Unsupervised domain adaptation (UDA) techniques are vital for semantic segmentation in geosciences, effectively utilizing remote sensing imagery across diverse domains. However, most existing UDA methods, which focus on domain alignment at the high-level feature space, struggle to simultaneously retain local spatial details and global contextual semantics. To overcome these challenges, a novel decomposition scheme is proposed to guide domain-invariant representation learning. Specifically, multiscale high/low-frequency decomposition (HLFD) modules are proposed to decompose feature maps into high- and low-frequency components across different subspaces. This decomposition is integrated into a fully global-local generative adversarial network (GLGAN) that incorporates global-local transformer blocks (GLTBs) to enhance the alignment of decomposed features. By integrating the HLFD scheme and the GLGAN, a novel decomposition-based UDA framework called De-GLGAN is developed to improve the cross-domain transferability and generalization capability of semantic segmentation models. Extensive experiments on two UDA benchmarks, namely ISPRS Potsdam and Vaihingen, and LoveDA Rural and Urban, demonstrate the effectiveness and superiority of the proposed approach over existing state-of-the-art UDA methods. The source code for this work is accessible athttps://github.com/sstary/SSRS.

The spread of fake news is a significant social problem that can have disastrous impacts on various domains, such as politics and the economy.Therefore, detecting fake news has become a major concern.However, prior research has relied solely on news text to derive news representation, which is inadequate because different news items under the same publisher are interconnected.To address this limitation, we propose an innovative approach called the Publisher-oriented Multi-view Graph Model (PMGM) that leverages the context awareness of the publisher to detect fake news.Our approach enriches the news representation by incorporating publisher profiles and text style features extracted from the news.Specifically, we construct a multi-view graph that encodes various relationships between news items from the same publisher, such as news topics and occasions in which they were released.Furthermore, we leverage a multi-layer Graph Convolutional Network in conjunction with jumping knowledge networks to model the multi-view graph and produce a publisher-oriented contextualized representation of news.Experimental results on two widely used fake news datasets, namely LIAR and Weibo21, demonstrate the effectiveness of our approach.Specifically, the PMGM model outperforms the state-ofthe-art methods significantly.Overall, our proposed model unifies various heterogeneous features and information related to news based on a publisher-oriented approach, thereby offering a novel idea to enhance fake news detection.

The dissemination of disinformation on social media platforms has a significant impact on personal reputation and public trust.There has been a recent surge of interest in fake news detection.However, detecting low-resource fake news, particularly those pertaining to recent events that have not yet been disseminated by users and are typically in short text, remains challenging due to the lack of training data and prior knowledge.In this paper, we introduce a novel framework named the Heterogeneous Graph Augmented Prompt-based Tuning framework (HGAPT) that can leverage the metadata of news such as publisher and topic to construct a heterogeneous graph in same batch, which improve the performance of low-resource fake news detection.We have conducted extensive experiments on two low-resource fake news datasets that were collected from real-world sources.The results demonstrate that our proposed framework outperforms state-of-the-art methods, with superior detection performance at the zero-shot setting.