312 research outputs found
Manipulation of Abro1 Localization in U2OS Cells
https://openworks.mdanderson.org/sumexp22/1142/thumbnail.jp
Experimental Study on the Factors of the Oil Shale Thermal Breakdown in High-Voltage Power Frequency Electric Heating Technology
We conducted an experimental study on the breakdown process of oil shale by high-voltage power frequency electric heating in-situ pyrolyzing (HVF) technology to examine the impact mechanisms of the electric field intensity, initial temperature, and moisture content on a breakdown, using Huadian oil shale samples. A thermal breakdown occurred when the electric field intensity was between 100 and 180 V/cm. The greater the electric field intensity, the easier the thermal breakdown and the lower the energy consumption. The critical temperature of the oil shale thermal breakdown ranged from 93 to 102 °C. A higher initial temperature increases the difficulty of breakdown, which is inconsistent with the classical theory of a solid thermal breakdown. The main factor that affects the electrical conductivity of oil shale is the presence of water, which is also a necessary condition for the thermal breakdown of oil shale. There should be an optimal moisture content that minimizes both the breakdown time and energy consumption for oil shale’s thermal breakdown. The thermal breakdown of oil shale results from heat generation and dissipation. The electric field intensity only affects the heat generation process, whereas the initial temperature and moisture content impact both the heat generation and dissipation processes, and the impacts of moisture content are greater than those of the initial temperature.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World
Deep neural networks (DNN) have been widely deployed in various applications.
However, many researches indicated that DNN is vulnerable to backdoor attacks.
The attacker can create a hidden backdoor in target DNN model, and trigger the
malicious behaviors by submitting specific backdoor instance. However, almost
all the existing backdoor works focused on the digital domain, while few
studies investigate the backdoor attacks in real physical world. Restricted to
a variety of physical constraints, the performance of backdoor attacks in the
real physical world will be severely degraded. In this paper, we propose a
robust physical backdoor attack method, PTB (physical transformations for
backdoors), to implement the backdoor attacks against deep learning models in
the real physical world. Specifically, in the training phase, we perform a
series of physical transformations on these injected backdoor instances at each
round of model training, so as to simulate various transformations that a
backdoor may experience in real world, thus improves its physical robustness.
Experimental results on the state-of-the-art face recognition model show that,
compared with the backdoor methods that without PTB, the proposed attack method
can significantly improve the performance of backdoor attacks in real physical
world. Under various complex physical conditions, by injecting only a very
small ratio (0.5%) of backdoor instances, the attack success rate of physical
backdoor attacks with the PTB method on VGGFace is 82%, while the attack
success rate of backdoor attacks without the proposed PTB method is lower than
11%. Meanwhile, the normal performance of the target DNN model has not been
affected
Quantitative and functional post-translational modification proteomics reveals that TREPH1 plays a role in plant thigmomorphogenesis
Plants can sense both intracellular and extracellular mechanical forces and
can respond through morphological changes. The signaling components responsible
for mechanotransduction of the touch response are largely unknown. Here, we
performed a high-throughput SILIA (stable isotope labeling in
Arabidopsis)-based quantitative phosphoproteomics analysis to profile changes
in protein phosphorylation resulting from 40 seconds of force stimulation in
Arabidopsis thaliana. Of the 24 touch-responsive phosphopeptides identified,
many were derived from kinases, phosphatases, cytoskeleton proteins, membrane
proteins and ion transporters. TOUCH-REGULATED PHOSPHOPROTEIN1 (TREPH1) and MAP
KINASE KINASE 2 (MKK2) and/or MKK1 became rapidly phosphorylated in
touch-stimulated plants. Both TREPH1 and MKK2 are required for touch-induced
delayed flowering, a major component of thigmomorphogenesis. The treph1-1 and
mkk2 mutants also exhibited defects in touch-inducible gene expression. A
non-phosphorylatable site-specific isoform of TREPH1 (S625A) failed to restore
touch-induced flowering delay of treph1-1, indicating the necessity of S625 for
TREPH1 function and providing evidence consistent with the possible functional
relevance of the touch-regulated TREPH1 phosphorylation. Bioinformatic analysis
and biochemical subcellular fractionation of TREPH1 protein indicate that it is
a soluble protein. Altogether, these findings identify new protein players in
Arabidopsis thigmomorphogenesis regulation, suggesting that protein
phosphorylation may play a critical role in plant force responses
Graph Condensation for Graph Neural Networks
Given the prevalence of large-scale graphs in real-world applications, the
storage and time for training neural models have raised increasing concerns. To
alleviate the concerns, we propose and study the problem of graph condensation
for graph neural networks (GNNs). Specifically, we aim to condense the large,
original graph into a small, synthetic and highly-informative graph, such that
GNNs trained on the small graph and large graph have comparable performance. We
approach the condensation problem by imitating the GNN training trajectory on
the original graph through the optimization of a gradient matching loss and
