From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework

Textual adversarial attacks can discover models' weaknesses by adding semantic-preserved but misleading perturbations to the inputs. The long-lasting adversarial attack-and-defense arms race in Natural Language Processing (NLP) is algorithm-centric, providing valuable techniques for automatic robustness evaluation. However, the existing practice of robustness evaluation may exhibit issues of incomprehensive evaluation, impractical evaluation protocol, and invalid adversarial samples. In this paper, we aim to set up a unified automatic robustness evaluation framework, shifting towards model-centric evaluation to further exploit the advantages of adversarial attacks. To address the above challenges, we first determine robustness evaluation dimensions based on model capabilities and specify the reasonable algorithm to generate adversarial samples for each dimension. Then we establish the evaluation protocol, including evaluation settings and metrics, under realistic demands. Finally, we use the perturbation degree of adversarial samples to control the sample validity. We implement a toolkit RobTest that realizes our automatic robustness evaluation framework. In our experiments, we conduct a robustness evaluation of RoBERTa models to demonstrate the effectiveness of our evaluation framework, and further show the rationality of each component in the framework. The code will be made public at \url{https://github.com/thunlp/RobTest}.Comment: Accepted to Findings of ACL 202

Identification of novel urine proteomic biomarkers for high stamina in high-altitude adaptation

Introduction: We aimed to identify urine biomarkers for screening individuals with adaptability to high-altitude hypoxia with high stamina levels. Although most non-high-altitude natives experience rapid decline in physical ability when ascending to high altitudes, some individuals with high-altitude adaptability continue to maintain high endurance levels.Methods: We divided the study population into two groups: the LC group (low change in endurance from low to high altitude) and HC group (high change in endurance from low to high altitude). We performed blood biochemistry testing for individuals at high altitudes and sea level. We used urine peptidome profiling to compare the HH (high-altitude with high stamina) and HL (high-altitude with low stamina) groups and the LC and HC groups to identify urine biomarkers.Results: Routine blood tests revealed that the concentration of white blood cells, lymphocytes and platelets were significantly higher in the HH group than in the HL group. Urine peptidome profiling showed that the proteins ITIH1, PDCD1LG2, NME1-NME2, and CSPG4 were significantly differentially expressed between the HH and HL groups, which was tested using ELISA. Urine proteomic analysis showed that LRG1, NID1, VASN, GPX3, ACP2, and PRSS8 were urine proteomic biomarkers of high stamina during high-altitude adaptation.Conclusion: This study provides a novel approach for identifying potential biomarkers for screening individuals who can adapt to high altitudes with high stamina

The Long Noncoding RNA MALAT1 Induces Tolerogenic Dendritic Cells and Regulatory T Cells via miR155/Dendritic Cell-Specific Intercellular Adhesion Molecule-3 Grabbing Nonintegrin/IL10 Axis

By shaping T cell immunity, tolerogenic dendritic cells (tDCs) play critical roles in the induction of immune tolerance after transplantation. However, the role of long noncoding RNAs (lncRNAs) in the function and immune tolerance of dendritic cells (DCs) is largely unknown. Here, we found that the lncRNA MALAT1 is upregulated in the infiltrating cells of tolerized mice with cardiac allografts and activated DCs. Functionally, MALAT1 overexpression favored a switch in DCs toward a tolerant phenotype. Mechanistically, ectopic MALAT1 promoted dendritic cell-specific intercellular adhesion molecule-3 grabbing nonintegrin (DC-SIGN) expression by functioning as an miR155 sponge, which is essential for the tolerogenic maintenance of DCs and the DC-SIGN-positive subset with more potent tolerogenic ability. The adoptive transfer of MALAT1-overexpressing DCs promoted cardiac allograft survival and protected from the development of experimental autoimmune myocarditis, accompanied with increasing antigen-specific regulatory T cells. Therefore, overexpressed MALAT1 induces tDCs and immune tolerance in heart transplantation and autoimmune disease by the miRNA-155/DC-SIGH/IL10 axis. This study highlights that the lncRNA MALAT1 is a novel tolerance regulator in immunity that has important implications in settings in which tDCs are preferred

Cyanidin-3-o-Glucoside Pharmacologically Inhibits Tumorigenesis via Estrogen Receptor β in Melanoma Mice

Expression patterns of estrogen receptors [ERα, ERβ, and G-protein associated ER (GPER)] in melanoma and skin may suggest their differential roles in carcinogenesis. Phytoestrogenic compound cyanidin-3-o-glucoside (C3G) has been shown to inhibit the growth and metastatic potential of melanoma, although the underlying molecular mechanism remains unclear. The aim of this study was to clarify the mechanism of action of C3G in melanoma in vitro and in vivo, as well as to characterize the functional expressions of ERs in melanoma. In normal skin or melanoma (n = 20/each), no ERα protein was detectable, whereas expression of ERβ was high in skin but weak focal or negative in melanoma; and finally high expression of GPER in all skin vs. 50% melanoma tissues (10/20) was found. These results correspond with our analysis of the melanoma survival rates (SRs) from Human Protein Atlas and The Cancer Genome Atlas GDC (362 patients), where low ERβ expression in melanoma correlate with a poor relapse-free survival, and no correlations were observed between SRs and ERα or GPER expression in melanoma. Furthermore, we demonstrated that C3G treatment arrested the cell cycle at the G2/M phase by targeting cyclin B1 (CCNB1) and promoted apoptosis via ERβ in both mouse and human melanoma cell lines, and inhibited melanoma cell growth in vivo. Our study suggested that C3G elicits an agonistic effect toward ERβ signaling enhancement, which may serve as a potential novel therapeutic and preventive approach for melanoma

