829 research outputs found
Development of Field-deployable Nucleic Acid Testing Platforms
This thesis is focused on the development of field-deployable nucleic acid testing platforms to allowed rapid detection and quantification of nucleic acids. Two distinct platforms suitable for nucleic acid testing in resource-limited settings were developed. First, a paper-based diagnostic device was developed. The principle of this paper-based device was based on the unique interfacial interaction of DNA and the DNA intercalating dye with cellulose on chromatographic paper. Second, a colorimetric reader was developed. The principle of the reader was based on measuring the absorbance change of a chromogenic substrate which is triggered by DNA and DNA intercalating dyes under light illumination. The performance of both devices was tested using synthetic DNA, nucleic acid amplicons, and actual parasites nucleic acid samples collected from school-age children in rural areas of Honduras
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Neural networks are vulnerable to adversarial examples, which poses a threat
to their application in security sensitive systems. We propose high-level
representation guided denoiser (HGD) as a defense for image classification.
Standard denoiser suffers from the error amplification effect, in which small
residual adversarial noise is progressively amplified and leads to wrong
classifications. HGD overcomes this problem by using a loss function defined as
the difference between the target model's outputs activated by the clean image
and denoised image. Compared with ensemble adversarial training which is the
state-of-the-art defending method on large images, HGD has three advantages.
First, with HGD as a defense, the target model is more robust to either
white-box or black-box adversarial attacks. Second, HGD can be trained on a
small subset of the images and generalizes well to other images and unseen
classes. Third, HGD can be transferred to defend models other than the one
guiding it. In NIPS competition on defense against adversarial attacks, our HGD
solution won the first place and outperformed other models by a large margin
Free electron emission in vacuum assisted by photonic time crystals
The Cerenkov radiation and the Smith-Purcell effect state that free electron
emission occurs exclusively in dielectrics when the velocity of the particles
exceeds the speed of light in the medium or in the vicinity of periodic
gratings close to each other within a vacuum. We demonstrate that free
electrons in a vacuum can also emit highly directional monochromatic waves when
they are in close proximity to a medium that is periodically modulated
temporally, suggesting the existence of temporal Smith-Purcell effect. The
momentum band gaps of time-varying media, such as photonic time crystals
(PTCs), create new pathways for the injection of external energy, allowing the
frequency, intensity, and spatial distribution of the electromagnetic fields to
be controlled. Moreover, the PTC substrate enables the conversion of localized
evanescent fields into amplified, highly directional propagating plane waves
that are only sensitive to the velocity of particles and the modulation
frequency, which allows us to observe and utilize Cerenkov-like radiation in
free space. Our work exhibits significant opportunities for the utilization of
time-varying structures in various fields, including particle identification,
ultraweak signal detection, and improved radiation source design
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks
Transfer-based adversarial attacks can effectively evaluate model robustness
in the black-box setting. Though several methods have demonstrated impressive
transferability of untargeted adversarial examples, targeted adversarial
transferability is still challenging. The existing methods either have low
targeted transferability or sacrifice computational efficiency. In this paper,
we develop a simple yet practical framework to efficiently craft targeted
transfer-based adversarial examples. Specifically, we propose a conditional
generative attacking model, which can generate the adversarial examples
targeted at different classes by simply altering the class embedding and share
a single backbone. Extensive experiments demonstrate that our method improves
the success rates of targeted black-box attacks by a significant margin over
the existing methods -- it reaches an average success rate of 29.6\% against
six diverse models based only on one substitute white-box model in the standard
testing of NeurIPS 2017 competition, which outperforms the state-of-the-art
gradient-based attack methods (with an average success rate of 2\%) by a
large margin. Moreover, the proposed method is also more efficient beyond an
order of magnitude than gradient-based methods
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