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.

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining methods have two problems as follows. 1) In the hardness measurement, the important structural information is overlooked for similarity calculation, degrading the representativeness of the selected hard negative samples. 2) Previous works merely focus on the hard negative sample pairs while neglecting the hard positive sample pairs. Nevertheless, samples within the same cluster but with low similarity should also be carefully learned. To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample, thus improving the discriminative capability of the samples further. Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method.

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering and recommendation systems, etc. According to the graph types, the existing KGR models can be roughly divided into three categories, \textit{i.e.,} static models, temporal models, and multi-modal models. The early works in this domain mainly focus on static KGR and tend to directly apply general knowledge graph embedding models to the reasoning task. However, these models are not suitable for more complex but practical tasks, such as inductive static KGR, temporal KGR, and multi-modal KGR. To this end, multiple works have been developed recently, but no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a survey for knowledge graph reasoning tracing from static to temporal and then to multi-modal KGs. Concretely, the preliminaries, summaries of KGR models, and typical datasets are introduced and discussed consequently. Moreover, we discuss the challenges and potential opportunities. The corresponding open-source repository is shared on GitHub: https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning.

Zero-sum Markov Games (MGs) has been an efficient framework for multi-agent systems and robust control, wherein a minimax problem is constructed to solve the equilibrium policies. At present, this formulation is well studied under tabular settings wherein the maximum operator is primarily and exactly solved to calculate the worst-case value function. However, it is non-trivial to extend such methods to handle complex tasks, as finding the maximum over large-scale action spaces is usually cumbersome. In this paper, we propose the smoothing policy iteration (SPI) algorithm to solve the zero-sum MGs approximately, where the maximum operator is replaced by the weighted LogSumExp (WLSE) function to obtain the nearly optimal equilibrium policies. Specially, the adversarial policy is served as the weight function to enable an efficient sampling over action spaces.We also prove the convergence of SPI and analyze its approximation error in $\infty -$norm based on the contraction mapping theorem. Besides, we propose a model-based algorithm called Smooth adversarial Actor-critic (SaAC) by extending SPI with the function approximations. The target value related to WLSE function is evaluated by the sampled trajectories and then mean square error is constructed to optimize the value function, and the gradient-ascent-descent methods are adopted to optimize the protagonist and adversarial policies jointly. In addition, we incorporate the reparameterization technique in model-based gradient back-propagation to prevent the gradient vanishing due to sampling from the stochastic policies. We verify our algorithm in both tabular and function approximation settings. Results show that SPI can approximate the worst-case value function with a high accuracy and SaAC can stabilize the training process and improve the adversarial robustness in a large margin.

关于无监督的域适应性（UDA）的广泛研究已将有限的实验数据集深入学习到现实世界中无约束的领域。大多数UDA接近通用嵌入空间中的对齐功能，并将共享分类器应用于目标预测。但是，由于当域差异很大时可能不存在完全排列的特征空间，因此这些方法受到了两个局限性。首先，由于缺乏目标标签监督，强制域的比对会恶化目标域的可区分性。其次，源监督分类器不可避免地偏向源数据，因此它在目标域中的表现可能不佳。为了减轻这些问题，我们建议在两个集中在不同领域的空间中同时进行特征对齐，并为每个空间创建一个针对该域的面向域的分类器。具体而言，我们设计了一个面向域的变压器（DOT），该变压器（DOT）具有两个单独的分类令牌，以学习不同的面向域的表示形式和两个分类器，以保持域的可区分性。理论保证的基于对比度的对齐和源指导的伪标签细化策略被用来探索域名和特定信息。全面的实验验证了我们的方法在几个基准上实现了最先进的方法。