Impacts of Consolidation Time on the Critical Hydraulic Gradient of Newly Deposited Silty Seabed in the Yellow River Delta

The silty seabed in the Yellow River Delta (YRD) is exposed to deposition, liquefaction, and reconsolidation repeatedly, during which seepage flows are crucial to the seabed strength. In extreme cases, seepage flows could cause seepage failure (SF) in the seabed, endangering the offshore structures. A critical condition exists for the occurrence of SF, i.e., the critical hydraulic gradient (icr). Compared with cohesionless sands, the icr of cohesive sediments is more complex, and no universal evaluation theory is available yet. The present work first improved a self-designed annular flume to avoid SF along the sidewall, then simulated the SF process of the seabed with different consolidation times in order to explore the icr of newly deposited silty seabed in the YRD. It is found that the theoretical formula for icr of cohesionless soil grossly underestimated the icr of cohesive soil. The icr range of silty seabed in the YRD was 8–16, which was significantly affected by the cohesion and was inversely proportional to the seabed fluidization degree. SF could “pump” the sediments vertically from the interior of the seabed with a contribution to sediment resuspension of up to 93.2–96.8%. The higher the consolidation degree, the smaller the contribution will be

Prediction of shear stress induced by shoaling internal solitary waves based on machine learning method

Recently, the interactions between internal solitary waves (ISWs) and the seabed have directed increasing attention to ocean engineering and offshore energy. In particular, ISWs induce bottom currents and pressure fluctuations in deep water. In this paper, we propose a method for predicting the shear stress induced by shoaling ISWs based on machine learning, and the developed approach can be used to quickly determine the safety and stability of ocean engineering. First, we provided a basic dataset for model training. Four machine learning models were selected to predict the shear stress induced by shoaling ISWs under different trim conditions. The results indicated that the performance of the convolutional neural network-long short-term memory (CNN-LSTM) forest prediction model was significantly better than the three other tested models, including long short-term memory (LSTM), support vector regression (SVR) and deep neural network (DNN) models. Therefore, the CNN-LSTM forest prediction model was the optimal model for predicting the shear stress induced by shoaling ISWs. Specifically, each metric of the CNN-LSTM model was smaller than that of the other three, and the root mean squared error to the standard deviation ratio was closest to 0.7. In addition, the CNN-LSTM model significantly outperformed the SVR and DNN models in terms of the length of prediction time. The predicted values by the CNN-LSTM model were consistent with the experimental values. The method for predicting shear stress based on machine learning in this paper can be used to predict the shear stress induced by shoaling ISWs, guide future field experiment designs, reduce damage to the seabed caused by ISWs, and promote the development of ocean engineering in deep water

Impacts of Consolidation Time on the Critical Hydraulic Gradient of Newly Deposited Silty Seabed in the Yellow River Delta

The silty seabed in the Yellow River Delta (YRD) is exposed to deposition, liquefaction, and reconsolidation repeatedly, during which seepage flows are crucial to the seabed strength. In extreme cases, seepage flows could cause seepage failure (SF) in the seabed, endangering the offshore structures. A critical condition exists for the occurrence of SF, i.e., the critical hydraulic gradient (icr). Compared with cohesionless sands, the icr of cohesive sediments is more complex, and no universal evaluation theory is available yet. The present work first improved a self-designed annular flume to avoid SF along the sidewall, then simulated the SF process of the seabed with different consolidation times in order to explore the icr of newly deposited silty seabed in the YRD. It is found that the theoretical formula for icr of cohesionless soil grossly underestimated the icr of cohesive soil. The icr range of silty seabed in the YRD was 8–16, which was significantly affected by the cohesion and was inversely proportional to the seabed fluidization degree. SF could “pump” the sediments vertically from the interior of the seabed with a contribution to sediment resuspension of up to 93.2–96.8%. The higher the consolidation degree, the smaller the contribution will be

Research on Submarine landslide monitoring and early warning system

Monitoring and early warning of submarine landslides could provide instant predictions for landslides, which is to avoid the destructive damage of submarine facilities such as pipelines and optical cable, etc effectively. However, researches on submarine landslide focus on numerical simulation and laboratory test, lacking support of in-situ observation data. This paper established the submarine landslide monitoring and early warning system by combining real-time monitoring data with web network platform and database technique. Based on the computational analysis of key monitoring parameters in the process of seabed deformation and sliding, the system has realized the accurate prediction and early warning of submarine landslides. The system has been applied to the submarine landslide monitoring in Zhoushan sea area, Zhejiang province, China, which has ensured the safety of offshore platforms and submarine projects in this area. The establishment of this system provides a new idea and method for submarine landslide warning