Graph Analysis in Decentralized Online Social Networks with Fine-Grained Privacy Protection

Graph analysts cannot directly obtain the global structure in decentralized social networks, and analyzing such a network requires collecting local views of the social graph from individual users. Since the edges between users may reveal sensitive social interactions in the local view, applying differential privacy in the data collection process is often desirable, which provides strong and rigorous privacy guarantees. In practical decentralized social graphs, different edges have different privacy requirements due to the distinct sensitivity levels. However, the existing differentially private analysis of social graphs provide the same protection for all edges. To address this issue, this work proposes a fine-grained privacy notion as well as novel algorithms for private graph analysis. We first design a fine-grained relationship differential privacy (FGR-DP) notion for social graph analysis, which enforces different protections for the edges with distinct privacy requirements. Then, we design algorithms for triangle counting and k-stars counting, respectively, which can accurately estimate subgraph counts given fine-grained protection for social edges. We also analyze upper bounds on the estimation error, including k-stars and triangle counts, and show their superior performance compared with the state-of-the-arts. Finally, we perform extensive experiments on two real social graph datasets and demonstrate that the proposed mechanisms satisfying FGR-DP have better utility than the state-of-the-art mechanisms due to the finer-grained protection

Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training

In recent years, graph contrastive learning (GCL) has emerged as one of the optimal solutions for various supervised tasks at the node level. However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance. In addition, general contrastive learning algorithms improve the performance of downstream tasks by increasing the number of negative samples, which leads to severe class collision and unfairness of community detection. To address above issues, we propose a novel Community-aware Efficient Graph Contrastive Learning Framework (CEGCL) to jointly learn community partition and node representations in an end-to-end manner. Specifically, we first design a personalized self-training (PeST) strategy for unsupervised scenarios, which enables our model to capture precise community-level personalized information in a graph. With the benefit of the PeST, we alleviate class collision and unfairness without sacrificing the overall model performance. Furthermore, the aligned graph clustering (AlGC) is employed to obtain the community partition. In this module, we align the clustering space of our downstream task with that in PeST to achieve more consistent node embeddings. Finally, we demonstrate the effectiveness of our model for community detection both theoretically and experimentally. Extensive experimental results also show that our CEGCL exhibits state-of-the-art performance on three benchmark datasets with different scales.Comment: 12 pages, 7 figure

Polymethylhydrosiloxane-modified gas-diffusion cathode for more efficient and durable H2O2 electrosynthesis in the context of water treatment

On-site H2O2 electrosynthesis via two-electron oxygen reduction reaction (ORR) is attracting great interest forwater treatment. The use of carbon black-based gas-diffusion electrodes (GDEs) is especially appealing, but theiractivity, selectivity and long-term stability must be improved. Here, a facile GDEs modification strategy usingtrace polymethylhydrosiloxane (PMHS) allowed reaching a outstanding H2O2 production, outperforming theconventional polytetrafluoroethylene (PTFE)-GDE (1874.8 vs 1087.4 mg L-1 at 360 min). The superhydrophobicityconferred by PMHS endowed the catalytic layer with high faradaic efficiencies (76.2%-89.7%)during long-term operation for 60 h. The electrochemical tests confirmed the high activity and selectivity of thePMHS-modified GDE. Moreover, the efficient degradation of several micropollutants by the electro-Fentonprocess demonstrated the great potential of the new GDE. An in-depth understanding of the roles of PMHSfunctional groups is provided from DFT calculations: the -CH3 groups contribute to form a superhydrophobicinterface, whereas Si-H and as-formed Si-O-C sites modulate the coordination environment of active carboncenters

Paragraph-to-Image Generation with Information-Enriched Diffusion Model

Text-to-image (T2I) models have recently experienced rapid development, achieving astonishing performance in terms of fidelity and textual alignment capabilities. However, given a long paragraph (up to 512 words), these generation models still struggle to achieve strong alignment and are unable to generate images depicting complex scenes. In this paper, we introduce an information-enriched diffusion model for paragraph-to-image generation task, termed ParaDiffusion, which delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation. At its core is using a large language model (e.g., Llama V2) to encode long-form text, followed by fine-tuning with LORA to alignthe text-image feature spaces in the generation task. To facilitate the training of long-text semantic alignment, we also curated a high-quality paragraph-image pair dataset, namely ParaImage. This dataset contains a small amount of high-quality, meticulously annotated data, and a large-scale synthetic dataset with long text descriptions being generated using a vision-language model. Experiments demonstrate that ParaDiffusion outperforms state-of-the-art models (SD XL, DeepFloyd IF) on ViLG-300 and ParaPrompts, achieving up to 15% and 45% human voting rate improvements for visual appeal and text faithfulness, respectively. The code and dataset will be released to foster community research on long-text alignment.Comment: The project website is at: https://weijiawu.github.io/ParaDiffusionPage/. Code: https://github.com/weijiawu/ParaDiffusio

Secondary infection of Fasciola gigantica in buffaloes shows a similar pattern of serum cytokine secretion as in primary infection

BackgroundAs a natural host of Fasciola gigantica, buffalo is widely infected by F. gigantica. Its impact on buffalo production has caused great losses to the husbandry sector, and repeat infection is non-negligible. In buffaloes experimentally infected with F. gigantica, primary and secondary infection have yielded the same rate of fluke recovery, indicating a high susceptibility of buffalo to F. gigantica, which contributes to the high infection rate. Determining the immunological mechanism of susceptibility will deepen the understanding of the interaction between F. gigantica and buffalo. Here, we explored the immune response of buffaloes against primary and secondary F. gigantica infection, with a focus on cytokines’ dynamics explored through serum cytokine detection.MethodsBuffaloes were assigned to three groups: group A (noninfected, n = 4), group B (primary infection, n = 3), and group C (secondary infection, n = 3). Group B was infected via oral gavage with 250 viable F. gigantica metacercariae, and group C was infected twice with 250 metacercariae at an interval of 4 weeks. The second infection of group C was performed simultaneously with that of group B. Whole blood samples were collected pre-infection (0 weeks) and at 1–6, 10, and 12  weeks after that. The serum levels of seven cytokines (IFN-γ, IL-4, IL-5, IL-10, IL-13, TGF-β, and IL-17) were simultaneously determined using ELISA and further analyzed.ResultsIn the present study, no significant changes in Th1-type cytokines production were detected in early infection, both in primary and secondary infections, while the Th2-type response was strongly induced. A comparison of primary and secondary infection showed no significant difference in the cytokine secretion, which may indicate that the re-infection at 4 weeks after primary infection could not induce a robust adaptive immune response. The full extent of interaction between buffalo and F. gigantica in re-infection requires further study