Structure Invariant Transformation for better Adversarial Transferability

Given the severe vulnerability of Deep Neural Networks (DNNs) against adversarial examples, there is an urgent need for an effective adversarial attack to identify the deficiencies of DNNs in security-sensitive applications. As one of the prevalent black-box adversarial attacks, the existing transfer-based attacks still cannot achieve comparable performance with the white-box attacks. Among these, input transformation based attacks have shown remarkable effectiveness in boosting transferability. In this work, we find that the existing input transformation based attacks transform the input image globally, resulting in limited diversity of the transformed images. We postulate that the more diverse transformed images result in better transferability. Thus, we investigate how to locally apply various transformations onto the input image to improve such diversity while preserving the structure of image. To this end, we propose a novel input transformation based attack, called Structure Invariant Attack (SIA), which applies a random image transformation onto each image block to craft a set of diverse images for gradient calculation. Extensive experiments on the standard ImageNet dataset demonstrate that SIA exhibits much better transferability than the existing SOTA input transformation based attacks on CNN-based and transformer-based models, showing its generality and superiority in boosting transferability. Code is available at https://github.com/xiaosen-wang/SIT.Comment: Accepted by ICCV 202

Modelling, Diagnosis, and Fault-Tolerant Control of Open-Circuit Faults in Three-Phase Two-Level PMSM Drives

Attributing to the high efficiency, compact structure, and rapid dynamics, powertrains utilizing Permanent Magnet Synchronous Motors (PMSM) have emerged as a promising alternative and have seen extensive deployment in various industrial and transportation sectors, including electric vehicles (EVs), more-electric aircraft, and robotics. Despite ongoing interest in advanced redundant topologies for PMSM drives from both academia and industry, the three-phase two-level (3P2L) PMSM drive continues to dominate the majority of the electric drive market. However, when compared to its multi-phase counterparts, the most-commonly used 3P2L PMSM drive exhibits limited reliability and fault tolerance capabilities, particularly in safety-critical or cost-sensitive scenarios. Therefore, the development of embedded reliability-enhancing techniques holds great significance in enhancing the safety and maintenance of on-site powertrains based on the 3P2L PMSM drive. The purposes of this study are to investigate post-fault system models and develop hardwarefree fault diagnostic and fault-tolerant methods that can be conveniently integrated into existing 3P2L PMSM drives. Special attention is dedicated to the open-circuit fault, as it represents one of the ultimate consequences of fault propagation in PMSM drives. In the first place, the fault propagation from component failures to open-circuit faults is analyzed, and the existing literature on the modelling, diagnosis, and fault-tolerant control of PMSM drives is comprehensively reviewed. Subsequently, the study delves into the postfault system model under the open-phase (OP) fault, which includes the examination of postfault phase voltages and current prediction. Based on the phase voltages observed under the OP fault, a phenomenon of particular interest is modelled: the remaining current that flows through the free-wheeling diodes of the faulty phase under the open-switch (OS) fault. The conduction mechanism is elucidated, and a real-time estimation model is established. Furthermore, a sampling method is designed to enable the motor drive to detect the remaining current in the OS phase, along with a set of diagnostic rules to distinguish between OS and OP faults. Finally, an embedded fault-tolerant control method is introduced to enhance the post-fault speed and torque outputs of 3P2L PMSM drives

One Forward is Enough for Neural Network Training via Likelihood Ratio Method

While backpropagation (BP) is the mainstream approach for gradient computation in neural network training, its heavy reliance on the chain rule of differentiation constrains the designing flexibility of network architecture and training pipelines. We avoid the recursive computation in BP and develop a unified likelihood ratio (ULR) method for gradient estimation with just one forward propagation. Not only can ULR be extended to train a wide variety of neural network architectures, but the computation flow in BP can also be rearranged by ULR for better device adaptation. Moreover, we propose several variance reduction techniques to further accelerate the training process. Our experiments offer numerical results across diverse aspects, including various neural network training scenarios, computation flow rearrangement, and fine-tuning of pre-trained models. All findings demonstrate that ULR effectively enhances the flexibility of neural network training by permitting localized module training without compromising the global objective and significantly boosts the network robustness

A Novel Noise Injection-based Training Scheme for Better Model Robustness

Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work. In this work, we propose a novel noise injection-based training scheme for better model robustness. Specifically, we first develop a likelihood ratio method to estimate the gradient with respect to both synaptic weights and noise levels for stochastic gradient descent training. Then, we design an approximation for the vanilla noise injection-based training method to reduce memory and improve computational efficiency. Next, we apply our proposed scheme to spiking neural networks and evaluate the performance of classification accuracy and robustness on MNIST and Fashion-MNIST datasets. Experiment results show that our proposed method achieves a much better performance on adversarial robustness and slightly better performance on original accuracy, compared with the conventional gradient-based training method

Metformin Uniquely Prevents Thrombosis by Inhibiting Platelet Activation and mtDNA Release

Thrombosis and its complications are the leading cause of death in patients with diabetes. Metformin, a first-line therapy for type 2 diabetes, is the only drug demonstrated to reduce cardiovascular complications in diabetic patients. However, whether metformin can effectively prevent thrombosis and its potential mechanism of action is unknown. Here we show, metformin prevents both venous and arterial thrombosis with no significant prolonged bleeding time by inhibiting platelet activation and extracellular mitochondrial DNA (mtDNA) release. Specifically, metformin inhibits mitochondrial complex I and thereby protects mitochondrial function, reduces activated platelet-induced mitochondrial hyperpolarization, reactive oxygen species overload and associated membrane damage. In mitochondrial function assays designed to detect amounts of extracellular mtDNA, we found that metformin prevents mtDNA release. This study also demonstrated that mtDNA induces platelet activation through a DC-SIGN dependent pathway. Metformin exemplifies a promising new class of antiplatelet agents that are highly effective at inhibiting platelet activation by decreasing the release of free mtDNA, which induces platelet activation in a DC-SIGN-dependent manner. This study has established a novel therapeutic strategy and molecular target for thrombotic diseases, especially for thrombotic complications of diabetes mellitus

Xanthohumol alleviates oxidative stress and impaired autophagy in experimental severe acute pancreatitis through inhibition of AKT/mTOR

Severe acute pancreatitis (SAP) is a lethal gastrointestinal disorder, yet no specific and effective treatment is available. Its pathogenesis involves inflammatory cascade, oxidative stress, and autophagy dysfunction. Xanthohumol (Xn) displays various medicinal properties,including anti-inflammation, antioxidative, and enhancing autophagic flux. However, it is unclear whether Xn inhibits SAP. This study investigated the efficacy of Xn on sodium taurocholate (NaT)-induced SAP (NaT-SAP) in vitro and in vivo. First, Xn attenuated biochemical and histopathological responses in NaT-SAP mice. And Xn reduced NaT-induced necrosis, inflammation, oxidative stress, and autophagy impairment. The mTOR activator MHY1485 and the AKT activator SC79 partly reversed the treatment effect of Xn. Overall, this is an innovative study to identify that Xn improved pancreatic injury by enhancing autophagic flux via inhibition of AKT/mTOR. Xn is expected to become a novel SAP therapeutic agent