G-VAE: A Continuously Variable Rate Deep Image Compression Framework

Rate adaption of deep image compression in a single model will become one of the decisive factors competing with the classical image compression codecs. However, until now, there is no perfect solution that neither increases the computation nor affects the compression performance. In this paper, we propose a novel image compression framework G-VAE (Gained Variational Autoencoder), which could achieve continuously variable rate in a single model. Unlike the previous solutions that encode progressively or change the internal unit of the network, G-VAE only adds a pair of gain units at the output of encoder and the input of decoder. It is so concise that G-VAE could be applied to almost all the image compression methods and achieve continuously variable rate with negligible additional parameters and computation. We also propose a new deep image compression framework, which outperforms all the published results on Kodak datasets in PSNR and MS-SSIM metrics. Experimental results show that adding a pair of gain units will not affect the performance of the basic models while endowing them with continuously variable rate

Associations of Polymorphisms in the Apolipoprotein APOA1-C3-A5 Gene Cluster with Acute Coronary Syndrome

Background. Acute coronary syndromes (ACSs) are clinically cardiovascular events associated with dyslipidemia in common. Single nucleotide polymorphisms (SNPs) and haplotypes in the APOA1/C3/A5 gene cluster are associated with diabetes and familial combined hyperlipidaemia (FCH). Little is known about whether the polymorphisms in these genes affect lipid homeostasis in patients with ACSs. The present paper aimed to examine these associations with 4 SNPs in the APOA1 −75G > A, the APOC3 −455T > C, and APOA5 −1131T > C, c.553G > T variant to ACSs in Chinese Han. Methods. Chinese Han of 229 patients with ACSs and 254 unrelated controls were analyzed. Four SNPs in APOA1/C3/A5 cluster were genotyped and lipid was determined. Results. Our data show that minor allelic frequencies of APOC3 −455T > C, APOA5 −1131T > C, and c.553G > T polymorphisms in patients with ACSs were significantly higher than control group (P < 0.05). Furthermore, the 3 polymorphic sites were strongly of linkage disequilibrium, and minor alleles of 3 SNP sites had higher TG level than wild alleles (P < 0.05), APOC3 −455C and APOA5 c.553T allele carriers also had lower level of HDL-C. Conclusions. The minor alleles of APOC3 −455T > C, APOA5 −1131T > C, and c.553G > T polymorphisms are closely associated with ACSs

DNMT3a in the hippocampal CA1 is crucial in the acquisition of morphine self‐administration in rats

Drug‐reinforced excessive operant responding is one fundamental feature of long-lasting addiction‐like behaviors and relapse in animals. However, the transcriptional regulatory mechanisms responsible for the persistent drug‐specific (not natural rewards) operant behavior are not entirely clear. In this study, we demonstrate a key role for one of the de novo DNA methyltransferase, DNMT3a, in the acquisition of morphine self‐administration (SA) in rats. The expression of DNMT3a in the hippocampal CA1 region but not in the nucleus accumbens shell was significantly up‐regulated after 1‐ and 7‐day morphine SA (0.3 mg/kg/infusion) but not after the yoked morphine injection. On the other hand, saccharin SA did not affect the expression of DNMT3a or DNMT3b. DNMT inhibitor 5‐aza‐2‐deoxycytidine (5‐aza) microinjected into the hippocampal CA1 significantly attenuated the acquisition of morphine SA. Knockdown of DNMT3a also impaired the ability to acquire the morphine SA. Overall, these findings suggest that DNMT3a in the hippocampus plays an important role in the acquisition of morphine SA and may be a valid target to prevent the development of morphine addiction. Includes Supplemental informatio

MODEL : motif-based deep feature learning for link prediction

Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%). © 2014 IEEE

Watermarking for Out-of-distribution Detection

Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models. However, existing methods largely ignore the reprogramming property of deep models and thus may not fully unleash their intrinsic strength: without modifying parameters of a well-trained deep model, we can reprogram this model for a new purpose via data-level manipulation (e.g., adding a specific feature perturbation to the data). This property motivates us to reprogram a classification model to excel at OOD detection (a new task), and thus we propose a general methodology named watermarking in this paper. Specifically, we learn a unified pattern that is superimposed onto features of original data, and the model's detection capability is largely boosted after watermarking. Extensive experiments verify the effectiveness of watermarking, demonstrating the significance of the reprogramming property of deep models in OOD detection