Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performance on a wide range of applications. However, its success usually relies on two assumptions: (i) vast troves of labeled datasets are required for accurate model fitting, and (ii) training and testing data are independent and identically distributed. Its performance on unseen target domains, thus, is not guaranteed, especially when encountering out-of-distribution data at the adaptation stage. The performance drop on data in a target domain is a critical problem in deploying deep neural networks that are successfully trained on data in a source domain. Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc. In this review, as a rapidly evolving topic, we provide a systematic comparison of its methods and applications. In addition, the connection of UDA with its closely related tasks, e.g., domain generalization and out-of-distribution detection, has also been discussed. Furthermore, deficiencies in current methods and possible promising directions are highlighted.

Background: Stroke is a pressing public health issue worldwide. This study aimed to forecast the future temporal trends and distribution characteristics of the global stroke burden. Methods and Results: Global data on stroke incidence, prevalence, deaths, and disability‐adjusted life years between 1990 and 2021 were obtained from the GBD (Global Burden of Disease) Study 2021. The temporal trends of stroke burden were estimated using various regression models. Gross domestic product per capita was adopted as a predictor in the model to consider the impact of economic development on stroke burden. Projections of stroke burden up to 2050 were generated using the optimal model selected based on the Akaike information criterion, encompassing global, World Bank income levels, national levels, and sex‐age groups. In 2050, we projected 21.43 million stroke cases, 159.31 million survivors, 12.05 million deaths, and 224.86 million disability‐adjusted life years due to stroke globally. From 2021 to 2050, there was a declining trend in the global age‐adjusted stroke rates, with −7% in incidence, −4% in prevalence, −28% in deaths, and −28% in disability‐adjusted life years. Upper‐middle‐income countries were projected to have the most severe stroke burden, followed by lower‐middle‐income countries, low‐income countries, and high‐income countries. The stroke burden in over half of the 204 countries and territories was expected to be alleviated from 2022 to 2050. Men and older women worldwide bear higher burden. Conclusions: Stroke remains a serious global health challenge, especially in low‐income and middle‐income countries. Targeted implementation of prevention and interventions is imperative across diverse demographic groups.

Recently, deep learning based methods have been widely used for wireless communications. In this paper, different from current researches considering full channel state information (F-CSI) feedback, the eigenvector based CSI feedback with deep learning approach is proposed, referred to as EVCsiNet, where the joint eigenvector concatenated from multiple subbands is compressed and recovered at the encoder and decoder, respectively. Simulation results verify the superiority of our schemes over CSI recovery accuracy and feedback overhead compared with conventional methods using codebooks on link-level channel models in different scenarios.