You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle

Deep learning achieves state-of-the-art results in many tasks in computer vision and natural language processing. However, recent works have shown that deep networks can be vulnerable to adversarial perturbations, which raised a serious robustness issue of deep networks. Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks. A major drawback of existing adversarial training algorithms is the computational overhead of the generation of adversarial examples, typically far greater than that of the network training. This leads to the unbearable overall computational cost of adversarial training. In this paper, we show that adversarial training can be cast as a discrete time differential game. Through analyzing the Pontryagin's Maximal Principle (PMP) of the problem, we observe that the adversary update is only coupled with the parameters of the first layer of the network. This inspires us to restrict most of the forward and back propagation within the first layer of the network during adversary updates. This effectively reduces the total number of full forward and backward propagation to only one for each group of adversary updates. Therefore, we refer to this algorithm YOPO (You Only Propagate Once). Numerical experiments demonstrate that YOPO can achieve comparable defense accuracy with approximately 1/5 ~ 1/4 GPU time of the projected gradient descent (PGD) algorithm. Our codes are available at https://https://github.com/a1600012888/YOPO-You-Only-Propagate-Once.Comment: Accepted as a conference paper at NeurIPS 201

Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection

Adversarial attacks in the physical world can harm the robustness of detection models. Evaluating the robustness of detection models in the physical world can be challenging due to the time-consuming and labor-intensive nature of many experiments. Thus, virtual simulation experiments can provide a solution to this challenge. However, there is no unified detection benchmark based on virtual simulation environment. To address this challenge, we proposed an instant-level data generation pipeline based on the CARLA simulator. Using this pipeline, we generated the DCI dataset and conducted extensive experiments on three detection models and three physical adversarial attacks. The dataset covers 7 continuous and 1 discrete scenes, with over 40 angles, 20 distances, and 20,000 positions. The results indicate that Yolo v6 had strongest resistance, with only a 6.59% average AP drop, and ASA was the most effective attack algorithm with a 14.51% average AP reduction, twice that of other algorithms. Static scenes had higher recognition AP, and results under different weather conditions were similar. Adversarial attack algorithm improvement may be approaching its 'limitation'.Comment: CVPR 2023 worksho

Beyond apples and pears: sex-specific genetics of body fat percentage

IntroductionBiological sex influences both overall adiposity and fat distribution. Further, testosterone and sex hormone binding globulin (SHBG) influence adiposity and metabolic function, with differential effects of testosterone in men and women. Here, we aimed to perform sex-stratified genome-wide association studies (GWAS) of body fat percentage (BFPAdj) (adjusting for testosterone and sex hormone binding globulin (SHBG)) to increase statistical power.MethodsGWAS were performed in white British individuals from the UK Biobank (157,937 males and 154,337 females). To avoid collider bias, loci associated with SHBG or testosterone were excluded. We investigated association of BFPAdj loci with high density cholesterol (HDL), triglyceride (TG), type 2 diabetes (T2D), coronary artery disease (CAD), and MRI-derived abdominal subcutaneous adipose tissue (ASAT), visceral adipose tissue (VAT) and gluteofemoral adipose tissue (GFAT) using publicly available data from large GWAS. We also performed 2-sample Mendelian Randomization (MR) using identified BFPAdj variants as instruments to investigate causal effect of BFPAdj on HDL, TG, T2D and CAD in males and females separately.ResultsWe identified 195 and 174 loci explaining 3.35% and 2.60% of the variation in BFPAdj in males and females, respectively at genome-wide significance (GWS, p<5x10-8). Although the direction of effect at these loci was generally concordant in males and females, only 38 loci were common to both sexes at GWS. Seven loci in males and ten loci in females have not been associated with any adiposity/cardiometabolic traits previously. BFPAdj loci generally did not associate with cardiometabolic traits; several had paradoxically beneficial cardiometabolic effects with favourable fat distribution. MR analyses did not find convincing supportive evidence that increased BFPAdj has deleterious cardiometabolic effects in either sex with highly significant heterogeneity.ConclusionsThere was limited genetic overlap between BFPAdj in males and females at GWS. BFPAdj loci generally did not have adverse cardiometabolic effects which may reflect the effects of favourable fat distribution and cardiometabolic risk modulation by testosterone and SHBG

Phase-field simulation of grain nucleation, growth, and Rayleigh distribution of U3Si2 nuclear fuel

U3Si2 is a potential accident-tolerant fuel (ATF) due to its high thermal conductivity and uranium density relative to UO2. The grain size and distribution play an essential role in the service performance of U3Si2. However, the grain evolution is quite complicated and remains unclear, which limits further application of U3Si2 in the ATF assembly. In the present work, a phase-field model is employed to investigate the nucleation and growth of grains in U3Si2. Our results show that the number of grains rises rapidly at the nucleation stage until they occupy the whole system. After that, the grain radius and area continue to grow, and the grain number decays. The grain area increases in time according to the linear law, while the mean grain radius increases with time in a power law form with the scaling growth exponent z = 0.42, which is quite close to the theoretically predicted value. Finally, we performed statistical analysis and found that the grain size evolution of U3Si2 obeys Rayleigh distribution. Our simulation not only elucidates the nucleation and evolution of grains in U3Si2 during the thermal treatment process unambiguously but also provides a fundamental study on the investigation of grain growth, subdivision, and even amorphization in the irradiated condition, which is very important for U3Si2 used as ATF in the light water reactor

Development of risk prediction models for depression combining genetic and early life risk factors

BackgroundBoth genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated.MethodsLeveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression.ResultsIn the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14–1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05–1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively.ConclusionWe have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression

Modeling and Simulation of Radiation Doses and Nuclide Distributions in the Mu2e Experimental Hall with MARS15

Mu2e is an experiment at Fermilab that will investigate the neutrino-less conversion of muons to electrons. The necessary muons will be generated by a relativistic proton beam hitting a tungsten target, which is expected to result in radiological hazards in the experimental hall. To model the emission of radioactive particles, we developed an algorithm in C++ as an extension to MARS15, a Monte Carlo code system that can simulate transport and interactions of particles. The algorithm is split into two stages. In the first stage, MARS15 generates nuclide inventories for regions in the model of the experimental hall. In the second stage, the Decay and Transmutation add-on to MARS15 adjusts these inventories for radioactive decay, and the resultant inventories are used to simulate the emission of gamma rays from regions in the model. Our results showed that the radiation levels in the remote handling room are only slightly above natural background levels, while those between the Production Solenoid and beam dump ranged from 100 to 10000 millirem per hour. Our algorithm may be adopted as a standard feature of MARS15 and assist other users in calculating residual doses in models of experiments in particle physics