design a strategy to condense node futures and structural information
simultaneously. Extensive experiments have demonstrated the effectiveness of
the proposed framework in condensing different graph datasets into informative
smaller graphs. In particular, we are able to approximate the original test
accuracy by 95.3% on Reddit, 99.8% on Flickr and 99.0% on Citeseer, while
reducing their graph size by more than 99.9%, and the condensed graphs can be
used to train various GNN architectures.Comment: 16 pages, 4 figure
Variation of culturable bacteria along depth in the East Rongbuk ice core, Mt. Everest
AbstractIce melt water from a 22.27 m ice core which was drilled from the East Rongbuk Glacier, Mt. Everest was incubation in two incubation ways: plate melt water directly and enrichment melt water prior plate, respectively. The abundance of cultivable bacteria ranged from 0–295 CFU mL−1 to 0–1720 CFU mL−1 in two incubations with a total of 1385 isolates obtained. Comparing to direct cultivation, enrichment cultivation recovered more bacteria. Pigment-producing bacteria accounted for an average of 84.9% of total isolates. Such high percentage suggested that pigment production may be an adaptive physiological feature for the bacteria in ice core to cope with strong ultraviolet radiation on the glacier. The abundances of cultivable bacteria and pigment-producing isolates varied synchronously along depth: higher abundance in the middle and lower at the top and bottom. It indicated that the middle part of the ice core was hospitable for the microbial survival. Based on the physiological properties of the colonies, eighty-nine isolates were selected for phylogenetic analysis. Obtained 16S rRNA gene sequences fell into four groups: Firmicutes, Alpha-Proteobacteria, Gamma-Proteobacteria, and Actinobacteria, with the Firmicutes being dominant. Microbial compositions derived from direct and enrichment cultivations were not overlapped. We suggest that it is a better way to explore the culturable microbial diversity in ice core by combining the approaches of both direct and enrichment cultivation
Detect and remove watermark in deep neural networks via generative adversarial networks
Deep neural networks (DNN) have achieved remarkable performance in various
fields. However, training a DNN model from scratch requires a lot of computing
resources and training data. It is difficult for most individual users to
obtain such computing resources and training data. Model copyright infringement
is an emerging problem in recent years. For instance, pre-trained models may be
stolen or abuse by illegal users without the authorization of the model owner.
Recently, many works on protecting the intellectual property of DNN models have
been proposed. In these works, embedding watermarks into DNN based on backdoor
is one of the widely used methods. However, when the DNN model is stolen, the
backdoor-based watermark may face the risk of being detected and removed by an
adversary. In this paper, we propose a scheme to detect and remove watermark in
deep neural networks via generative adversarial networks (GAN). We demonstrate
that the backdoor-based DNN watermarks are vulnerable to the proposed GAN-based
watermark removal attack. The proposed attack method includes two phases. In
the first phase, we use the GAN and few clean images to detect and reverse the
watermark in the DNN model. In the second phase, we fine-tune the watermarked
DNN based on the reversed backdoor images. Experimental evaluations on the
MNIST and CIFAR10 datasets demonstrate that, the proposed method can
effectively remove about 98% of the watermark in DNN models, as the watermark
retention rate reduces from 100% to less than 2% after applying the proposed
attack. In the meantime, the proposed attack hardly affects the model's
performance. The test accuracy of the watermarked DNN on the MNIST and the
CIFAR10 datasets drops by less than 1% and 3%, respectively
SocialGuard: An Adversarial Example Based Privacy-Preserving Technique for Social Images
The popularity of various social platforms has prompted more people to share
their routine photos online. However, undesirable privacy leakages occur due to
such online photo sharing behaviors. Advanced deep neural network (DNN) based
object detectors can easily steal users' personal information exposed in shared
photos. In this paper, we propose a novel adversarial example based
privacy-preserving technique for social images against object detectors based
privacy stealing. Specifically, we develop an Object Disappearance Algorithm to
craft two kinds of adversarial social images. One can hide all objects in the
social images from being detected by an object detector, and the other can make
the customized sensitive objects be incorrectly classified by the object
detector. The Object Disappearance Algorithm constructs perturbation on a clean
social image. After being injected with the perturbation, the social image can
easily fool the object detector, while its visual quality will not be degraded.
We use two metrics, privacy-preserving success rate and privacy leakage rate,
to evaluate the effectiveness of the proposed method. Experimental results show
that, the proposed method can effectively protect the privacy of social images.
The privacy-preserving success rates of the proposed method on MS-COCO and
PASCAL VOC 2007 datasets are high up to 96.1% and 99.3%, respectively, and the
privacy leakage rates on these two datasets are as low as 0.57% and 0.07%,
respectively. In addition, compared with existing image processing methods (low
brightness, noise, blur, mosaic and JPEG compression), the proposed method can
achieve much better performance in privacy protection and image visual quality
maintenance
- …