尽管具有明显的区分靶向分布样本的能力，但深度神经网络在检测异常分布数据方面的性能差。为了解决此缺陷，最先进的解决方案选择在离群值的辅助数据集上训练深网。这些辅助离群值的各种培训标准是根据启发式直觉提出的。但是，我们发现这些直观设计的离群训练标准可能会损害分布学习，并最终导致劣等的表现。为此，我们确定了分布不兼容的三个原因：矛盾的梯度，错误的可能性和分布变化。基于我们的新理解，我们通过调整深层模型和损耗函数的顶级设计，提出一种新的分布检测方法。我们的方法通过减少对分布特征的概率特征的干扰来实现分布兼容性。在几个基准上，我们的方法不仅可以实现最新的分布检测性能，而且还提高了分布精度。

文本对象的重新识别（REID）旨在通过文本描述搜索感兴趣的身份的行人图像。由于丰富的模式内变化和明显的模式间差异，这是具有挑战性的。现有作品通常忽略两种方式之间的特征粒度差异，即，视觉特征通常是细粒度的，而文本特征则粗糙，这主要负责大型模式间间隙。在本文中，我们提出了一个基于变形金刚的端到端框架，以学习两种模式的粒度统一表示，称为LGUR。 LGUR框架包含两个模块：基于字典的粒度比对（DGA）模块和基于原型的粒度统一（PGU）模块。在DGA中，为了使两种模式的粒度对齐，我们引入了一个多模式共享词典（MSD）以重建视觉和文本特征。此外，DGA还具有两个重要因素，即跨模式指导和以前景为中心的重建，以促进MSD的优化。在PGU中，我们采用一组共享和可学习的原型作为查询，以提取粒度统一特征空间中这两种方式的多样化和语义对齐特征，从而进一步促进了REID的性能。综合实验表明，我们的LGUR在Cuhk-Pedes和ICFG-Pedes数据集上始终以大幅度的优势优于最先进的东西。代码将在https://github.com/zhiyinshao-h/lgur上发布。

3D点云是捕获真实世界3D对象的重要3D表示。但是，真正扫描的3D点云通常不完整，并且对于恢复下游应用程序的完整点云非常重要。大多数现有点云完成方法使用倒角距离（CD）训练丢失。通过搜索最近的邻居，CD损耗估计两个点云之间的对应关系，该邻居不会捕获所生成的形状上的总点密度分布，因此可能导致非均匀的点云生成。为了解决这个问题，我们提出了一个新的点扩散细化（PDR）范例，用于点云完成。 PDR包括条件生成网络（CGNET）和细化网络（RFNET）。 CGNET使用称为去噪扩散概率模型（DDPM）的条件生成模型，以在部分观察中产生粗略完成。 DDPM在生成的点云和统一的地面真理之间建立一对一的映射，然后优化平均平方误差损耗以实现均匀生成。 RFNET精制CGNet的粗输出，并进一步提高完成点云的质量。此外，我们开发了两个网络的新型双路架构。该体系结构可以（1）有效且有效地从部分观察到的点云提取多级特征以指导完成，并且（2）精确地操纵3D点的空间位置以获得平滑的表面和尖锐的细节。各种基准数据集上的广泛实验结果表明，我们的PDR范例优于以前的最先进的方法，用于点云完成。值得注意的是，在RFNET的帮助下，我们可以在没有太多的性能下降的情况下加速DDPM的迭代生成过程。

传统的域泛化旨在从多个域学习域不变表示，这需要准确的注释。然而，在现实的应用方案中，收集和注释大量数据太麻烦甚至不可行。然而，Web数据提供免费午餐，以便使用丰富的风格信息访问大量未标记的数据，这些数据可以利用增强域泛化能力。在本文中，我们介绍了一个新的任务，称为半监督域泛化，研究如何互动和未标记的域名，并建立两个基准，包括一个网上爬行数据集，它造成了一种新颖的但是逼真的挑战来推动现有技术的限制。为了解决这项任务，简单的解决方案是通过伪标记与域混淆训练一起传播标签到未标记的域的类信息。考虑缩小域间隙可以提高伪标签的质量和进一步推进域不变特征学习的泛化，我们提出了一个循环学习框架，以鼓励标签传播和域泛化之间的积极反馈，有利于桥接标记的不断发展的中间域课程学习方式的未标记域。进行实验以验证我们框架的有效性。值得突出显示的是，Web爬网数据受益于我们的结果中所示的域泛化。我们的代码稍后将提供。

Along with the springing up of semantics-empowered communication (SemCom) researches, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present corresponding ecosystems, including theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content \& channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., reliable and goal-oriented communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate the future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.

