The cone-beam computed tomography (CBCT) provides 3D volumetric imaging of a target with low radiation dose and cost compared with conventional computed tomography, and it is widely used in the detection of paranasal sinus disease. However, it lacks the sensitivity to detect soft tissue lesions owing to reconstruction constraints. Consequently, only physicians with expertise in CBCT reading can distinguish between inherent artifacts or noise and diseases, restricting the use of this imaging modality. The development of artificial intelligence (AI)-based computer-aided diagnosis methods for CBCT to overcome the shortage of experienced physicians has attracted substantial attention. However, advanced AI-based diagnosis addressing intrinsic noise in CBCT has not been devised, discouraging the practical use of AI solutions for CBCT. To address this issue, we propose an AI-based computer-aided diagnosis method using CBCT with a denoising module. This module is implemented before diagnosis to reconstruct the internal ground-truth full-dose scan corresponding to an input CBCT image and thereby improve the diagnostic performance. The external validation results for the unified diagnosis of sinus fungal ball, chronic rhinosinusitis, and normal cases show that the proposed method improves the micro-, macro-average AUC, and accuracy by 7.4, 5.6, and 9.6% (from 86.2, 87.0, and 73.4 to 93.6, 92.6, and 83.0%), respectively, compared with a baseline while improving human diagnosis accuracy by 11% (from 71.7 to 83.0%), demonstrating technical differentiation and clinical effectiveness. This pioneering study on AI-based diagnosis using CBCT indicates denoising can improve diagnostic performance and reader interpretability in images from the sinonasal area, thereby providing a new approach and direction to radiographic image reconstruction regarding the development of AI-based diagnostic solutions.

社交媒体帖子包含有关医疗条件和与健康相关行为的潜在有价值的信息。生物重建VII任务3专注于通过识别推文中的药物和膳食补充剂的提及来挖掘这些信息。我们通过精细调整多个BERT样式语言模型来执行此任务以执行令牌级分类，并将它们组合成集合以生成最终预测。我们最好的系统由五个Megatron-Bert-345M型号组成，在看不见的测试数据上实现了0.764的严格F1得分。

生物重建VII Track-2挑战包括命名实体识别，实体链接（或实体 - 归一化），主题索引任务 - 与实体和主题限制为这项挑战的化学品。命名实体识别是一个完善的问题，我们通过基于Bert的生物群体模型实现了我们的最佳性能。我们将基于BERT的方法扩展到实体链接任务。在预先预订Biobert的第二阶段，通过称为自对准预先训练（SAP）的度量学习损失策略，我们将基于其SAP-Biobert Word Embeddings之间的余弦相似性链接实体。尽管我们的命名实体识别实验取得了成功，但我们发现化学指数任务一般更具挑战性。除了传统的NER方法之外，我们还尝试使用基于新颖的文本或“提示”方法的命名实体识别和实体链接，该方法使用生成语言模型，例如T5和GPT。我们通过这种新方法实现了令人鼓舞的结果。

在Bircocrive VII的Track-1中，要求参与者识别药物/化学品和蛋白质之间的相互作用。提供每个药物/化学和蛋白质的内部名称实体注释，必须自动预测14个不同的相互作用中的一种。对于此关系提取任务，我们尝试两种基于BERT的句子分类方法，以及使用T5模型的更新文本到文本方法。我们发现基于BERT的模型一般表现更好，我们的生物综太基模型实现了所有指标的最高分，实现了0.74 F1得分。虽然我们的小说T5文本到文本方法没有表现出基于BERT的大多数模型，但它表现出在类似数据上培训的那些，呈现出有希望的结果，实现0.65 F1得分。我们认为，与关系提取的文本文本方法有一些竞争优势，并且有很多研究进步的空间。

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.