Defense against Universal Adversarial Perturbations

Recent advances in Deep Learning show the existence of image-agnostic quasi-imperceptible perturbations that when applied to `any' image can fool a state-of-the-art network classifier to change its prediction about the image label. These `Universal Adversarial Perturbations' pose a serious threat to the success of Deep Learning in practice. We present the first dedicated framework to effectively defend the networks against such perturbations. Our approach learns a Perturbation Rectifying Network (PRN) as `pre-input' layers to a targeted model, such that the targeted model needs no modification. The PRN is learned from real and synthetic image-agnostic perturbations, where an efficient method to compute the latter is also proposed. A perturbation detector is separately trained on the Discrete Cosine Transform of the input-output difference of the PRN. A query image is first passed through the PRN and verified by the detector. If a perturbation is detected, the output of the PRN is used for label prediction instead of the actual image. A rigorous evaluation shows that our framework can defend the network classifiers against unseen adversarial perturbations in the real-world scenarios with up to 97.5% success rate. The PRN also generalizes well in the sense that training for one targeted network defends another network with a comparable success rate.Comment: Accepted in IEEE CVPR 201

Evolution of midplate hotspot swells: Numerical solutions

The evolution of midplate hotspot swells on an oceanic plate moving over a hot, upwelling mantle plume is numerically simulated. The plume supplies a Gaussian-shaped thermal perturbation and thermally-induced dynamic support. The lithosphere is treated as a thermal boundary layer with a strongly temperature-dependent viscosity. The two fundamental mechanisms of transferring heat, conduction and convection, during the interaction of the lithosphere with the mantle plume are considered. The transient heat transfer equations, with boundary conditions varying in both time and space, are solved in cylindrical coordinates using the finite difference ADI (alternating direction implicit) method on a 100 x 100 grid. The topography, geoid anomaly, and heat flow anomaly of the Hawaiian swell and the Bermuda rise are used to constrain the models. Results confirm the conclusion of previous works that the Hawaiian swell can not be explained by conductive heating alone, even if extremely high thermal perturbation is allowed. On the other hand, the model of convective thinning predicts successfully the topography, geoid anomaly, and the heat flow anomaly around the Hawaiian islands, as well as the changes in the topography and anomalous heat flow along the Hawaiian volcanic chain

Spectral tracing of deuterium for imaging glucose metabolism.

Cells and tissues often display pronounced spatial and dynamical metabolic heterogeneity. Common glucose-imaging techniques report glucose uptake or catabolism activity, yet do not trace the functional utilization of glucose-derived anabolic products. Here we report a microscopy technique for the optical imaging, via the spectral tracing of deuterium (STRIDE), of diverse macromolecules derived from glucose. Based on stimulated Raman-scattering imaging, STRIDE visualizes the metabolic dynamics of newly synthesized macromolecules, such as DNA, protein, lipids and glycogen, via the enrichment and distinct spectra of carbon-deuterium bonds transferred from the deuterated glucose precursor. STRIDE can also use spectral differences derived from different glucose isotopologues to visualize temporally separated glucose populations using a pulse-chase protocol. We also show that STRIDE can be used to image glucose metabolism in many mouse tissues, including tumours, brain, intestine and liver, at a detection limit of 10 mM of carbon-deuterium bonds. STRIDE provides a high-resolution and chemically informative assessment of glucose anabolic utilization

Microlensing effects of wormholes associated to blackhole spacetimes

In this paper, we investigate the microlensing effects of wormholes associated to black hole spacetimes. Specifically, we work on three typical wormholes (WH): Schwarzschild WH, Kerr WH, and RN WH, as well as their blackhole correspondences. We evaluate the deflection angle upon the second order under weak field approximation using Gauss-Bonnet theorem. Then, we study their magnification with numerics. We find that the prograde case of Kerr-like metric could lead to multi-peaks of magnification when the mass part is compatible with the charge part. Moreover, the first two gentle peaks of Kerr BH are larger than the WH case by one order of magnitude, while the main peak of Kerr BHs and WHs are of the same order. For other cases, the behavior of magnification from wormholes and their corresponding blackholes is similar. Our result may shed new light on exploring compact objects through the microlensing effect.Comment: Figures are improved, discussions are improve