Reducing Adversarial Training Cost with Gradient Approximation

Deep learning models have achieved state-of-the-art performances in various domains, while they are vulnerable to the inputs with well-crafted but small perturbations, which are named after adversarial examples (AEs). Among many strategies to improve the model robustness against AEs, Projected Gradient Descent (PGD) based adversarial training is one of the most effective methods. Unfortunately, the prohibitive computational overhead of generating strong enough AEs, due to the maximization of the loss function, sometimes makes the regular PGD adversarial training impractical when using larger and more complicated models. In this paper, we propose that the adversarial loss can be approximated by the partial sum of Taylor series. Furthermore, we approximate the gradient of adversarial loss and propose a new and efficient adversarial training method, adversarial training with gradient approximation (GAAT), to reduce the cost of building up robust models. Additionally, extensive experiments demonstrate that this efficiency improvement can be achieved without any or with very little loss in accuracy on natural and adversarial examples, which show that our proposed method saves up to 60\% of the training time with comparable model test accuracy on MNIST, CIFAR-10 and CIFAR-100 datasets.Comment: The experiments are insufficient, later will be updated. Withraw this manuscrip

The Mechanism of Na+/K+ Selectivity in Mammalian Voltage-Gated Sodium Channels Based on Molecular Dynamics Simulation

AbstractVoltage-gated sodium (Nav) channels and their Na+/K+ selectivity are of great importance in the mammalian neuronal signaling. According to mutational analysis, the Na+/K+ selectivity in mammalian Nav channels is mainly determined by the Lys and Asp/Glu residues located at the constriction site within the selectivity filter. Despite successful molecular dynamics simulations conducted on the prokaryotic Nav channels, the lack of Lys at the constriction site of prokaryotic Nav channels limits how much can be learned about the Na+/K+ selectivity in mammalian Nav channels. In this work, we modeled the mammalian Nav channel by mutating the key residues at the constriction site in a prokaryotic Nav channel (NavRh) to its mammalian counterpart. By simulating the mutant structure, we found that the Na+ preference in mammalian Nav channels is collaboratively achieved by the deselection from Lys and the selection from Asp/Glu within the constriction site

Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks

Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations. However, most defense mechanisms only consider a single type of perturbation while various attack methods might be adopted to perform stronger adversarial attacks against the deployed model in real-world scenarios, e.g., $\ell_2$ or $\ell_\infty$. Defending against various attacks can be a challenging problem since multi-perturbation adversarial training and its variants only achieve suboptimal robustness trade-offs, due to the theoretical limit to multi-perturbation robustness for a single model. Besides, it is impractical to deploy large models in some storage-efficient scenarios. To settle down these drawbacks, in this paper we propose a novel multi-perturbation adversarial training framework, parameter-saving adversarial training (PSAT), to reinforce multi-perturbation robustness with an advantageous side effect of saving parameters, which leverages hypernetworks to train specialized models against a single perturbation and aggregate these specialized models to defend against multiple perturbations. Eventually, we extensively evaluate and compare our proposed method with state-of-the-art single/multi-perturbation robust methods against various latest attack methods on different datasets, showing the robustness superiority and parameter efficiency of our proposed method, e.g., for the CIFAR-10 dataset with ResNet-50 as the backbone, PSAT saves approximately 80\% of parameters with achieving the state-of-the-art robustness trade-off accuracy.Comment: 9 pages, 2 figure

Screening of an individualized treatment strategy for an advanced gallbladder cancer using patient-derived tumor xenograft and organoid models

Gallbladder cancer is a highly aggressive malignancy with poor sensitivity to postoperative radiotherapy or chemotherapy; therefore, the development of individualized treatment strategies is paramount to improve patient outcomes. Both patient-derived tumor xenograft (PDX) and patient-derived tumor organoid (PDO) models derived from surgical specimens can better preserve the biological characteristics and heterogeneity of individual original tumors, display a unique advantage for individualized therapy and predicting clinical outcomes. In this study, PDX and PDO models of advanced gallbladder cancer were established, and the consistency of biological characteristics between them and primary patient samples was confirmed using pathological analysis and RNA-sequencing. Additionally, we tested the efficacy of chemotherapeutic drugs, targeted drugs, and immune checkpoint inhibitors using these two models. The results demonstrated that gemcitabine combined with cisplatin induced significant therapeutic effects. Furthermore, treatment with immune checkpoint inhibitors elicited promising responses in both the humanized mice and PDO immune models. Based on these results, gemcitabine combined with cisplatin was used for basic treatment, and immune checkpoint inhibitors were applied as a complementary intervention for gallbladder cancer. The patient responded well to treatment and exhibited a clearance of tumor foci. Our findings indicate that the combined use of PDO and PDX models can guide the clinical treatment course for gallbladder cancer patients to achieve individualized and effective treatment