上下文信息对于各种计算机视觉任务至关重要，以前的作品通常设计插件模块和结构损失，以有效地提取和汇总全局上下文。这些方法利用优质标签来优化模型，但忽略了精细训练的特征也是宝贵的训练资源，可以将优选的分布引入硬像素（即错误分类的像素）。受到无监督范式的对比学习的启发，我们以监督的方式应用了对比度损失，并重新设计了损失功能，以抛弃无监督学习的刻板印象（例如，积极和负面的不平衡，对锚定计算的混淆）。为此，我们提出了阳性阴性相等的对比损失（PNE损失），这增加了阳性嵌入对锚的潜在影响，并同时对待阳性和阴性样本对。 PNE损失可以直接插入现有的语义细分框架中，并以可忽视的额外计算成本导致出色的性能。我们利用许多经典的分割方法（例如，DeepLabv3，Ocrnet，Upernet）和骨干（例如Resnet，Hrnet，Swin Transformer）进行全面的实验，并在两个基准数据集（例如，例如，例如，，例如城市景观和可可固定）。我们的代码将公开

对于3D医学图像（例如CT和MRI）分割，在临床情况下分割每个切片的难度差异很大。先前以逐片方式进行体积医学图像分割的研究通常使用相同的2D深神经网络来细分同一情况的所有切片，从而忽略了图像切片之间的数据异质性。在本文中，我们专注于多模式3D MRI脑肿瘤分割，并根据自适应模型选择提出了一个名为MED-DANET的动态体系结构网络，以实现有效的准确性和效率折衷。对于输入3D MRI量的每个切片，我们提出的方法学习了决策网络的特定于切片的决策，以动态从预定义的模型库中选择合适的模型，以完成后续的2D分割任务。 Brats 2019和2020年数据集的广泛实验结果表明，我们提出的方法比以前的3D MRI脑肿瘤分割的最先进方法获得了可比或更好的结果，模型的复杂性要少得多。与最新的3D方法TransBT相比，提出的框架提高了模型效率高达3.5倍，而无需牺牲准确性。我们的代码将很快公开可用。

本文介绍了Yidun Nisp团队向视频关键字唤醒挑战提交的系统。我们提出了一个普通话关键字发现系统（KWS），具有几种新颖且有效的改进，包括大骨干（B）模型，一个关键字偏置（B）机制和版本建模单元的引入。通过考虑一下，我们将总系统BBS-KWS作为缩写。 BBS-KWS系统由端到端的自动语音识别（ASR）模块和KWS模块组成。 ASR模块将语音特征转换为文本表示，文本表示将大骨干网络应用于声学模型，并考虑了音节建模单元。另外，关键字偏置机制用于改善ASR推断阶段中的关键字的召回率。 KWS模块应用多个标准，以确定关键字的缺席或存在，例如多级匹配，模糊匹配和连接主义时间分类（CTC）前缀分数。为了进一步改进我们的系统，我们对CN-Celeb数据集进行半监督学习，以获得更好的概括。在VKW任务中，BBS-KWS系统实现了基线的显着收益，并在两条轨道中获得了第一名。

作为解决复杂优化问题的有效算法，人造蜜蜂菌落（ABC）算法表明竞争，但与其他基于人口的算法相同，它难以平衡整个解决方案空间中全球搜索的能力（命名作为探索）和快速搜索定义为剥削的本地解决方案空间。为了提高ABC的性能，引入了自适应组协作ABC（AGABC）算法，其中不同阶段的群体划分为特定的组，并且分配给成员的不同能力的不同搜索策略，以及成员或策略获得最佳解决方案将采用进一步搜索。基准函数的实验结果表明，具有动态机制的提议算法优于其他搜索精度和稳定性的算法。此外，数值实验表明，该方法可以为复杂调度问题产生最佳解决方案。

无监督的域适应（UDA）旨在将知识从标记的源域传输到未标记的目标域。大多数现有的UDA方法通过学习域 - 不变的表示和在两个域中共享一个分类器来实现知识传输。但是，忽略与任务相关的域特定信息，并强制统一的分类器以适合两个域将限制每个域中的特征表达性。在本文中，通过观察到具有可比参数的变压器架构可以产生比CNN对应的更可转换的表示，我们提出了一个双赢的变压器框架（WINTR），它分别探讨了每个域的特定于域的知识，而同时交互式跨域知识。具体而言，我们使用变压器中的两个单独的分类令牌学习两个不同的映射，以及每个特定于域的分类器的设计。跨域知识通过源引导标签改进和与源或目标的单侧特征对齐传输，这保持了特定于域的信息的完整性。三个基准数据集的广泛实验表明，我们的方法优于最先进的UDA方法，验证利用域特定和不变性的有效性