Variations of lake area and shoreline can indicate hydrological and climatic changes effectively. Accordingly, how to automatically and simultaneously extract lake area and shoreline from remote sensing images attracts our attention. In this paper, we formulate lake area and shoreline extraction as a multitask learning problem. Different from existing models that take the deep and complex network architecture as the backbone to extract feature maps, we present LaeNet—a novel end-to-end lightweight multitask fully CNN with no-downsampling to automatically extract lake area and shoreline from remote sensing images. Landsat-8 images over Selenco and the vicinity in the Tibetan Plateau are utilized to train and evaluate our model. Experimental results over the testing image patches achieve an Accuracy of 0.9962, Precision of 0.9912, Recall of 0.9982, F1-score of 0.9941, and mIoU of 0.9879, which align with the mainstream semantic segmentation models (UNet, DeepLabV3+, etc.) or even better. Especially, the running time of each epoch and the size of our model are only 6 s and 0.047 megabytes, which achieve a significant reduction compared to the other models. Finally, we conducted fieldwork to collect the in-situ shoreline position for one typical part of lake Selenco, in order to further evaluate the performance of our model. The validation indicates high accuracy in our results (DRMSE: 30.84 m, DMAE: 22.49 m, DSTD: 21.11 m), only about one pixel deviation for Landsat-8 images. LaeNet can be expanded potentially to the tasks of area segmentation and edge extraction in other application fields.

As eating-out became an indispensable part of our daily lives, demand for the food recognition of unfamiliar restaurant increased significantly due to health-care. Although there are many researches on generic food recognition, there are relatively fewer studies on restaurant food image recognition. Meanwhile, it becomes extremely challenging for restaurant food image recognition due to insufficient food image. Prototypical network is common utilized to address the such a task in recent years. Although the methods based on prototypical network achieve impressive results in capturing similarities feature of the same food category, it fails to highlight important information on feature and instance level. Toward this end, we propose an effective food image recognition scheme by incorporating hybrid attention mechanism into prototypical network in this paper. Specifically, the image feature is first captured by convolutional neural network (CNN). Then the image attention weights yielded by instance-based attention mechanism are used to modulate the image feature of CNN for constructing class prototypes. And feature-based attention mechanism is employed to grasp important information of image for enriching image representation. Extensive experimental results on the large food image dataset verify that the performance of our proposed classification scheme outperforms the state-of-the-art ones.

Currently, micro-videos have grown explosively on various online social platforms. Accordingly, how to encode them to yield effective representation attracts our attention. NeXtVLAD is such an effective network that aggregates frame-level features into a compact supervector. However, the discriminant capability of such a supervector is still limited due to the lack of non-linear transformation and L2 normalization at the head and tail of original NeXtVLAD network, respectively. In order to address such problems, we propose an improved neural network architecture, normalized NeXtVLAD (NNeXtVLAD), which is extended with ReLU function and L2 normalization. In the light of such a new network, we build up an end-to-end framework which jointly learns NNeXtVLAD, CNN layer, and context gating for micro-video venue classification. Specifically, we first apply NNeXtVLAD layers as three-stream architecture to aggregate visual, acoustic, and textual features. We then pack and embed the aggregated features into CNN layer for enhancing the sparse concept-level representation. Finally, context gating is used to capture the interdependency among different network activations. Extensive experimental results on a real-world micro-video dataset exhibit that our proposed model significantly outperforms the state-of-the-art baselines in terms of both Micro-F1 and Macro-F1 scores.

Background: Tumor heterogeneity arises from complex interactions among diverse biological factors, posing a major challenge for the development of robust multi-omics data integration methods. While the existing Tumor Multi-Omics pre-trained Network (TMO-Net) enables the fusion of multi-omics features into unified representations, its practical utility is constrained by issues such as missing modalities, incomplete within-omics data, and high-dimensional noise. To overcome these limitations, we propose TMO-Net+, an enhanced architecture specifically designed to improve the robustness and reliability of multi-omics modeling. Methods: TMO-Net+ introduces several coordinated architectural enhancements. First, a feature attention encoder is applied to each omics data type to reduce the influence of modality-dependent input variation. Second, we combine a gated Mixture-of-Experts (MoE) module with a Product-of Experts (PoE) mechanism to capture sample-specific contributions and enable robust inference even when partial omics data are available. Additionally, a supervised deep classification head with a tailored loss function is incorporated to enhance the separability of learned embeddings in the latent space. Results: Extensive experiments on pan-cancer datasets demonstrate that TMO-Net+ consistently outperforms the original TMO-Net, as measured by LogME scores. Furthermore, in various downstream tasks (e.g., pan-cancer classification, primary/metastatic site prediction, and prognostic modeling), TMO-Net+ achieves superior performance under partial-omics settings, which proves that it enhances the robustness and cross-cancer transferability of the multi-omics representations. Conclusions: The proposed TMO-Net+ improves the robustness and cross-cancer transferability of multi-omics representations within the evaluated TCGA cohorts. Biological interpretability analyses further show that TMO-Net+ prioritizes established cancer-driver genes, preserves cancer-dependent molecular-state information, and adaptively redistributes relative modality contributions across molecular states. By addressing modality-level missingness and modality-dependent input variation, it offers a reliable framework for integrative tumor analysis within the evaluated TCGA cohorts.