Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning

Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security standards, such as product recommendation and user behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and further downgrading its performance become extremely incentive for adversaries. Previous attackers mainly focus on structural perturbations or node injections to the existing graphs, guided by gradients from the surrogate models. Although they deliver promising results, several limitations still exist. For the structural perturbation attack, to launch a proposed attack, adversaries need to manipulate the existing graph topology, which is impractical in most circumstances. Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model. To bridge these gaps, in this paper, we study the problem of black-box node injection attack, without training a potentially misleading surrogate model. Specifically, we model the node injection attack as a Markov decision process and propose Gradient-free Graph Advantage Actor Critic, namely G2A2C, a reinforcement learning framework in the fashion of advantage actor critic. By directly querying the victim model, G2A2C learns to inject highly malicious nodes with extremely limited attacking budgets, while maintaining a similar node feature distribution. Through our comprehensive experiments over eight acknowledged benchmark datasets with different characteristics, we demonstrate the superior performance of our proposed G2A2C over the existing state-of-the-art attackers. Source code is publicly available at: https://github.com/jumxglhf/G2A2C}.Comment: AAAI 2023. v2: update acknowledgement section. arXiv admin note: substantial text overlap with arXiv:2202.0938

Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering

A common thread of open-domain question answering (QA) models employs a retriever-reader pipeline that first retrieves a handful of relevant passages from Wikipedia and then peruses the passages to produce an answer. However, even state-of-the-art readers fail to capture the complex relationships between entities appearing in questions and retrieved passages, leading to answers that contradict the facts. In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA. Specifically, for each pair of question and retrieved passage, we first construct a localized bipartite graph, attributed to entity embeddings extracted from the intermediate layer of the reader model. Then, a graph neural network learns relational knowledge while fusing graph and contextual representations into the hidden states of the reader model. Experiments on three open-domain QA benchmarks show Grape can improve the state-of-the-art performance by up to 2.2 exact match score with a negligible overhead increase, with the same retriever and retrieved passages. Our code is publicly available at https://github.com/jumxglhf/GRAPE.Comment: Findings of EMNLP202

Research on Dynamic Response of Pipeline under the Reeling Process and Laying Process

During the process of laying submarine pipelines using the R-lay (short for reel-lay) method, the interaction between the pipeline and the laying equipment undergoes continual fluctuations, leading to bending in the pipeline induced by the stochastic dynamics of various external loads. Considering the challenge in forecasting the dynamic behavior of pipeline bending moments and ovality throughout this procedure, we constructed a finite element-based shell element model for a 6-inch pipeline. In this paper, a multi-step simulation approach was used to replicate the pipeline laying process, and the dynamic response in pipeline bending moments and ovality during the winding, unwinding, and straightening processes was analyzed. Additionally, the effects of the pipeline’s diameter–thickness ratio and material properties on the dynamic response process were also studied. The results show that the dynamic response in bending moments and ovality is closely related to the curvature of the pipeline; a brief peak will appear at the critical point where the pipeline deforms, and the peak is related to the different bending stages of the pipeline, with the winding stage having a greater impact on the peak than the unwinding stage. During the unwinding process, a reverse bending moment will occur. The dynamic response of pipeline bending moments and ovality is influenced to some extent by the pipeline’s diameter–thickness ratio and material properties, with the diameter–thickness ratio demonstrating a more conspicuous impact

Disentangled Representation Learning in Heterogeneous Information Network for Large-scale Android Malware Detection in the COVID-19 Era and Beyond

In the fight against the COVID-19 pandemic, many social activities have moved online; society's overwhelming reliance on the complex cyberspace makes its security more important than ever. In this paper, we propose and develop an intelligent system named Dr.HIN to protect users against the evolving Android malware attacks in the COVID-19 era and beyond. In Dr.HIN, besides app content, we propose to consider higher-level semantics and social relations among apps, developers and mobile devices to comprehensively depict Android apps; and then we introduce a structured heterogeneous information network (HIN) to model the complex relations and exploit meta-path guided strategy to learn node (i.e., app) representations from HIN. As the representations of malware could be highly entangled with benign apps in the complex ecosystem of development, it poses a new challenge of learning the latent explanatory factors hidden in the HIN embeddings to detect the evolving malware. To address this challenge, we propose to integrate domain priors generated from different views (i.e., app content, app authorship, app installation) to devise an adversarial disentangler to separate the distinct, informative factors of variations hidden in the HIN embeddings for large-scale Android malware detection. This is the first attempt of disentangled representation learning in HIN data. Promising experimental results based on the large-scale and real sample collections from security industry demonstrate the performance of Dr.HIN in evolving Android malware detection, by comparison with baselines and popular mobile security products

