Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call "fooling images" (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.Comment: To appear at CVPR 201

Jig Twist Optimization of Mach 0.745 Transonic Truss-Braced Wing Aircraft and High-Fidelity CFD Validation

This paper presents a jig twist optimization study of Mach 0.745 Transonic Truss-Braced Wing (TTBW) aircraft using an in-house developed aero-structural analysis solver VSPAERO coupled to BEAM3D. A vortex-lattice model of the TTBW model is developed, and a transonic and viscous flow correction method is implemented in the VSPAERO model to account for transonic and viscous flow effects. A correction method for the wing-strut interference aerodynamics is developed and applied to the VSPAERO solver. Also, a structural dynamic finite-element model of the TTBW aircraft is developed. This finite-element model includes the geometric nonlinear effect due to the tension in the struts which causes a deflection-dependent nonlinear stiffness. The VSPAERO model is coupled to the corresponding finite-element model to provide a rapid aero-structural analysis. A design flight condition corresponding to Mach 0.745 at 42000 ft is selected for the TTBW aircraft jig twist optimization to reduce the drag coefficient. After the design is implemented, the drag coefficient of the twist optimized TTBW aircraft is reduced about 8 counts. At the end, a high-fidelity CFD solver FUN3D is used to validate the design

Jig Twist Optimization of Mach 0.745 Transonic Truss-Braced Wing Aircraft and High-Fidelity CFD Validation

This paper presents a jig twist optimization study of Mach 0.745 Transonic Truss-Braced Wing (TTBW) aircraft using an in-house developed aero-structural analysis solver VSPAERO coupled to BEAM3D. A vortex-lattice model of the TTBW model is developed, and a transonic and viscous flow correction method is implemented in the VSPAERO model to account for transonic and viscous flow effects. A correction method for the wing-strut interference aerodynamics is developed and applied to the VSPAERO solver. Also, a structural dynamic finite-element model of the TTBW aircraft is developed. This finite-element model includes the geometric nonlinear effect due to the tension in the struts which causes a deflection-dependent nonlinear stiffness. The VSPAERO model is coupled to the corresponding finite-element model to provide a rapid aero-structural analysis. A flight condition corresponding to Mach 0.745 at 42000 ft is selected for the TTBW aircraft jig twist optimization to reduce the drag coefficient. After the design is implemented, the drag coefficient of the twist optimized TTBW aircraft is reduced about 8 counts. At the end, a high-fidelity CFD solver FUN3D is used to validate the design

Formation of matter-wave soliton trains by modulational instability

Nonlinear systems can exhibit a rich set of dynamics that are inherently sensitive to their initial conditions. One such example is modulational instability, which is believed to be one of the most prevalent instabilities in nature. By exploiting a shallow zero-crossing of a Feshbach resonance, we characterize modulational instability and its role in the formation of matter-wave soliton trains from a Bose-Einstein condensate. We examine the universal scaling laws exhibited by the system, and through real-time imaging, address a long-standing question of whether the solitons in trains are created with effectively repulsive nearest neighbor interactions, or rather, evolve into such a structure

The effect of a diet supplemented with flaxseed oil on the lipid content and fatty acid profile of rainbow trout (Oncorhynchus mykiss) muscle tissue

This experiment examined the effect of nine diets supplemented with varied combinations of flaxseed oil and vitamin E on the total lipid content and fatty acid profile of rainbow trout over four months. Mean values were tested for significant differences between groups using one-way analyses of variance.;The most flaxseed oil supplementation, 23.5%, resulted in the highest concentration of total omega-3 fat in fillets (P\u3c0.05). This was due to the increased (P\u3c0.05) concentration of alpha-linolenic acid (ALA). The 23.5% flaxseed oil trout had the lowest (P\u3c0.05) proportion of saturated fatty acids and the highest amount of total unsaturated fatty acid. Unexpectedly, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) contents significantly decreased as the experiment progressed. No significant differences were seen among total fat contents. The results indicate that feeding a diet supplemented with flaxseed oil and vitamin E produces omega-3 enhanced trout with higher ALA but lower EPA and DHA