在基于学习的语义沟通中，神经网络在传统通信系统中取代了不同的构件。但是，数字调制仍然是神经网络的挑战。基于神经网络的数字调制的固有机制是将神经网络编码器的连续输出映射到离散的星座符号中，这是一个不可差的函数，无法使用现有的梯度下降算法进行训练。为了克服这一挑战，在本文中，我们为使用BPSK调制的数字语义通信制定了联合编码调节方案。在我们的方法中，神经网络输出了每个星座点的可能性，而不是具有混凝土映射。因此，使用了随机代码而不是确定性代码，该代码在每个星座上都有可能的符号保留更多信息。联合编码调制设计可以与频道状态相匹配，从而提高数字语义通信的性能。实验结果表明，我们的方法在广泛的SNR上优于语义通信中现有的数字调制方法，并且低SNR制度中的基于神经网络的模拟调制方法优于基于神经网络的模拟调制方法。

本文提出了一个4D主链，以供长期点云视频理解。捕获空间上下文的一种典型方法是使用无层次结构的4DCONV或变压器。但是，由于相机运动，场景变化，采样模式和4D数据的复杂性，这些方法既没有有效也没有高效的效率。为了解决这些问题，我们利用原始平面作为中层表示，以捕获4D点云视频中的长期空间上下文，并提出了一个名为Point Point Primitive Transformer（PPTR）的新型层次骨架，主要由该骨架组成，该骨架主要由主要的点变压器和原始变压器。广泛的实验表明，PPTR在不同任务上优于先前的艺术状态

我们展示了MVLayoutNet，是来自多视图全景的整体三维重建端到端网络。我们的核心贡献是无缝地将学习的单目布局估计和多视图立体声（MV）结合起来，以便在3D和图像空间中准确地重建。我们共同列出布局模块以产生初始布局和新型MVS模块，以获得精确的布局几何形状。与标准MVSNET [33]不同，我们的MVS模块采用新建的布局成本卷，其在相同的深度层中聚合到相应的布局元件中的多视图成本。我们还提供了一种基于注意的方案，指导MVS模块专注于结构区域。这种设计考虑了本地像素级成本和全球整体信息，以便更好地重建。实验表明，我们的方法在2D-3D-S [1]和Zind [5]数据集中，在深度RMSE方面以21.7％和20.6％表示最先进的。最后，我们的方法导致连贯的布局几何，使整个场景的重建能够。

Covid-19上的知识图（KGS）已建立在加速Covid-19的研究过程中。然而，KGs总是不完整，特别是新建造的Covid-19公斤。链路预测任务旨在预测（e，r，t）或（h，r，e）的丢失实体，其中H和t是某些实体，E是需要预测的实体，R是关系。这项任务还有可能解决Covid-19相关的KGS的不完全问题。虽然已经提出了各种知识图形嵌入（KGE）方法的链路预测任务，但这些现有方法遭受了使用单个评分函数的限制，这不能捕获Covid-19 Kgs的丰富特征。在这项工作中，我们提出了利用多个评分函数来提取来自现有三元组的更多特征的MDistmult模型。我们在CCKS2020 Covid-19抗病毒药物知识图（CADKG）上采用实验。实验结果表明，我们的MDistmult在CADKG数据集上的链路预测任务中实现了最先进的性能

The growing interest in intelligent services and privacy protection for mobile devices has given rise to the widespread application of federated learning in Multi-access Edge Computing (MEC). Diverse user behaviors call for personalized services with heterogeneous Machine Learning (ML) models on different devices. Federated Multi-task Learning (FMTL) is proposed to train related but personalized ML models for different devices, whereas previous works suffer from excessive communication overhead during training and neglect the model heterogeneity among devices in MEC. Introducing knowledge distillation into FMTL can simultaneously enable efficient communication and model heterogeneity among clients, whereas existing methods rely on a public dataset, which is impractical in reality. To tackle this dilemma, Federated MultI-task Distillation for Multi-access Edge CompuTing (FedICT) is proposed. FedICT direct local-global knowledge aloof during bi-directional distillation processes between clients and the server, aiming to enable multi-task clients while alleviating client drift derived from divergent optimization directions of client-side local models. Specifically, FedICT includes Federated Prior Knowledge Distillation (FPKD) and Local Knowledge Adjustment (LKA). FPKD is proposed to reinforce the clients' fitting of local data by introducing prior knowledge of local data distributions. Moreover, LKA is proposed to correct the distillation loss of the server, making the transferred local knowledge better match the generalized representation. Experiments on three datasets show that FedICT significantly outperforms all compared benchmarks in various data heterogeneous and model architecture settings, achieving improved accuracy with less than 1.2% training communication overhead compared with FedAvg and no more than 75% training communication round compared with FedGKT.

