效的轨迹产生在无人体表面车辆（USV）域中仍然是一个开放的问题。在本文中，提出了针对USV-UAV系统的合作轨迹计划算法，以确保USV可以在多障碍物图中的自主进步过程中执行安全，平稳的路径。具体而言，无人机（UAV）扮演飞行传感器的角色，并提供了实时的全球地图和障碍信息，并具有轻巧的语义细分网络和3D投影转换。然后通过基于图的搜索方法生成初始的避免轨迹。关于USV的独特运动不足的运动学特性，引入了基于船体动态约束的数值优化方法，以使该轨迹易于跟踪进行运动控制。最后，提出了基于在执行过程中具有最低能量消耗限制的NMPC的运动控制方法。实验结果验证了整个系统的有效性，并且生成的轨迹在局部对USV始终具有相当大的跟踪精度。

无人的表面容器（USV）广泛用于海洋勘探和环境保护场。为了确保USV能够成功执行其任务，轨迹计划和运动跟踪是两种最关键的技术。在本文中，我们根据优化理论提出了一种新型的USV轨迹生成和跟踪方法。具体而言，USV动力学模型以差异平坦度进行描述，因此在最佳边界值的目标下，在线性不变系统表达式中可以通过动态RRT*生成轨迹。为了降低样本数并提高效率，我们通过局部优化调整轨迹。在优化过程中考虑了动态约束，因此生成的轨迹符合未散发船体的运动学特征，并使其更容易跟踪。最后，在顺序二次编程问题下使用模型预测控制添加运动跟踪。实验结果表明，计划的轨迹与USV的运动学特性更加一致，并且跟踪精度仍然更高。

药物建议是智能医疗系统的关键任务。先前的研究主要建议使用电子健康记录（EHRS）药物。但是，在EHR中可能会忽略或忽略医生与患者之间的相互作用的一些细节，这对于自动药物建议至关重要。因此，我们首次尝试通过医生和患者之间的对话推荐药物。在这项工作中，我们构建了Dialmed，这是第一个用于基于医学对话的药物建议任务的高质量数据集。它包含与3个部门的16种常见疾病和70种相应常见药物有关的11,996次医疗对话。此外，我们提出了对话结构和疾病知识意识网络（DDN），其中QA对话图机制旨在模拟对话结构，并使用知识图来引入外部疾病知识。广泛的实验结果表明，所提出的方法是推荐与医疗对话的药物的有前途的解决方案。该数据集和代码可在https://github.com/f-window/dialmed上找到。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

To generate high quality rendering images for real time applications, it is often to trace only a few samples-per-pixel (spp) at a lower resolution and then supersample to the high resolution. Based on the observation that the rendered pixels at a low resolution are typically highly aliased, we present a novel method for neural supersampling based on ray tracing 1/4-spp samples at the high resolution. Our key insight is that the ray-traced samples at the target resolution are accurate and reliable, which makes the supersampling an interpolation problem. We present a mask-reinforced neural network to reconstruct and interpolate high-quality image sequences. First, a novel temporal accumulation network is introduced to compute the correlation between current and previous features to significantly improve their temporal stability. Then a reconstruct network based on a multi-scale U-Net with skip connections is adopted for reconstruction and generation of the desired high-resolution image. Experimental results and comparisons have shown that our proposed method can generate higher quality results of supersampling, without increasing the total number of ray-tracing samples, over current state-of-the-art methods.