Effective Targeted Attacks for Adversarial Self-Supervised Learning

Recently, unsupervised adversarial training (AT) has been highlighted as a means of achieving robustness in models without any label information. Previous studies in unsupervised AT have mostly focused on implementing self-supervised learning (SSL) frameworks, which maximize the instance-wise classification loss to generate adversarial examples. However, we observe that simply maximizing the self-supervised training loss with an untargeted adversarial attack often results in generating ineffective adversaries that may not help improve the robustness of the trained model, especially for non-contrastive SSL frameworks without negative examples. To tackle this problem, we propose a novel positive mining for targeted adversarial attack to generate effective adversaries for adversarial SSL frameworks. Specifically, we introduce an algorithm that selects the most confusing yet similar target example for a given instance based on entropy and similarity, and subsequently perturbs the given instance towards the selected target. Our method demonstrates significant enhancements in robustness when applied to non-contrastive SSL frameworks, and less but consistent robustness improvements with contrastive SSL frameworks, on the benchmark datasets.Comment: NeurIPS 202

Learning Transferable Adversarial Robust Representations via Multi-view Consistency

Despite the success on few-shot learning problems, most meta-learned models only focus on achieving good performance on clean examples and thus easily break down when given adversarially perturbed samples. While some recent works have shown that a combination of adversarial learning and meta-learning could enhance the robustness of a meta-learner against adversarial attacks, they fail to achieve generalizable adversarial robustness to unseen domains and tasks, which is the ultimate goal of meta-learning. To address this challenge, we propose a novel meta-adversarial multi-view representation learning framework with dual encoders. Specifically, we introduce the discrepancy across the two differently augmented samples of the same data instance by first updating the encoder parameters with them and further imposing a novel label-free adversarial attack to maximize their discrepancy. Then, we maximize the consistency across the views to learn transferable robust representations across domains and tasks. Through experimental validation on multiple benchmarks, we demonstrate the effectiveness of our framework on few-shot learning tasks from unseen domains, achieving over 10\% robust accuracy improvements against previous adversarial meta-learning baselines.Comment: *Equal contribution (Author ordering determined by coin flip). NeurIPS SafetyML workshop 2022, Under revie

GSK3B induces autophagy by phosphorylating ULK1

Unc-51-like autophagy activating kinase 1 (ULK1), a mammalian homolog of the yeast kinase Atg1, has an essential role in autophagy induction. In nutrient and growth factor signaling, ULK1 activity is regulated by various posttranslational modifications, including phosphorylation, acetylation, and ubiquitination. We previously identified glycogen synthase kinase 3 beta (GSK3B) as an upstream regulator of insulin withdrawal-induced autophagy in adult hippocampal neural stem cells. Here, we report that following insulin withdrawal, GSK3B directly interacted with and activated ULK1 via phosphorylation of S405 and S415 within the GABARAP-interacting region. Phosphorylation of these residues facilitated the interaction of ULK1 with MAP1LC3B and GABARAPL1, while phosphorylation-defective mutants of ULK1 failed to do so and could not induce autophagy flux. Furthermore, high phosphorylation levels of ULK1 at S405 and S415 were observed in human pancreatic cancer cell lines, all of which are known to exhibit high levels of autophagy. Our results reveal the importance of GSK3B-mediated phosphorylation for ULK1 regulation and autophagy induction and potentially for tumorigenesis. © 2021, The Author(s).1

Load Evaluation for Tower Design of Large Floating Offshore Wind Turbine System According to Wave Conditions

