Generative Adversarial Networks (GANs) have demonstrated their remarkable capacity to learn the training data distribution and produce high-quality synthetic images, which have been widely adopted in image recognition tasks in remote sensing research communities. However, previous work has shown that using GANs does not preserves privacy, e.g., being susceptible to membership attacks, while sensitive information is vulnerable to nefarious activities. This drawback is considered severe in remote sensing communities, in which critical researches highly value the security and privacy of the image content. Thus, to publicly share sensitive data for supporting critical researches, in the meantime guarantee the model accuracy trained from privacy-preserving data, this work develops GANs within the Differential Privacy (DP) framework, and proposes a Remote Sensing Differentially Private Generative Adversarial Networks (RS-DPGANs) for both privacy-preserving synthetic image generation and classification. Our RS-DPGANs is capable of releasing safe-version of synthetic data meanwhile obtaining favorable classification r esults, w hich gives rigorous guarantees for the privacy of sensitive data and balance between the model accuracy and privacy-preserving degree. Extensive empirical and statistical results both confirm the effectiveness of our framework.

Sequential recommendation aims to predict users’ next interactions based on their historical behavior. However, existing models suffer from three major limitations: (1) reliance solely on collaborative filtering signals while overlooking item semantic information; (2) vulnerability to high-frequency noise in behavior sequences, which interferes with representation learning; and (3) computational inefficiency in Transformer-based bidirectional architectures. To address these challenges, we propose AFNBM (Adaptive Fusion via Noise-filtered Bidirectional Mamba), a novel framework that introduces three key innovations. First, we develop an adaptive semantic-collaborative fusion module that leverages pre-trained language models to extract item semantic embeddings and integrates them with collaborative embeddings through learnable parameters \(\varvec{\alpha }\) and \(\varvec{\beta }\). This mechanism effectively mitigates data sparsity and cold-start problems. Second, we integrate selective frequency modulation (SFM) into a bidirectional Mamba encoder, making this the first work to combine frequency-domain adaptive denoising with state space models (SSMs) for sequential recommendation. The bidirectional encoder captures temporal dependencies in both directions during training while the Mamba blocks themselves maintain linear complexity \(\varvec{O(L)}\). Each Mamba block incorporates an SFM layer with learnable parameter \(\varvec{\gamma }\), enabling adaptive control over high-frequency component retention. The SFM operation introduces an \(\varvec{O(L \log L)}\) factor, making the overall framework complexity \(\varvec{O(L \log L)}\), which remains significantly more efficient than Transformer-based models with \(\varvec{O(L}^{\varvec{2}}\varvec{)}\) complexity while simultaneously improving computational efficiency and noise robustness. Third, we construct a comprehensive end-to-end framework built upon the adaptive fusion and bidirectional Mamba-SFM architecture. For prediction, we employ a gated linear unit (GLU) to selectively enhance salient features, followed by a masked-terminal prediction layer. Extensive experiments validate the effectiveness of our design: ablation studies quantify each component’s contribution (e.g., GLU yields +2.2% to +5.2% gains across datasets), while AFNBM achieves 1.02%-11.76% improvements in Hit@10 and 2.84%-7.57% improvements in NDCG@10 over state-of-the-art baselines across four benchmark datasets, with particularly strong results on sparse and cold-start scenarios.

Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA. Results: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RBPs. The platform supports 70 deep learning algorithms, covering feature representation, selection, model training, comparison, optimization, and evaluation, all integrated within an automated pipeline. EnrichRBP is adept at providing comprehensive visualizations, enhancing model interpretability, and facilitating the discovery of functionally significant sequence regions crucial for RBP interactions. In addition, EnrichRBP supports base-level functional annotation tasks, offering explanations and graphical visualizations that confirm the reliability of the predicted RNA-binding sites. Leveraging high-performance computing, EnrichRBP provides ultra-fast predictions ranging from seconds to hours, applicable to both pre-trained and custom model scenarios, thus proving its utility in real-world applications. Case studies highlight that EnrichRBP provides robust and interpretable predictions, demonstrating the power of deep learning in the functional analysis of RBP interactions. Finally, EnrichRBP aims to enhance the reproducibility of computational method analyses for RBP sequences, as well as reduce the programming and hardware requirements for biologists, thereby offering meaningful functional insights. Availability and implementation: EnrichRBP is available at https://airbp.aibio-lab.com/. The source code is available at https://github.com/wangyb97/EnrichRBP, and detailed online documentation can be found at https://enrichrbp.readthedocs.io/en/latest/.