With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulnerable to strategically modified samples, named adversarial examples . These samples are generated with some imperceptible perturbations, but can fool the DNNs to give false predictions. Inspired by the popularity of generating adversarial examples against DNNs in Computer Vision (CV), research efforts on attacking DNNs for Natural Language Processing (NLP) applications have emerged in recent years. However, the intrinsic difference between image (CV) and text (NLP) renders challenges to directly apply attacking methods in CV to NLP. Various methods are proposed addressing this difference and attack a wide range of NLP applications. In this article, we present a systematic survey on these works. We collect all related academic works since the first appearance in 2017. We then select, summarize, discuss, and analyze 40 representative works in a comprehensive way. To make the article self-contained, we cover preliminary knowledge of NLP and discuss related seminal works in computer vision. We conclude our survey with a discussion on open issues to bridge the gap between the existing progress and more robust adversarial attacks on NLP DNNs.
Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions.
ObjectivesTo estimate the average social network size in the general population and the size of HIV key affected populations (KAPs) in Chongqing municipality using the network scale-up method (NSUM).MethodsA general population survey was conducted in 2011 through a multistage random sampling method. Participants aged between 18 and 60 years were recruited. The average social network size (c) was estimated and adjusted by known population method. The size of HIV KAP in Chongqing municipality was estimated using the adjusted c value with adjustment for the transmission effect using the scaled respect factor.Results3,026 inhabitants of Chongqing agreed to the survey, and 2,957 (97.7%) completed the questionnaire. The adjusted c value was 310. The estimated size of KAP was 28,418(95% Confidence Interval (CI):26,636∼30,201) for female sex workers (FSW), 163,199(95%CI:156,490∼169,908) for clients of FSW, 37,959(95%CI: 34,888∼41,030) for drug users (DU), 14,975(95%CI:13,047∼16,904) for injecting drug users (IDU) and 16,767(95%CI:14,602∼18,932) for men who have sex with men (MSM). The ratio of clients to FSW was 5.74∶1, and IDU accounted for 39.5% of the DU population. The estimates suggest that FSW account for 0.37% of the female population aged 15–49 years in Chongqing, and clients of FSW and MSM represent 2.09% and 0.21% of the male population aged 15–49 years in the city, respectively.ConclusionNSUM provides reasonable population size estimates for FSW, their clients, DU and IDU in Chongqing. However, it is likely to underestimate the population size of MSM even after adjusting for the transmission effect.
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