This study entailed a load evaluation for the tower design of a large floating offshore wind turbine system in accordance with the wave conditions. The target model includes the IEA 15 MW reference wind turbine and a semi-submersible VolturnUS-S reference floating offshore wind turbine platform from the University of Maine. The OpenFAST, which is an aero-hydro-servo-elastic fully coupled analysis tool, was used for load analysis. The DLC1.2 and 1.6 were used as the design load cases, and the environmental conditions suitable for the design load cases were cited in the VolturnUS-S platform report. Load evaluation was performed according to time series and FFT results. The findings of the study are as follows: first, in the correlation analysis, the tower-top deflection had the highest correlation, and this further affects nacelle acceleration. Second, the tower-base pitch moment increased with the significant wave height. However, the wave peak period increased until it matched the tower-top deflection frequency and decreased thereafter. Third, the comparison between the normal and severe sea state conditions revealed that the tower-base pitch moments for the two conditions are almost similar, despite the conditions wherein the wave spectral energy differs by a factor of 3.5. Fourth, the tower shape is changed while adjusting the diameter of the tower, and the tower-top and tower-base pitch moments are reviewed using a redesigned tower. Even if the mass is the same, adjusting the diameter of the tower reduces only the pitch moment

A Numerical Study on the Performance Evaluation of a Semi-Type Floating Offshore Wind Turbine System According to the Direction of the Incoming Waves

In this study, the performance evaluation of a semi-type floating offshore wind turbine system according to the direction of the incoming waves is investigated. The target model in this this study is a DTU 10 MW reference wind turbine and a LIFES50+ OO-Star Wind Floater Semi 10 MW, which is the semisubmersible platform. Numerical simulation is performed using FAST developed by National Renewable Energy Laboratory (NREL), which is an aero-hydro-servo-elastic fully coupled simulation tool. The analysis condition used in this study is the misalignment condition, which is the wind direction fixed at 0 degree and the wave direction changed at 15 degrees intervals. In this study, two main contents could be confirmed. First, it is confirmed that sway, roll, and yaw motions occur even though the direction of the incoming waves is 0 degree. The cause of the platform’s motion such as sway, roll and yaw is the turbulent wind and gyroscope phenomenon. In addition, the optimal value for the nacelle–yaw angle that maximizes the rotor power and minimizes the tower load is confirmed by solving the multiobjective optimization problem. These results show the conclusion that setting the initial nacelle–yaw angle can reduce the tower load and get a higher generator power. Second, it is confirmed that the platform’s motion and loads may be underestimated depending on the interval angle of incidence of the wind and waves. In particular, through the load diagram results, it is confirmed that most of the results are asymmetric, and the blade and tower loads are especially spiky. Through these results, the importance of examining the interval angle of incidence of the wind and waves is confirmed. Unlike previous studies, this will be a more considerable issue as turbines become larger and platforms become more complex

<i>Artemisia gmelinii</i> Extract Attenuates Particulate Matter-Induced Neutrophilic Inflammation in a Mouse Model of Lung Injury

Particulate matter (PM) induces and augments oxidative stress and inflammation, leading to respiratory diseases. Although Artemisia gmelinii Weber ex Stechm has antioxidant and anti-inflammatory effects, there are no reports on whether Artemisia gmelinii extract (AGE) regulates lung inflammation in a PM-induced model. Thus, we investigated the protective effects of AGE using a PM-induced mouse lung inflammation model. AGE significantly decreased the expression of inflammatory chemokines, neutrophil extracellular trap formation, and the total number of inflammatory cells in the bronchoalveolar lavage fluid (BALF). Furthermore, AGE attenuated lung inflammation through the suppression of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)/mitogen-activated protein kinase (MAPK) signaling pathway, while promoting the nuclear factor erythroid-2-related factor 2 (NRF2)/heme oxygenase-1 (HO-1) signaling pathway in lung tissues. Concordant with these observations, AGE suppressed inflammatory cytokines, chemokines, reactive oxygen species, NETosis, myeloperoxidase, and neutrophil elastase by decreasing the mRNA expression of High mobility group box 1, Runt-related transcription factor 1, and Kruppel-like factor 6 in differentiated HL-60 cells. In summary, our data demonstrated that AGE suppresses PM-induced neutrophil infiltration, lung damage, and pulmonary inflammation by suppressing NF-κB/MAPK signaling pathways and enhancing the NRF2/HO-1 signaling pathway. These findings suggest that AGE administration is an effective approach for preventing and treating PM-induced respiratory inflammation