FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning

In recent decades, wildfires, as widespread and extremely destructive natural disasters, have caused tremendous property losses and fatalities, as well as extensive damage to forest ecosystems. Many fire risk assessment projects have been proposed to prevent wildfires, but GIS-based methods are inherently challenging to scale to different geographic areas due to variations in data collection and local conditions. Inspired by the abundance of publicly available remote sensing projects and the burgeoning development of deep learning in computer vision, our research focuses on assessing fire risk using remote sensing imagery. In this work, we propose a novel remote sensing dataset, FireRisk, consisting of 7 fire risk classes with a total of 91872 labelled images for fire risk assessment. This remote sensing dataset is labelled with the fire risk classes supplied by the Wildfire Hazard Potential (WHP) raster dataset, and remote sensing images are collected using the National Agriculture Imagery Program (NAIP), a high-resolution remote sensing imagery program. On FireRisk, we present benchmark performance for supervised and self-supervised representations, with Masked Autoencoders (MAE) pre-trained on ImageNet1k achieving the highest classification accuracy, 65.29%. This remote sensing dataset, FireRisk, provides a new direction for fire risk assessment, and we make it publicly available on https://github.com/CharmonyShen/FireRisk.Comment: 10 pages, 6 figures, 1 table, 1 equatio

Adversarial Camouflage for Node Injection Attack on Graphs

Node injection attacks against Graph Neural Networks (GNNs) have received emerging attention as a practical attack scenario, where the attacker injects malicious nodes instead of modifying node features or edges to degrade the performance of GNNs. Despite the initial success of node injection attacks, we find that the injected nodes by existing methods are easy to be distinguished from the original normal nodes by defense methods and limiting their attack performance in practice. To solve the above issues, we devote to camouflage node injection attack, i.e., camouflaging injected malicious nodes (structure/attributes) as the normal ones that appear legitimate/imperceptible to defense methods. The non-Euclidean nature of graph data and the lack of human prior brings great challenges to the formalization, implementation, and evaluation of camouflage on graphs. In this paper, we first propose and formulate the camouflage of injected nodes from both the fidelity and diversity of the ego networks centered around injected nodes. Then, we design an adversarial CAmouflage framework for Node injection Attack, namely CANA, to improve the camouflage while ensuring the attack performance. Several novel indicators for graph camouflage are further designed for a comprehensive evaluation. Experimental results demonstrate that when equipping existing node injection attack methods with our proposed CANA framework, the attack performance against defense methods as well as node camouflage is significantly improved

Robust Recommender System: A Survey and Future Directions

With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload. However, their practical deployment often encounters "dirty" data, where noise or malicious information can lead to abnormal recommendations. Research on improving recommender systems' robustness against such dirty data has thus gained significant attention. This survey provides a comprehensive review of recent work on recommender systems' robustness. We first present a taxonomy to organize current techniques for withstanding malicious attacks and natural noise. We then explore state-of-the-art methods in each category, including fraudster detection, adversarial training, certifiable robust training against malicious attacks, and regularization, purification, self-supervised learning against natural noise. Additionally, we summarize evaluation metrics and common datasets used to assess robustness. We discuss robustness across varying recommendation scenarios and its interplay with other properties like accuracy, interpretability, privacy, and fairness. Finally, we delve into open issues and future research directions in this emerging field. Our goal is to equip readers with a holistic understanding of robust recommender systems and spotlight pathways for future research and development

Graph Adversarial Immunization for Certifiable Robustness

Despite achieving great success, graph neural networks (GNNs) are vulnerable to adversarial attacks. Existing defenses focus on developing adversarial training or robust GNNs. However, little research attention is paid to the potential and practice of immunization on graphs. In this paper, we propose and formulate graph adversarial immunization, i.e., vaccinating part of graph structure to improve certifiable robustness of graph against any admissible adversarial attack. We first propose edge-level immunization to vaccinate node pairs. Despite the primary success, such edge-level immunization cannot defend against emerging node injection attacks, since it only immunizes existing node pairs. To this end, we further propose node-level immunization. To circumvent computationally expensive combinatorial optimization when solving adversarial immunization, we design AdvImmune-Edge and AdvImmune-Node algorithms to effectively obtain the immune node pairs or nodes. Experiments demonstrate the superiority of AdvImmune methods. In particular, AdvImmune-Node remarkably improves the ratio of robust nodes by 79%, 294%, and 100%, after immunizing only 5% nodes. Furthermore, AdvImmune methods show excellent defensive performance against various attacks, outperforming state-of-the-art defenses. To the best of our knowledge, this is the first attempt to improve certifiable robustness from graph data perspective without losing performance on clean graphs, providing new insights into graph adversarial learning

