185 research outputs found
Lycorine reduces mortality of human enterovirus 71-infected mice by inhibiting virus replication
Human enterovirus 71 (EV71) infection causes hand, foot and mouth disease in children under 6 years old and this infection occasionally induces severe neurological complications. No vaccines or drugs are clinical available to control EV71 epidemics. In present study, we show that treatment with lycorine reduced the viral cytopathic effect (CPE) on rhabdomyosarcoma (RD) cells by inhibiting virus replication. Analysis of this inhibitory effect of lycorine on viral proteins synthesis suggests that lycorine blocks the elongation of the viral polyprotein during translation. Lycorine treatment of mice challenged with a lethal dose of EV71 resulted in reduction of mortality, clinical scores and pathological changes in the muscles of mice, which were achieved through inhibition of viral replication. When mice were infected with a moderate dose of EV71, lycorine treatment was able to protect them from paralysis. Lycorine may be a potential drug candidate for the clinical treatment of EV71-infected patients
INDIVIDUALITY OR CONFORMITY: RECOMMENDATION EXPLOITING COMMUNITY-LEVEL SOCIAL INFLUENCE
With the increasing prevalence of online businesses and social networking services, a huge volume of data about transaction records and social connections between users is accumulated at an unprecedented speed, which enables us to take advantage of electronic word-of-mouth effect embedded in social networks for precision marketing and social recommendations. Different from existing works on social recommendations, our research focuses on discriminating the community-level social influence of different friend groups to enhance the quality of recommendation. To this end, we propose a novel probabilistic topic model integrating community detection with topic discovery to model user behaviors. Based on this model, a recommendation method taking both individual interests and conformity influence into consideration is developed. To evaluate the performance of the proposed model and method, experiments are conducted on two real recommendation applications, and the results demonstrate that the proposed recommendation method exhibits superior performance compared with the state-of-art recommendation methods, and the proposed topic model exhibits good explainablibity of topic semantics and community interests. Furthermore, as some people are more individual interest oriented and some are more conformity oriented demonstrated by the experiments, we explore factors that influence each individual’s conformity tendency, and obtain some meaningful findings
Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose
Recent works have shown how realistic talking face images can be obtained
under the supervision of geometry guidance, e.g., facial landmark or boundary.
To alleviate the demand for manual annotations, in this paper, we propose a
novel self-supervised hybrid model (DAE-GAN) that learns how to reenact face
naturally given large amounts of unlabeled videos. Our approach combines two
deforming autoencoders with the latest advances in the conditional generation.
On the one hand, we adopt the deforming autoencoder to disentangle identity and
pose representations. A strong prior in talking face videos is that each frame
can be encoded as two parts: one for video-specific identity and the other for
various poses. Inspired by that, we utilize a multi-frame deforming autoencoder
to learn a pose-invariant embedded face for each video. Meanwhile, a
multi-scale deforming autoencoder is proposed to extract pose-related
information for each frame. On the other hand, the conditional generator allows
for enhancing fine details and overall reality. It leverages the disentangled
features to generate photo-realistic and pose-alike face images. We evaluate
our model on VoxCeleb1 and RaFD dataset. Experiment results demonstrate the
superior quality of reenacted images and the flexibility of transferring facial
movements between identities
Imaging-based amplitude laser beam shaping for material processing by 2D reflectivity tuning of a spatial light modulator
We have demonstrated an imaging-based amplitude laser-beam-shaping technique for material processing by 2D reflectivity tuning of a spatial light modulator. Intensity masks with 256 gray levels were designed to shape the input laser beam in the outline profile and inside intensity distribution. Squared and circular flattop beam shapes were obtained at the diffractive near-field and then reconstructed at an image plane of a
Ultrafast laser beam shaping for material processing at imaging plane by geometric masks using a spatial light modulator
We have demonstrated an original ultrafast laser beam shaping technique for material processing using a spatial light modulator (SLM). Complicated and time-consuming diffraction far-field phase hologram calculations based on Fourier transformations are avoided, while simple and direct geometric masks are used to shape the incident beam at diffraction near-field. Various beam intensity shapes, such as square, triangle, ring and star, are obtained and then reconstructed at the imaging plane of an f-theta lens. The size of the shaped beam is approximately 20 µm, which is comparable to the beam waist at the focal plane. A polished stainless steel sample is machined by the shaped beam at the imaging plane. The shape of the ablation footprint well matches the beam shape
Learning Global-aware Kernel for Image Harmonization
Image harmonization aims to solve the visual inconsistency problem in
composited images by adaptively adjusting the foreground pixels with the
background as references. Existing methods employ local color transformation or
region matching between foreground and background, which neglects powerful
proximity prior and independently distinguishes fore-/back-ground as a whole
part for harmonization. As a result, they still show a limited performance
across varied foreground objects and scenes. To address this issue, we propose
a novel Global-aware Kernel Network (GKNet) to harmonize local regions with
comprehensive consideration of long-distance background references.
Specifically, GKNet includes two parts, \ie, harmony kernel prediction and
harmony kernel modulation branches. The former includes a Long-distance
Reference Extractor (LRE) to obtain long-distance context and Kernel Prediction
Blocks (KPB) to predict multi-level harmony kernels by fusing global
information with local features. To achieve this goal, a novel Selective
Correlation Fusion (SCF) module is proposed to better select relevant
long-distance background references for local harmonization. The latter employs
the predicted kernels to harmonize foreground regions with both local and
global awareness. Abundant experiments demonstrate the superiority of our
method for image harmonization over state-of-the-art methods, \eg, achieving
39.53dB PSNR that surpasses the best counterpart by +0.78dB ;
decreasing fMSE/MSE by 11.5\%/6.7\% compared with the
SoTA method. Code will be available at
\href{https://github.com/XintianShen/GKNet}{here}.Comment: 10 pages, 10 figure
Iterative Few-shot Semantic Segmentation from Image Label Text
Few-shot semantic segmentation aims to learn to segment unseen class objects
with the guidance of only a few support images. Most previous methods rely on
the pixel-level label of support images. In this paper, we focus on a more
challenging setting, in which only the image-level labels are available. We
propose a general framework to firstly generate coarse masks with the help of
the powerful vision-language model CLIP, and then iteratively and mutually
refine the mask predictions of support and query images. Extensive experiments
on PASCAL-5i and COCO-20i datasets demonstrate that our method not only
outperforms the state-of-the-art weakly supervised approaches by a significant
margin, but also achieves comparable or better results to recent supervised
methods. Moreover, our method owns an excellent generalization ability for the
images in the wild and uncommon classes. Code will be available at
https://github.com/Whileherham/IMR-HSNet.Comment: ijcai 202
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