Interactions between depositional regime and climate proxies in the northern South China Sea since the Last Glacial Maximum

Sedimentary deposits from the northern South China Sea (SCS) can provide important constraints on past changes in ocean currents and the East Asian summer monsoon (EASM) in this region. However, the interpretation of such records spanning the last deglaciation is complicated because sea-level change may also have influenced the depositional processes and patterns. Here, we present new records of grain size, clay mineralogy, and magnetic mineralogy spanning the past 24 kyr from both shallow and deep-water sediment cores in the northern SCS. Our multi-proxy comparison among multiple cores helps constrain the influence of sea-level change, providing confidence in interpreting the regional climate-forced signals. After accounting for the influence of sea-level change, we find that these multi-proxy records reflect a combination of changes in (a) the strength of the North Pacific Intermediate Water inflow, (b) the EASM strength, and (c) the Kuroshio Current extent. Overall, this study provides new insights into the roles of varying terrestrial weathering and oceanographic processes in controlling the depositional record on the northern SCS margin in response to climate and sea-level fluctuations

Optimum design and research on novel vehicle hybrid excitation synchronous generator

Hybrid excitation is an organic combination of permanent magnet excitation and electric excitation. Hybrid excitation synchronous generator (HESG) both has the advantages of light quality, less losses and high efficiency like permanent magnet generator and the advantages of good magnetic field adjusting performance like electric excitation generator, so it is very suitable for the vehicle application. This paper presented a novel vehicle HESG which has skew stator core, permanent magnet rotor and both armature winding and field winding in the stator. Using ANSYS software, simulating the electric excitation field and the magnetic field, and finally the main parameters of HESG were designed. The simulation and the test results both show that the novel vehicle PMSG has the advantages of small cogging torque, high efficiency, small harmonic component output voltage and low waveform aberration, so as to meet the design requirements fully

Anemia is a risk factor for rapid eGFR decline in type 2 diabetes

ObjectiveTo investigate the association between anemia and progression of diabetic kidney disease (DKD) in type 2 diabetes.MethodsThis was a retrospective study. A total of 2570 in-patients with type 2 diabetes hospitalized in Jinan branch of Huashan hospital from January 2013 to October 2017 were included, among whom 526 patients were hospitalized ≥ 2 times with a median follow-up period of 2.75 years. Annual rate of eGFR decline was calculated in patients with multiple admissions. A rate of eGFR decline exceeding -5 ml/min per 1.73 m2 per year was defined as rapid eGFR decline. The prevalence of DKD and clinical characteristics were compared between anemia and non-anemia patients. Correlation analysis was conducted between anemia and clinical parameters. Comparison of clinical features were carried out between rapid eGFR decline and slow eGFR decline groups. The risk factors for rapid DKD progression were analyzed using logistic regression analysis.ResultsThe prevalence of anemia was 28.2% among the 2570 diabetic patients, while in patients with DKD, the incidence of anemia was 37.8%. Patients with anemia had greater prevalence of DKD, higher levels of urinary albumin-to-creatinine ratio (UACR), serum creatinine, BUN, urine α1-MG, urine β2-MG, urine NAG/Cr, hsCRP, Cystatin C, homocysteine and lower eGFR, as compared to the patients without anemia. Anemia was correlated with age, UACR, eGFR, urinary NAG/Cr, hsCRP and diabetic retinopathy (DR). Logistic regression analysis of 526 patients with type 2 diabetes during the follow-up period showed that anemia was an independent risk factor for rapid eGFR decline.ConclusionAnemia is associated with worse renal function and is an independent risk factor for rapid eGFR decline in type 2 diabetes