9,046 research outputs found
Text Generation Based on Generative Adversarial Nets with Latent Variable
In this paper, we propose a model using generative adversarial net (GAN) to
generate realistic text. Instead of using standard GAN, we combine variational
autoencoder (VAE) with generative adversarial net. The use of high-level latent
random variables is helpful to learn the data distribution and solve the
problem that generative adversarial net always emits the similar data. We
propose the VGAN model where the generative model is composed of recurrent
neural network and VAE. The discriminative model is a convolutional neural
network. We train the model via policy gradient. We apply the proposed model to
the task of text generation and compare it to other recent neural network based
models, such as recurrent neural network language model and SeqGAN. We evaluate
the performance of the model by calculating negative log-likelihood and the
BLEU score. We conduct experiments on three benchmark datasets, and results
show that our model outperforms other previous models
Generative Cooperative Net for Image Generation and Data Augmentation
How to build a good model for image generation given an abstract concept is a
fundamental problem in computer vision. In this paper, we explore a generative
model for the task of generating unseen images with desired features. We
propose the Generative Cooperative Net (GCN) for image generation. The idea is
similar to generative adversarial networks except that the generators and
discriminators are trained to work accordingly. Our experiments on hand-written
digit generation and facial expression generation show that GCN's two
cooperative counterparts (the generator and the classifier) can work together
nicely and achieve promising results. We also discovered a usage of such
generative model as an data-augmentation tool. Our experiment of applying this
method on a recognition task shows that it is very effective comparing to other
existing methods. It is easy to set up and could help generate a very large
synthesized dataset.Comment: 12 pages, 8 figure
Ferritin level prospectively predicts hepatocarcinogenesis in patients with chronic hepatitis B virus infection
Previous studies have detected a higher level of ferritin in patients with hepatocellular carcinoma (HCC), but a potential causal association between serum ferritin level and hepatocarcinogenesis remains to be clarified. Using a well-established prospective cohort and longitudinally collected serial blood samples, the association between baseline ferritin levels and HCC risk were evaluated in 1,152 patients infected with hepatitis B virus (HBV), a major risk factor for HCC. The association was assessed by Cox proportional hazards regression model using univariate and multivariate analyses and longitudinal analysis. It was demonstrated that HBV patients who developed HCC had a significantly higher baseline ferritin level than those who remained cancer-free (188.00 vs. 108.00 ng/ml, P\u3c0.0001). The patients with a high ferritin level (≥200 ng/ml) had 2.43-fold increased risk of HCC compared to those with lower ferritin levels [hazard ratio (HR), 2.43; 95% confidence interval, 1.63-3.63]. A significant trend of increasing HRs along with elevated ferritin levels was observed (P for trend \u3c0.0001). The association was still significant after multivariate adjustment. Incorporating ferritin into the α-fetoprotein (AFP) model significantly improved the performance of HCC prediction (the area under the curve from 0.74 to 0.77, P=0.003). Longitudinal analysis showed that the average ferritin level in HBV patients who developed HCC was persistently higher than in those who were cancer-free during follow-up. HCC risk reached a peak at approximately the fifth year after baseline ferritin detection. Moreover, stratified analyses showed that the association was noted in both males and females, and was prominent in patients with a low AFP value. In short, serum ferritin level could independently predict the risk of HBV-related HCC and may have a complementary role in AFP-based HCC diagnosis. Future studies are warranted to validate these findings and test its clinical applicability in HCC prevention and management. © 2018, Spandidos Publication
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