Robustness-Inspired Defense Against Backdoor Attacks on Graph Neural Networks

Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significant threat to their real-world adoption. Despite initial efforts to defend against specific graph backdoor attacks, there is no work on defending against various types of backdoor attacks where generated triggers have different properties. Hence, we first empirically verify that prediction variance under edge dropping is a crucial indicator for identifying poisoned nodes. With this observation, we propose using random edge dropping to detect backdoors and theoretically show that it can efficiently distinguish poisoned nodes from clean ones. Furthermore, we introduce a novel robust training strategy to efficiently counteract the impact of the triggers. Extensive experiments on real-world datasets show that our framework can effectively identify poisoned nodes, significantly degrade the attack success rate, and maintain clean accuracy when defending against various types of graph backdoor attacks with different properties

Region Normalization for Image Inpainting

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem; (2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements.Comment: Accepted by AAAI-202

A VMD and LSTM based hybrid model of load forecasting for power grid security

As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply-demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on Vari-ational mode decomposition (VMD) and Long Short-Term Memory (LSTM) with seasonal factors elimination and error correction is proposed in this paper. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models. Index Terms-Power grid security, short-term load forecasting , seasonal factors elimination, error correction

Counterfactual Learning on Graphs: A Survey

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success in representation learning on graphs, facilitating various downstream tasks. However, GNNs have several drawbacks such as lacking interpretability, can easily inherit the bias of data and cannot model casual relations. Recently, counterfactual learning on graphs has shown promising results in alleviating these drawbacks. Various approaches have been proposed for counterfactual fairness, explainability, link prediction and other applications on graphs. To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning. We divide existing methods into four categories based on problems studied. For each category, we provide background and motivating examples, a general framework summarizing existing works and a detailed review of these works. We point out promising future research directions at the intersection of graph-structured data, counterfactual learning, and real-world applications. To offer a comprehensive view of resources for future studies, we compile a collection of open-source implementations, public datasets, and commonly-used evaluation metrics. This survey aims to serve as a ``one-stop-shop'' for building a unified understanding of graph counterfactual learning categories and current resources. We also maintain a repository for papers and resources and will keep updating the repository https://github.com/TimeLovercc/Awesome-Graph-Causal-Learning

The clinical outcome of pembrolizumab for patients with recurrent or metastatic squamous cell carcinoma of the head and neck: a single center, real world study in China

BackgroundThe KEYNOTE-048 and KEYNOTE-040 study have demonstrated the efficacy of pembrolizumab in recurrent or metastatic squamous cell carcinoma of the head and neck (R/M HNSCC), we conducted this real-world study to investigate the efficacy of pembrolizumab in patients with R/M HNSCC.MethodsThis is a single-center retrospective study conducted in the Shanghai Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Shanghai, China). Between December 2020 and December 2022, a total of 77 patients with R/M HNSCC were included into analysis. The primary endpoint of the study was overall survival (OS), and the secondary endpoints were progression-free survival (PFS), overall response rate (ORR)and toxicity.Efficacy was assessed according to RECIST version 1.1.SPSS 27.0 and GraphPad Prism 8.0 software were utilized to perform the statistical analysis.ResultsBy the cut-off date (February 28, 2023), the median OS,PFS and ORR were 15.97 months,8.53 months and 48.9% in patients treated with the pembrolizumab regimen in the first line therapy. Among these patients, 17 patients received pembrolizumab with cetuximab,and 18 received pembrolizumab with chemotherapy.We observed no significant differences between two groups neither in median OS (13.9 vs 19.4 months, P=0.3582) nor PFS (unreached vs 8.233 months, P= 0.2807). In the ≥2nd line therapy (n=30), the median OS, PFS and ORR were 5.7 months, 2.58 months and 20% respectively. Combined positive score (CPS) was eligible from 54 patients. For first line therapy, the median OS and PFS were 14.6 and 8.53 months in patients with CPS ≥1, and median OS and PFS were 14.6 and 12.33 months in patients with CPS ≥20. The immune-related adverse events (irAEs) were occurred in the 31 patients (31/77, 40.26%), and the most common potential irAEs were hypothyroidism (25.97%), and pneumonitis (7.79%).ConclusionOur real-world results indicated that pembrolizumab regimen is a promising treatment in patients with R/M HNSC

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. METHODS: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). FINDINGS: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. INTERPRETATION: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. FUNDING: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438

Sp1 Is Essential for p16(INK4a) Expression in Human Diploid Fibroblasts during Senescence

BACKGROUND: p16 (INK4a) tumor suppressor protein has been widely proposed to mediate entrance of the cells into the senescent stage. Promoter of p16 (INK4a) gene contains at least five putative GC boxes, named GC-I to V, respectively. Our previous data showed that a potential Sp1 binding site, within the promoter region from −466 to −451, acts as a positive transcription regulatory element. These results led us to examine how Sp1 and/or Sp3 act on these GC boxes during aging in cultured human diploid fibroblasts. METHODOLOGY/PRINCIPAL FINDINGS: Mutagenesis studies revealed that GC-I, II and IV, especially GC-II, are essential for p16 (INK4a) gene expression in senescent cells. Electrophoretic mobility shift assays (EMSA) and ChIP assays demonstrated that both Sp1 and Sp3 bind to these elements and the binding activity is enhanced in senescent cells. Ectopic overexpression of Sp1, but not Sp3, induced the transcription of p16 (INK4a). Both Sp1 RNAi and Mithramycin, a DNA intercalating agent that interferes with Sp1 and Sp3 binding activities, reduced p16 (INK4a) gene expression. In addition, the enhanced binding of Sp1 to p16 (INK4a) promoter during cellular senescence appeared to be the result of increased Sp1 binding affinity, not an alteration in Sp1 protein level. CONCLUSIONS/SIGNIFICANCE: All these results suggest that GC- II is the key site for Sp1 binding and increase of Sp1 binding activity rather than protein levels contributes to the induction of p16 (INK4a) expression during cell aging

Guidelines for burn rehabilitation in China

Abstract Quality of life and functional recovery after burn injury is the final goal of burn care, especially as most of burn patients survive the injury due to advanced medical science. However, dysfunction, disfigurement, contractures, psychological problems and other discomforts due to burns and the consequent scars are common, and physical therapy and occupational therapy provide alternative treatments for these problems of burn patients. This guideline, organized by the Chinese Burn Association and Chinese Association of Burn Surgeons aims to emphasize the importance of team work in burn care and provide a brief introduction of the outlines of physical and occupational therapies during burn treatment, which is suitable for the current medical circumstances of China. It can be used as the start of the tools for burn rehabilitation.</jats:p