336 research outputs found
Expression and Promoter Analysis of Six Heat Stress-Inducible Genes in Rice
During the long evolutionary process, plant gradually formed a series of strategies and mechanisms to cope with stress environment such as drought, heat, cold, and high salinity. Six highly heat responsive genes were identified in rice by microarray data analysis. The qRT-PCR analysis confirmed that the expression of these six genes were highly heat inducible and moderately responded to salt stress, polyethylene glycol, and abscisic acid treatment, but little affected by cold treatment. Promoters of the three highly heat-inducible genes (OsHsfB2cp, PM19p, and Hsp90p) were used to drive GUS gene expression in rice. The results of the GUS gene expression, histochemical staining, and GUS activities in panicles and flag leaves of the transgenic rice plants confirmed high heat-induced GUS activities and moderate drought-induced activities. The three promoters exhibited similar high activity lever in rice leaf under heat, but OsHsfB2cp and PM19p showed much higher activities in panicles under heat stress. Our work confirmed that the OsHsfB2c and PM19 promoters were highly heat inducible and further characterization and reconstruction of cis-elements in their promoters could lead to the development of highly effective heat-inducible promoters for plant genetic engineering
HFORD: High-Fidelity and Occlusion-Robust De-identification for Face Privacy Protection
With the popularity of smart devices and the development of computer vision
technology, concerns about face privacy protection are growing. The face
de-identification technique is a practical way to solve the identity protection
problem. The existing facial de-identification methods have revealed several
problems, including the impact on the realism of anonymized results when faced
with occlusions and the inability to maintain identity-irrelevant details in
anonymized results. We present a High-Fidelity and Occlusion-Robust
De-identification (HFORD) method to deal with these issues. This approach can
disentangle identities and attributes while preserving image-specific details
such as background, facial features (e.g., wrinkles), and lighting, even in
occluded scenes. To disentangle the latent codes in the GAN inversion space, we
introduce an Identity Disentanglement Module (IDM). This module selects the
latent codes that are closely related to the identity. It further separates the
latent codes into identity-related codes and attribute-related codes, enabling
the network to preserve attributes while only modifying the identity. To ensure
the preservation of image details and enhance the network's robustness to
occlusions, we propose an Attribute Retention Module (ARM). This module
adaptively preserves identity-irrelevant details and facial occlusions and
blends them into the generated results in a modulated manner. Extensive
experiments show that our method has higher quality, better detail fidelity,
and stronger occlusion robustness than other face de-identification methods
Diff-Privacy: Diffusion-based Face Privacy Protection
Privacy protection has become a top priority as the proliferation of AI
techniques has led to widespread collection and misuse of personal data.
Anonymization and visual identity information hiding are two important facial
privacy protection tasks that aim to remove identification characteristics from
facial images at the human perception level. However, they have a significant
difference in that the former aims to prevent the machine from recognizing
correctly, while the latter needs to ensure the accuracy of machine
recognition. Therefore, it is difficult to train a model to complete these two
tasks simultaneously. In this paper, we unify the task of anonymization and
visual identity information hiding and propose a novel face privacy protection
method based on diffusion models, dubbed Diff-Privacy. Specifically, we train
our proposed multi-scale image inversion module (MSI) to obtain a set of SDM
format conditional embeddings of the original image. Based on the conditional
embeddings, we design corresponding embedding scheduling strategies and
construct different energy functions during the denoising process to achieve
anonymization and visual identity information hiding. Extensive experiments
have been conducted to validate the effectiveness of our proposed framework in
protecting facial privacy.Comment: 17page
Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection
Due to the successful development of deep image generation technology, visual
data forgery detection would play a more important role in social and economic
security. Existing forgery detection methods suffer from unsatisfactory
generalization ability to determine the authenticity in the unseen domain. In
this paper, we propose a novel Attention Consistency Refined masked frequency
forgery representation model toward generalizing face forgery detection
algorithm (ACMF). Most forgery technologies always bring in high-frequency
aware cues, which make it easy to distinguish source authenticity but difficult
to generalize to unseen artifact types. The masked frequency forgery
representation module is designed to explore robust forgery cues by randomly
discarding high-frequency information. In addition, we find that the forgery
attention map inconsistency through the detection network could affect the
generalizability. Thus, the forgery attention consistency is introduced to
force detectors to focus on similar attention regions for better generalization
ability. Experiment results on several public face forgery datasets
(FaceForensic++, DFD, Celeb-DF, and WDF datasets) demonstrate the superior
performance of the proposed method compared with the state-of-the-art methods.Comment: The source code and models are publicly available at
https://github.com/chenboluo/ACM
LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification
Given a natural language statement, how to verify its veracity against a
large-scale textual knowledge source like Wikipedia? Most existing neural
models make predictions without giving clues about which part of a false claim
goes wrong. In this paper, we propose LOREN, an approach for interpretable fact
verification. We decompose the verification of the whole claim at phrase-level,
where the veracity of the phrases serves as explanations and can be aggregated
into the final verdict according to logical rules. The key insight of LOREN is
to represent claim phrase veracity as three-valued latent variables, which are
regularized by aggregation logical rules. The final claim verification is based
on all latent variables. Thus, LOREN enjoys the additional benefit of
interpretability -- it is easy to explain how it reaches certain results with
claim phrase veracity. Experiments on a public fact verification benchmark show
that LOREN is competitive against previous approaches while enjoying the merit
of faithful and accurate interpretability. The resources of LOREN are available
at: https://github.com/jiangjiechen/LOREN.Comment: Accepted to AAAI 202
Qualitative Simulation of Photon Transport in Free Space Based on Monte Carlo Method and Its Parallel Implementation
During the past decade, Monte Carlo method has obtained wide applications in optical imaging to simulate photon transport process inside tissues. However, this method has not been effectively extended to the simulation of free-space photon transport at present. In this paper, a uniform framework for noncontact optical imaging is proposed based on Monte Carlo method, which consists of the simulation of photon transport both in tissues and in free space. Specifically, the simplification theory of lens system is utilized to model the camera lens equipped in the optical imaging system, and Monte Carlo method is employed to describe the energy transformation from the tissue surface to the CCD camera. Also, the focusing effect of camera lens is considered to establish the relationship of corresponding points between tissue surface and CCD camera. Furthermore, a parallel version of the framework is realized, making the simulation much more convenient and effective. The feasibility of the uniform framework and the effectiveness of the parallel version are demonstrated with a cylindrical phantom based on real experimental results
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