Role of Exchange Protein Directly Activated by Cyclic AMP Isoform 1 in Energy Homeostasis: Regulation of Leptin Expression and Secretion in White Adipose Tissue

Epacs (exchange proteins directly activated by cyclic AMP [cAMP]) act as downstream effectors of cAMP and play important roles in energy balance and glucose homeostasis. While global deletion of Epac1 in mice leads to heightened leptin sensitivity in the hypothalamus and partial protection against high-fat diet (HFD)-induced obesity, the physiological functions of Epac1 in white adipose tissue (WAT) has not been explored. Here, we report that adipose tissue-specific Epac1 knockout (AEKO) mice are more prone to HFD-induced obesity, with increased food intake, reduced energy expenditure, and impaired glucose tolerance. Despite the fact that AEKO mice on HFD display increased body weight, these mice have decreased circulating leptin levels compared to their wild-type littermates. In vivo and in vitro analyses further reveal that suppression of Epac1 in WAT decreases leptin mRNA expression and secretion by inhibiting cAMP response element binding (CREB) protein and AKT phosphorylation, respectively. Taken together, our results demonstrate that Epac1 plays an important role in regulating energy balance and glucose homeostasis by promoting leptin expression and secretion in WAT

Role of Exchange Protein Directly Activated by Cyclic AMP Isoform 1 in Energy Homeostasis: Regulation of Leptin Expression and Secretion in White Adipose Tissue

Epacs (exchange proteins directly activated by cyclic AMP [cAMP]) act as downstream effectors of cAMP and play important roles in energy balance and glucose homeostasis. While global deletion of Epac1 in mice leads to heightened leptin sensitivity in the hypothalamus and partial protection against high-fat diet (HFD)-induced obesity, the physiological functions of Epac1 in white adipose tissue (WAT) has not been explored. Here, we report that adipose tissue-specific Epac1 knockout (AEKO) mice are more prone to HFD-induced obesity, with increased food intake, reduced energy expenditure, and impaired glucose tolerance. Despite the fact that AEKO mice on HFD display increased body weight, these mice have decreased circulating leptin levels compared to their wild-type littermates. In vivo and in vitro analyses further reveal that suppression of Epac1 in WAT decreases leptin mRNA expression and secretion by inhibiting cAMP response element binding (CREB) protein and AKT phosphorylation, respectively. Taken together, our results demonstrate that Epac1 plays an important role in regulating energy balance and glucose homeostasis by promoting leptin expression and secretion in WAT

CEPC Conceptual Design Report: Volume 2 - Physics & Detector

The Circular Electron Positron Collider (CEPC) is a large international scientific facility proposed by the Chinese particle physics community to explore the Higgs boson and provide critical tests of the underlying fundamental physics principles of the Standard Model that might reveal new physics. The CEPC, to be hosted in China in a circular underground tunnel of approximately 100 km in circumference, is designed to operate as a Higgs factory producing electron-positron collisions with a center-of-mass energy of 240 GeV. The collider will also operate at around 91.2 GeV, as a Z factory, and at the WW production threshold (around 160 GeV). The CEPC will produce close to one trillion Z bosons, 100 million W bosons and over one million Higgs bosons. The vast amount of bottom quarks, charm quarks and tau-leptons produced in the decays of the Z bosons also makes the CEPC an effective B-factory and tau-charm factory. The CEPC will have two interaction points where two large detectors will be located. This document is the second volume of the CEPC Conceptual Design Report (CDR). It presents the physics case for the CEPC, describes conceptual designs of possible detectors and their technological options, highlights the expected detector and physics performance, and discusses future plans for detector R&D and physics investigations. The final CEPC detectors will be proposed and built by international collaborations but they are likely to be composed of the detector technologies included in the conceptual designs described in this document. A separate volume, Volume I, recently released, describes the design of the CEPC accelerator complex, its associated civil engineering, and strategic alternative scenarios