Explainability of Graph Neural Networks (GNNs) is critical to various GNN applications but remains an open challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a trade-off between the two. To search for the most necessary and sufficient explanation, the Probability of Necessity and Sufficiency (PNS) can be applied since it can mathematically quantify the necessity and sufficiency of an explanation. Nevertheless, the difficulty of obtaining PNS due to non-monotonicity and the challenge of counterfactual estimation limits its wide use. To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound. Specifically, we employ nearest neighbor matching to generate counterfactual samples for the features, which is different from the random perturbation. In particular, NSEG combines the edges and node features to generate an explanation, where the common edge explanation is a special case of the combined explanation. Empirical study shows that NSEG achieves excellent performance in generating the most necessary and sufficient explanations among a series of state-of-the-art methods.

Most Deep Learning (DL) based Compressed Sensing (DCS) algorithms adopt a single neural network for signal reconstruction, and fail to jointly consider the influences of the sampling operation for reconstruction. In this paper, we propose unified framework, which jointly considers the sampling and reconstruction process for image compressive sensing based on well-designed cascade neural networks. Two sub-networks, which are the sampling sub-network and the reconstruction sub-network, are included in the proposed framework. In the sampling sub-network, an adaptive full connected layer instead of the traditional random matrix is used to mimic the sampling operator. In the reconstruction sub-network, a cascade network combining stacked denoising autoencoder (SDA) and convolutional neural network (CNN) is designed to reconstruct signals. The SDA is used to solve the signal mapping problem and the signals are initially reconstructed. Furthermore, CNN is used to fully recover the structure and texture features of the image to obtain better reconstruction performance. Extensive experiments show that this framework outperforms many other state-of-the-art methods, especially at low sampling rates.

Multi-view graph clustering (MGC) methods are increasingly being studied due to the explosion of multi-view data with graph structural information. The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views. However, existing works have an inherent limitation that they are unable to concurrently utilize the consensus graph information across multiple graphs and the view-specific feature information. To address this issue, we propose Variational Graph Generator for Multi-View Graph Clustering (VGMGC). Specifically, a novel variational graph generator is proposed to extract common information among multiple graphs. This generator infers a reliable variational consensus graph based on a priori assumption over multiple graphs. Then a simple yet effective graph encoder in conjunction with the multi-view clustering objective is presented to learn the desired graph embeddings for clustering, which embeds the inferred view-common graph and view-specific graphs together with features. Finally, theoretical results illustrate the rationality of VGMGC by analyzing the uncertainty of the inferred consensus graph with information bottleneck principle. Extensive experiments demonstrate the superior performance of our VGMGC over SOTAs.

与传统CS方法相比，基于深度学习（DL）的压缩传感（CS）已被应用于图像重建的更好性能。但是，大多数现有的DL方法都利用逐个块测量，每个测量块分别恢复，这引入了重建的有害阻塞效应。此外，这些方法的神经元接受场被设计为每一层的大小相同，这只能收集单尺度的空间信息，并对重建过程产生负面影响。本文提出了一个新的框架，称为CS测量和重建的多尺度扩张卷积神经网络（MSDCNN）。在测量期间，我们直接从训练有素的测量网络中获得所有测量，该测量网络采用了完全卷积结构，并通过输入图像与重建网络共同训练。它不必将其切成块，从而有效地避免了块效应。在重建期间，我们提出了多尺度特征提取（MFE）体系结构，以模仿人类视觉系统以捕获同一功能映射的多尺度特征，从而增强了框架的图像特征提取能力并提高了框架的性能并提高了框架的性能。影像重建。在MFE中，有多个并行卷积通道以获取多尺度特征信息。然后，将多尺度功能信息融合在一起，并以高质量重建原始图像。我们的实验结果表明，根据PSNR和SSIM，该提出的方法对最新方法的性能有利。

信息指标的年龄无法正确描述状态更新的内在语义。在一个智能反映表面上的合作中继通信系统中，我们提出了语义年龄（AOS），用于测量状态更新的语义新鲜度。具体而言，我们专注于从源节点（SN）到目标的状态更新，该状态被称为马尔可夫决策过程（MDP）。 SN的目的是在最大发射功率约束下最大程度地提高AOS和能源消耗的预期满意度。为了寻求最佳的控制政策，我们首先在派利时间差异学习框架下推出了在线深层演员批评（DAC）学习方案。但是，实践实施在线DAC在SN和系统之间无限重复的互动中构成了关键的挑战，这可能是危险的，尤其是在探索过程中。然后，我们提出了一个新颖的离线DAC方案，该方案估算了先前收集的数据集的最佳控制策略，而无需与系统进行任何进一步的交互。数值实验验证了理论结果，并表明我们的离线DAC方案在平均效用方面显着优于在线DAC方案和最具代表性的基线，这表明了对数据集质量的强大鲁棒性。