Adaptive multimodal continuous ant colony optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima

A maximal clique based multiobjective evolutionary algorithm for overlapping community detection

Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm

GAN-based Image Compression with Improved RDO Process

GAN-based image compression schemes have shown remarkable progress lately due to their high perceptual quality at low bit rates. However, there are two main issues, including 1) the reconstructed image perceptual degeneration in color, texture, and structure as well as 2) the inaccurate entropy model. In this paper, we present a novel GAN-based image compression approach with improved rate-distortion optimization (RDO) process. To achieve this, we utilize the DISTS and MS-SSIM metrics to measure perceptual degeneration in color, texture, and structure. Besides, we absorb the discretized gaussian-laplacian-logistic mixture model (GLLMM) for entropy modeling to improve the accuracy in estimating the probability distributions of the latent representation. During the evaluation process, instead of evaluating the perceptual quality of the reconstructed image via IQA metrics, we directly conduct the Mean Opinion Score (MOS) experiment among different codecs, which fully reflects the actual perceptual results of humans. Experimental results demonstrate that the proposed method outperforms the existing GAN-based methods and the state-of-the-art hybrid codec (i.e., VVC)

Molecular Cloning and Functional Characterization of the Dehydrin (IpDHN) Gene From Ipomoea pes-caprae

Dehydrin (DHN) genes can be rapidly induced to offset water deficit stresses in plants. Here, we reported on a dehydrin gene (IpDHN) related to salt tolerance isolated from Ipomoea pes-caprae L. (Convolvulaceae). The IpDHN protein shares a relatively high homology with Arabidopsis dehydrin ERD14 (At1g76180). IpDHN was shown to have a cytoplasmic localization pattern. Quantitative RT-PCR analyses indicated that IpDHN was differentially expressed in most organs of I. pes-caprae plants, and its expression level increased after salt, osmotic stress, oxidative stress, cold stress and ABA treatments. Analysis of the 974-bp promoter of IpDHN identified distinct cis-acting regulatory elements, including an MYB binding site (MBS), ABRE (ABA responding)-elements, Skn-1 motif, and TC-rich repeats. The induced expression of IpDHN in Escherichia coli indicated that IpDHN might be involved in salt, drought, osmotic, and oxidative stresses. We also generated transgenic Arabidopsis lines that over-expressed IpDHN. The transgenic Arabidopsis plants showed a significant enhancement in tolerance to salt/drought stresses, as well as less accumulation of hydrogen peroxide (H2O2) and the superoxide radical (O2−), accompanied by increasing activity of the antioxidant enzyme system in vivo. Under osmotic stresses, the overexpression of IpDHN in Arabidopsis can elevate the expression of ROS-related and stress-responsive genes and can improve the ROS-scavenging ability. Our results indicated that IpDHN is involved in cellular responses to salt and drought through a series of pleiotropic effects that are likely involved in ROS scavenging and therefore influence the physiological processes of microorganisms and plants exposed to many abiotic stresses

LLCMDA: A Novel Method for Predicting miRNA Gene and Disease Relationship Based on Locality-Constrained Linear Coding

MiRNAs are small non-coding regulatory RNAs which are associated with multiple diseases. Increasing evidence has shown that miRNAs play important roles in various biological and physiological processes. Therefore, the identification of potential miRNA-disease associations could provide new clues to understanding the mechanism of pathogenesis. Although many traditional methods have been successfully applied to discover part of the associations, they are in general time-consuming and expensive. Consequently, computational-based methods are urgently needed to predict the potential miRNA-disease associations in a more efficient and resources-saving way. In this paper, we propose a novel method to predict miRNA-disease associations based on Locality-constrained Linear Coding (LLC). Specifically, we first reconstruct similarity networks for both miRNAs and diseases using LLC and then apply label propagation on the similarity networks to get relevant scores. To comprehensively verify the performance of the proposed method, we compare our method with several state-of-the-art methods under different evaluation metrics. Moreover, two types of case studies conducted on two common diseases further demonstrate the validity and utility of our method. Extensive experimental results indicate that our method can effectively predict potential associations between miRNAs and diseases