Exposure to 50 Hz Extremely-Low-Frequency Magnetic Fields Induces No DNA Damage in Cells by Gamma H2AX Technology

The current results for extremely-low-frequency magnetic fields (ELF-MF) on DNA damage are still debated. A sensitive indicator and systematic research are needed to assess the effects of ELF-MF. In this study, we used γH2AX as an early and sensitive molecular marker to evaluate the DNA damage effects of ELF-MF in vitro. Human amnion epithelial cells (FLs), human skin fibroblast cells (HSFs), and human umbilical vein endothelial cells (HUVECs) were exposed to 50 Hz ELF-MF at 0.4, 1, and 2 mT for 15 min, 1 h, and 24 h, respectively. After exposure, cells were subjected to γH2AX immunofluorescence and western blot. The results showed no significant difference in the average number of foci per cell, the percentage of γH2AX foci-positive cells, or the expression of γH2AX between the sham and 50 Hz ELF-MF exposure groups (P>0.05). In conclusion, 50 Hz ELF-MF did not induce DNA damage in FLs, HSFs, or HUVECs, which was independent of the intensity or duration of the exposure

GLP-1/GLP-1R Signaling Regulates Ovarian PCOS-Associated Granulosa Cells Proliferation and Antiapoptosis by Modification of Forkhead Box Protein O1 Phosphorylation Sites

As the major cause of female anovulatory infertility, polycystic ovary syndrome (PCOS) affects a great proportion of women at childbearing age. Although glucagon-like peptide 1 receptor agonists (GLP-IRAs) show therapeutic effects for PCOS, its target and underlying mechanism remains elusive. In the present study, we identified that, both in vivo and in vitro, GLP-1 functioned as the regulator of proliferation and antiapoptosis of MGCs of follicle in PCOS mouse ovary. Furthermore, forkhead box protein O1 (FoxO1) plays an important role in the courses. Regarding the importance of granulosa cells (GCs) in oocyte development and function, the results from the current study could provide a more detailed illustration on the already known beneficial effects of GLP-1RAs on PCOS and support the future efforts to develop more efficient GLP-1RAs for PCOS treatment

CCR7 expression and intratumoral FOXP3+ regulatory T cells are correlated with overall survival and lymph node metastasis in gastric cancer.

The aim of this study was to investigate the prognostic value of chemokine receptor CCR7 expression and intratumoral FOXP3(+) regulatory T cells (Tregs) in gastric cancer. CCR7(+) tumor cells and FOXP3(+) Tregs were assessed by immunohistochemistry in tissue microarrays containing gastric cancer from 133 patients. Prognostic effects of low or high CCR7 and FOXP3 expression were evaluated by Cox regression and Kaplan-Meier analysis, as well as the correlation between CCR7 positive score and intratumoral FOXP3(+) cell number in a longitudinal assessment. The analysis showed that the high expression levels of CCR7 and FOXP3 were detected in 69.9% and 65.4% of cases, respectively. High CCR7 expression in gastric cancer cells was significantly associated with poor overall survival (OS) (P = 0.010) and lymph node metastasis (P = 0.009), and was an independent factor for worse OS (P = 0.023) by multivariate analysis. High numbers of intratumoral FOXP3(+) Tregs significantly correlated with shorter OS (P = 0.021) and lymph node metastasis (P = 0.024), and was also an independent factor for adverse OS (P = 0.035). Furthermore, there was a significantly positive correlation between CCR7 positive score and intratumoral FOXP3(+) cell number (r = 0.949, P<0.001). These results revealed that CCR7 expression in gastric cancer cells and intratumoral FOXP3(+) Tregs could be considered as a co-indicator of clinical prognosis of gastric cancer