Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework

Common biological processes and mutual crosstalk mechanisms between cardiovascular disease and cancer

Cancer and cardiovascular disease (CVD) are leading causes of mortality and thus represent major health challenges worldwide. Clinical data suggest that cancer patients have an increased likelihood of developing cardiovascular disease, while epidemiologic studies have shown that patients with cardiovascular disease are also more likely to develop cancer. These observations underscore the increasing importance of studies exploring the mechanisms underlying the interaction between the two diseases. We review their common physiological processes and potential pathophysiological links. We explore the effects of chronic inflammation, oxidative stress, and disorders of fatty acid metabolism in CVD and cancer, and also provide insights into how cancer and its treatments affect heart health, as well as present recent advances in reverse cardio-oncology using a new classification approach

ASPS: Augmented Segment Anything Model for Polyp Segmentation

Polyp segmentation plays a pivotal role in colorectal cancer diagnosis. Recently, the emergence of the Segment Anything Model (SAM) has introduced unprecedented potential for polyp segmentation, leveraging its powerful pre-training capability on large-scale datasets. However, due to the domain gap between natural and endoscopy images, SAM encounters two limitations in achieving effective performance in polyp segmentation. Firstly, its Transformer-based structure prioritizes global and low-frequency information, potentially overlooking local details, and introducing bias into the learned features. Secondly, when applied to endoscopy images, its poor out-of-distribution (OOD) performance results in substandard predictions and biased confidence output. To tackle these challenges, we introduce a novel approach named Augmented SAM for Polyp Segmentation (ASPS), equipped with two modules: Cross-branch Feature Augmentation (CFA) and Uncertainty-guided Prediction Regularization (UPR). CFA integrates a trainable CNN encoder branch with a frozen ViT encoder, enabling the integration of domain-specific knowledge while enhancing local features and high-frequency details. Moreover, UPR ingeniously leverages SAM's IoU score to mitigate uncertainty during the training procedure, thereby improving OOD performance and domain generalization. Extensive experimental results demonstrate the effectiveness and utility of the proposed method in improving SAM's performance in polyp segmentation. Our code is available at https://github.com/HuiqianLi/ASPS.Comment: Accepted by MICCAI202

DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services

We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services. We adopt legal syllogism prompting strategies to construct supervised fine-tuning datasets in the Chinese Judicial domain and fine-tune LLMs with legal reasoning capability. We augment LLMs with a retrieval module to enhance models' ability to access and utilize external legal knowledge. A comprehensive legal benchmark, DISC-Law-Eval, is presented to evaluate intelligent legal systems from both objective and subjective dimensions. Quantitative and qualitative results on DISC-Law-Eval demonstrate the effectiveness of our system in serving various users across diverse legal scenarios. The detailed resources are available at https://github.com/FudanDISC/DISC-LawLLM

Optimization of Flash Extraction Process and Antioxidant Activity of American Ginseng Flower Polysaccharides

Objective: To study the optimal process conditions of flash extraction and antioxidant activity of American ginseng flower polysaccharides (AGFPs). Methods: American ginseng flower (AGF) as the raw material, the effects of extraction voltage, liquid-material ratio, and extraction time on the yield of AGFPs were explored. The flash extraction process of AGFPs was improved by response surface methodology. The scavenging effect on DPPH and hydroxy free radicals and total reduction capacity of AGFPs were determined to assess the antioxidant activity of AGFPs. Results: The optimal extraction conditions were determined as 130 V of extraction voltage, 30:1 mL/g of liquid-material ratio, and 100 s of extraction time, resulting in an AGFPs yield of 11.12%±0.23%, which agreed with the model prediction. The AGFPs exhibited significant scavenging effects on DPPH and hydroxyl radicals, with IC50 values of 1.34 mg/mL and 1.42 mg/mL, respectively, and had a certain reducing power. Conclusion: These results suggested that flash extraction was an efficient and rapid method for obtaining AGFPs from AGF, and that AGFPs had promising antioxidant potential for further applications. This study can provide a theoretical basis for the development and application of AGF

Potent neutralization of hepatitis A virus reveals a receptor mimic mechanism and the receptor recognition site.

Hepatitis A virus (HAV) infects ∼1.4 million people annually and, although there is a vaccine, there are no licensed therapeutic drugs. HAV is unusually stable (making disinfection problematic) and little is known of how it enters cells and releases its RNA. Here we report a potent HAV-specific monoclonal antibody, R10, which neutralizes HAV infection by blocking attachment to the host cell. High-resolution cryo-EM structures of HAV full and empty particles and of the complex of HAV with R10 Fab reveal the atomic details of antibody binding and point to a receptor recognition site at the pentamer interface. These results, together with our observation that the R10 Fab destabilizes the capsid, suggest the use of a receptor mimic mechanism to neutralize virus infection, providing new opportunities for therapeutic intervention

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-identification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obstacle Segmentation and Detection features three sub-challenges, including a new embedded challenge addressing efficicent inference on real-world embedded devices. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 195 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi24.Comment: Part of 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 IEEE Xplore submission as part of WACV 202