Most existing text-video retrieval methods focus on cross-modal matching between the visual content of offline videos and textual query sentences. However, in real scenarios, online videos are frequently accompanied by relevant text information such as titles, tags, and even subtitles, which can be utilized to match textual queries. This inspires us to generate associated captions from offline videos to help with existing text-video retrieval methods. To do so, we propose to use the zero-shot video captioner with knowledge of pre-trained web-scale models (e.g., CLIP and GPT-2) to generate captions for offline videos without any training. Given the captions, one question naturally arises: what can auxiliary captions do for text-video retrieval? In this paper, we present a novel framework Cap4Video, which makes use of captions from three aspects: i) Input data: The video and captions can form new video-caption pairs as data augmentation for training. ii) Feature interaction: We perform feature interaction between video and caption to yield enhanced video representations. iii) Output score: The Query-Caption matching branch can be complementary to the original Query-Video matching branch for text-video retrieval. We conduct thorough ablation studies to demonstrate the effectiveness of our method. Without any post-processing, our Cap4Video achieves state-of-the-art performance on MSR-VTT (51.4%), VATEX (66.6%), MSVD (51.8%), and DiDeMo (52.0%).

While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed. This called for a high-performance adaptive bitrate (ABR) algorithm that can maximize the user quality of experience given 5G network characteristics and data rate of UHD contents. Recently, many of the newly proposed ABR techniques were machine-learning based. Among that, Pensieve is one of the state-of-the-art techniques, which utilized reinforcement-learning to generate an ABR algorithm based on observation of past decision performance. By incorporating the context of the 5G network and UHD content, Pensieve has been optimized into Pensieve 5G. New QoE metrics that more accurately represent the QoE of UHD video streaming on the different types of devices were proposed and used to evaluate Pensieve 5G against other ABR techniques including the original Pensieve. The results from the simulation based on the real 5G Standalone (SA) network throughput shows that Pensieve 5G outperforms both conventional algorithms and Pensieve with the average QoE improvement of 8.8% and 14.2%, respectively. Additionally, Pensieve 5G also performed well on the commercial 5G NR-NR Dual Connectivity (NR-DC) Network, despite the training being done solely using the data from the 5G Standalone (SA) network.

The typical way for relation extraction is fine-tuning large pre-trained language models on task-specific datasets, then selecting the label with the highest probability of the output distribution as the final prediction. However, the usage of the Top-k prediction set for a given sample is commonly overlooked. In this paper, we first reveal that the Top-k prediction set of a given sample contains useful information for predicting the correct label. To effectively utilizes the Top-k prediction set, we propose Label Graph Network with Top-k Prediction Set, termed as KLG. Specifically, for a given sample, we build a label graph to review candidate labels in the Top-k prediction set and learn the connections between them. We also design a dynamic $k$-selection mechanism to learn more powerful and discriminative relation representation. Our experiments show that KLG achieves the best performances on three relation extraction datasets. Moreover, we observe that KLG is more effective in dealing with long-tailed classes.

Sequence generation demonstrates promising performance in recent information extraction efforts, by incorporating large-scale pre-trained Seq2Seq models. This paper investigates the merits of employing sequence generation in relation extraction, finding that with relation names or synonyms as generation targets, their textual semantics and the correlation (in terms of word sequence pattern) among them affect model performance. We then propose Relation Extraction with Label Augmentation (RELA), a Seq2Seq model with automatic label augmentation for RE. By saying label augmentation, we mean prod semantically synonyms for each relation name as the generation target. Besides, we present an in-depth analysis of the Seq2Seq model's behavior when dealing with RE. Experimental results show that RELA achieves competitive results compared with previous methods on four RE datasets.

Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are vulnerable to adversarial attacks which aim to mislead the node or subgraph classification prediction by adding subtle perturbations. Detecting these attacks is challenging due to the small magnitude of perturbation and the discrete nature of graph data. In this paper, we propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation. Specifically, we propose a novel graph generation approach combined with link prediction to detect suspicious adversarial edges. To effectively train the graph generative model, we sample several sub-graphs from the given graph data. We show that since the number of adversarial edges is usually low in practice, with low probability the sampled sub-graphs will contain adversarial edges based on the union bound. In addition, considering the strong attacks which perturb a large number of edges, we propose a set of novel features to perform outlier detection as the preprocessing for our detection. Extensive experimental results on three real-world graph datasets including a private transaction rule dataset from a major company and two types of synthetic graphs with controlled properties show that EDoG can achieve above 0.8 AUC against four state-of-the-art unseen attack strategies without requiring any knowledge about the attack type; and around 0.85 with knowledge of the attack type. EDoG significantly outperforms traditional malicious edge detection baselines. We also show that an adaptive attack with full knowledge of our detection pipeline is difficult to bypass it.