Adversarial Training for Free!

Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters. Our "free" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks. The code is available at https://github.com/ashafahi/free_adv_train.Comment: Accepted to NeurIPS 201

Design criteria of subway tunnels

This paper is dedicated to present criteria and rules for design of metro path including tunnel geometrical sections and choosing parameters, loads applied to tunnels, fundamentals of static and seismic analysis, and primaries of structural design, structural joints, and control criteria, behavior measuring and sensitive devices. Design criteria of shallow and semi-deep tunnels are presented in this paper

Investigating on Hydrodynamic Behavior of Slotted Breakwater Walls Under Sea Waves

Breakwater walls are buildings that are built to prevent the collapse of the soil or other granular materials and the safety of the sea. One of the destructive phenomena in these structures is the impact of sea wave forces on the overturning phenomenon and instability of the coastal wall, which has damaged the structures existing on these sites. The pattern of interaction between water and seas is complex in coastal structures. In this research, the influence of the different wall heights and soil type changes on wall stability and water pressure distribution in the coastal wall have been investigated. Also, studies will be done on the investigation and optimization of the wall and Finally, by comparing the results obtained with classical methods, the strengths and weaknesses of the classical methods have been analyzed and the effectiveness of these methods (classical) has been evaluated. These walls are made in two types of weighted and flexible (mainly metal) types, in which flexible performance is considered in this research. The behavior of metal shields in front of the water will be examined using the ANSYS software. Several methods for calculating wave forces on perforated coastal walls are also reviewed. In this study, the behavior of the elastic wall is assumed. Coastal walls have been investigated in different hardships and the distribution of pressure and anchor due to hydrodynamic pressure of water on the wall have been investigated. The walls are different in terms of material and amount of rigidity

Uncertainty quantification of granular computing‑neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams

Discharge of pollution loads into natural water systems remains a global challenge that threatens water and food supply, as well as endangering ecosystem services. Natural rehabilitation of contaminated streams is mainly influenced by the longitudinal dispersion coefficient, or the rate of longitudinal dispersion (Dx), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of Dx in streams limits the water quality assessment in natural streams and design of water quality enhancement strategies. This study develops an artificial intelligence-based predictive model, coupling granular computing and neural network models (GrC-ANN) to provide robust estimation of Dx and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of Dx estimated from the proposed GrC-ANN model was performed by alteration of the training data used to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a global database of tracer experiments in streams with 503 datapoints. Comparison between the Dx values estimated by GrC-ANN to those determined from tracer measurements shows the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. The GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth-factor = 0.56) that brackets the highest percentage of true Dx data (i.e., 100%) is the best model to compute Dx in streams. Considering the significant inherent uncertainty reported in the previous Dx models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing (Dx) in turbulent environmental flow systems