知识图的归纳链路预测旨在预测未见实体之间的缺失联系，而那些未在训练阶段显示的实体。大多数以前的作品都学习实体的特定实体嵌入，这些实体无法处理看不见的实体。最近的几种方法利用封闭子图来获得归纳能力。但是，所有这些作品仅在没有完整的邻近关系的情况下考虑子图的封闭部分，这导致了忽略部分邻近关系的问题，并且很难处理稀疏的子图。为了解决这个问题，我们提出了SNRI子图邻近关系Infomax，它足够从两个方面利用完整的相邻关系：节点特征的相邻关系特征和稀疏子图的相邻关系路径。为了进一步以全球方式建模邻近关系，我们对知识图进行创新的相互信息（MI）最大化。实验表明，SNRI在归纳链路预测任务上的大幅度优于现有的最新方法，并验证以全局方式探索完整的邻近关系的有效性，以表征节点特征和在稀疏子分类上的理由。

通过仅使用训练有素的分类器，模型内（MI）攻击可以恢复用于训练分类器的数据，从而导致培训数据的隐私泄漏。为了防止MI攻击，先前的工作利用单方面依赖优化策略，即，在培训分类器期间，最大程度地减少了输入（即功能）和输出（即标签）之间的依赖关系。但是，这样的最小化过程与最小化监督损失相冲突，该损失旨在最大程度地提高输入和输出之间的依赖关系，从而在模型鲁棒性针对MI攻击和模型实用程序上对分类任务进行明确的权衡。在本文中，我们旨在最大程度地减少潜在表示和输入之间的依赖性，同时最大化潜在表示和输出之间的依赖关系，称为双边依赖性优化（BIDO）策略。特别是，除了对深神经网络的常用损失（例如，跨渗透性）外，我们还将依赖性约束用作普遍适用的正常化程序，可以根据不同的任务将其实例化使用适当的依赖标准。为了验证我们策略的功效，我们通过使用两种不同的依赖性度量提出了两种BIDO的实施：具有约束协方差的Bido（Bido-Coco）（Bido-Coco）和Bido具有Hilbert-Schmidt独立标准（Bido-HSIC）。实验表明，比多（Bido防御MI攻击的道路。

在本文中，我们发现两个因素抑制POMS从实现高感感性质量：1）方向优化（COO）问题和2）模型的低频趋势。首先，POMS倾向于生成SR图像，其位置空间中的位置最接近所有潜在的高分辨率（HR）图像的分配中心，导致这种POMS失去高频细节。其次，图像的90美元\％$区域由低频信号组成;相比之下，人类感知依赖于图像的高频细节。然而，POMS应用相同的计算来处理不同频率区域，使POM倾向于恢复低频区域。基于这两个因素，我们提出了一种细节，通过组合高频增强模块和空间对比学习模块来降低COO问题的影响和低频趋势来提高对比损失（DECHROSTS）。实验结果表明，在若干常规SR模型上施加DROCKS时的效率和有效性。例如，在EDSR中，与基于GAN的方法相比，我们所提出的方法与视觉质量微妙降级的基于GAN的方法实现了3.60美元。此外，我们的最终结果表明，与最先进的方法相比，配备了我们的DECHROSS的SR网络更具现实和视觉上令人愉悦的纹理。 ％拟议方法的源代码包含在补充材料中，并将在将来公开。

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering. Motivated by the fact that informative point cloud features should be able to encode rich geometry and appearance cues and render realistic images, we train a point-cloud encoder within a devised point-based neural renderer by comparing the rendered images with real images on massive RGB-D data. The learned point-cloud encoder can be easily integrated into various downstream tasks, including not only high-level tasks like 3D detection and segmentation, but low-level tasks like 3D reconstruction and image synthesis. Extensive experiments on various tasks demonstrate the superiority of our approach compared to existing pre-training methods.

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which require low delay and high accuracy. Sampling rate adaption which dynamically configures the sampling rates of industrial IoT devices according to network conditions, is the key in minimizing the service delay. In this paper, we investigate the collaborative DNN inference problem in industrial IoT networks. To capture the channel variation and task arrival randomness, we formulate the problem as a constrained Markov decision process (CMDP). Specifically, sampling rate adaption, inference task offloading and edge computing resource allocation are jointly considered to minimize the average service delay while guaranteeing the long-term accuracy requirements of different inference services. Since CMDP cannot be directly solved by general reinforcement learning (RL) algorithms due to the intractable long-term constraints, we first transform the CMDP into an MDP by leveraging the Lyapunov optimization technique. Then, a deep RL-based algorithm is proposed to solve the MDP. To expedite the training process, an optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal edge computing resource allocation. Extensive simulation results are provided to demonstrate that the proposed RL-based algorithm can significantly reduce the average service delay while preserving long-term inference accuracy with a high probability.

The traditional statistical inference is static, in the sense that the estimate of the quantity of interest does not affect the future evolution of the quantity. In some sequential estimation problems however, the future values of the quantity to be estimated depend on the estimate of its current value. This type of estimation problems has been formulated as the dynamic inference problem. In this work, we formulate the Bayesian learning problem for dynamic inference, where the unknown quantity-generation model is assumed to be randomly drawn according to a random model parameter. We derive the optimal Bayesian learning rules, both offline and online, to minimize the inference loss. Moreover, learning for dynamic inference can serve as a meta problem, such that all familiar machine learning problems, including supervised learning, imitation learning and reinforcement learning, can be cast as its special cases or variants. Gaining a good understanding of this unifying meta problem thus sheds light on a broad spectrum of machine learning problems as well.

Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world graphs are always incomplete, noisy, or redundant. How to reveal the inherent graph structure in a unified way remains under-explored. We proposed PRI-GSL, a Graph Structure Learning framework guided by the Principle of Relevant Information, providing a simple and unified framework for identifying the self-organization and revealing the hidden structure. PRI-GSL learns a structure that contains the most relevant yet least redundant information quantified by von Neumann entropy and Quantum Jensen-Shannon divergence. PRI-GSL incorporates the evolution of quantum continuous walk with graph wavelets to encode node structural roles, showing in which way the nodes interplay and self-organize with the graph structure. Extensive experiments demonstrate the superior effectiveness and robustness of PRI-GSL.