Vibrational spectroscopy at electrolyte/electrode interfaces with graphene gratings.

Microscopic understanding of physical and electrochemical processes at electrolyte/electrode interfaces is critical for applications ranging from batteries, fuel cells to electrocatalysis. However, probing such buried interfacial processes is experimentally challenging. Infrared spectroscopy is sensitive to molecule vibrational signatures, yet to approach the interface three stringent requirements have to be met: interface specificity, sub-monolayer molecular detection sensitivity, and electrochemically stable and infrared transparent electrodes. Here we show that transparent graphene gratings electrode provide an attractive platform for vibrational spectroscopy at the electrolyte/electrode interfaces: infrared diffraction from graphene gratings offers enhanced detection sensitivity and interface specificity. We demonstrate the vibrational spectroscopy of methylene group of adsorbed sub-monolayer cetrimonium bromide molecules and reveal a reversible field-induced electrochemical deposition of cetrimonium bromide on the electrode controlled by the bias voltage. Such vibrational spectroscopy with graphene gratings is promising for real time and in situ monitoring of different chemical species at the electrolyte/electrode interfaces

ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector

Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) image classifier, as demonstrated in prior work. In this work, we propose ShapeShifter, an attack that tackles the more challenging problem of crafting physical adversarial perturbations to fool image-based object detectors like Faster R-CNN. Attacking an object detector is more difficult than attacking an image classifier, as it needs to mislead the classification results in multiple bounding boxes with different scales. Extending the digital attack to the physical world adds another layer of difficulty, because it requires the perturbation to be robust enough to survive real-world distortions due to different viewing distances and angles, lighting conditions, and camera limitations. We show that the Expectation over Transformation technique, which was originally proposed to enhance the robustness of adversarial perturbations in image classification, can be successfully adapted to the object detection setting. ShapeShifter can generate adversarially perturbed stop signs that are consistently mis-detected by Faster R-CNN as other objects, posing a potential threat to autonomous vehicles and other safety-critical computer vision systems

Robust Principles: Architectural Design Principles for Adversarially Robust CNNs

Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs. To accomplish our goal, we synthesize a suite of three generalizable robust architectural design principles: (a) optimal range for depth and width configurations, (b) preferring convolutional over patchify stem stage, and (c) robust residual block design through adopting squeeze and excitation blocks and non-parametric smooth activation functions. Through extensive experiments across a wide spectrum of dataset scales, adversarial training methods, model parameters, and network design spaces, our principles consistently and markedly improve AutoAttack accuracy: 1-3 percentage points (pp) on CIFAR-10 and CIFAR-100, and 4-9 pp on ImageNet. The code is publicly available at https://github.com/poloclub/robust-principles.Comment: Published at BMVC'2

Environmental surveillance as a tool for identifying high-risk settings for typhoid transmission

Enteric fever remains a major cause of morbidity in developing countries with poor sanitation conditions that enable fecal contamination of water distribution systems. Historical evidence has shown that contamination of water systems used for household consumption or agriculture are key transmission routes for Salmonella Typhi and Salmonella Paratyphi A. The World Health Organization now recommends that typhoid conjugate vaccines (TCV) be used in settings with high typhoid incidence; consequently, governments face a challenge regarding how to prioritize typhoid against other emerging diseases. A key issue is the lack of typhoid burden data in many low- and middle-income countries where TCV could be deployed. Here we present an argument for utilizing environmental sampling for the surveillance of enteric fever organisms to provide data on community-level typhoid risk. Such an approach could complement traditional blood culture-based surveillance or even replace it in settings where population-based clinical surveillance is not feasible. We review historical studies characterizing the transmission of enteric fever organisms through sewage and water, discuss recent advances in the molecular detection of typhoidal Salmonella in the environment, and outline challenges and knowledge gaps that need to be addressed to establish environmental sampling as a tool for generating actionable data that can inform public health responses to enteric fever