心血管疾病（CVD）是全球死亡的第一大原因。尽管有越来越多的证据表明心房颤动（AF）与各种CVD有着密切的关联，但这种心律不齐通常是使用心电图（ECG）诊断的，这是一种无风险，无侵入性和具有成本效益的工具。在任何威胁生命的疾病/疾病发展之前，不断和远程监视受试者的心电图信息迅速诊断和及时对AF进行预处理的潜力。最终，可以降低CVD相关的死亡率。在此手稿中，展示了体现可穿戴心电图设备，移动应用程序和后端服务器的个性化医疗系统的设计和实施。该系统不断监视用户的心电图信息，以提供个性化的健康警告/反馈。用户能够通过该系统与他们的配对健康顾问进行远程诊断，干预措施等。已经评估了实施的可穿戴ECG设备，并显示出极好的一致性（CVRMS = 5.5％），可接受的一致性（CVRMS = CVRMS = CVRMS = 12.1％），可忽略不计的RR间隙错误（<1.4％）。为了提高可穿戴设备的电池寿命，提出了使用ECG信号的准周期特征来实现压缩的有损压缩模式。与公认的架构相比，它在压缩效率和失真方面优于其他模式，并在MIT-BIH数据库中以ECG信号的某个PRD或RMSE达到了至少2倍的Cr。为了在拟议系统中实现自动化AF诊断/筛查，开发了基于重新系统的AF检测器。对于2017年Physionet CINC挑战的ECG记录，该AF探测器获得了平均测试F1 = 85.10％和最佳测试F1 = 87.31％，表现优于最先进。

状态制备是在量子物理学中的基本重要性，这可以通过将量子电路构造为整体来实现，该单一地将初始状态转换为目标，或者实现量子控制协议以设计的汉密尔顿人发展到目标状态。在这项工作中，我们通过用固定耦合和变分磁场的时间演变来研究后者对量子的数量。具体而言，我们考虑准备汉密尔顿人的地面州，其中包含汉密尔顿人的某些互动的互动，以时间进化。提出了一种优化方法来通过“微粒”的离散化来优化磁场，以获得高精度和稳定性。利用反向传播技术来获得违反对数保真度的字段的梯度。我们的方法在准备Heisenberg链的地面状态与XY和Ising互动的时间演变进行了准备，其性能超过了两种使用本地和全球优化策略的基线方法。我们的工作可以应用和推广到其他量子型号，例如在高维格子上定义的型号。它启示以降低所需交互的复杂性，以通过优化磁场实现量子信息和计算中的量子信息和其他任务。

典型的多源域适应性（MSDA）方法旨在将知识从一组标记的源域中学习的知识转移到一个未标记的目标域。然而，先前的工作严格假设每个源域都与目标域共享相同的类别类别，因为目标标签空间无法观察到，这几乎无法保证。在本文中，我们考虑了MSDA的更广泛的设置，即广义的多源域适应性，其中源域部分重叠，并且允许目标域包含任何源域中未呈现的新型类别。由于域的共存和类别跨源域和目标域的转移，因此这种新设置比任何现有的域适应协议都难以捉摸。为了解决这个问题，我们提出了一个变分域分解（VDD）框架，该框架通过鼓励尺寸独立性来分解每个实例的域表示和语义特征。为了识别未知类别的目标样本，我们利用在线伪标签，该标签将伪标签分配给基于置信分数的未标记目标数据。在两个基准数据集上进行的定量和定性实验证明了拟议框架的有效性。

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.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.