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Capturing the genetic diversity of different wild populations is crucial for unraveling the mechanisms of adaptation and establishing links between genome evolution and local adaptation. The Asian corn borer (ACB) moth has undergone natural selection during its adaptative evolution. However, structural variants (SVs), which play significant roles in these adaptation processes, have not been previously identified. Here, we constructed a multi-assembly graph pan-genome to highlight the importance of SVs in local adaptation. Our analysis revealed that the graph pan-genome contained 176.60 Mb (∼37.33%) of unique sequences. Subsequently, we performed an analysis of expression quantitative trait loci (QTLs) to explore the impact of SVs on gene expression regulation. Notably, through QTL mapping analysis, we identified the FTZ-F1 gene as a potential candidate gene associated with the traits of larval development rate. In sum, we explored the impact of SVs on the local adaptation of pests, therefore facilitating accelerated pest management strategies. [Display omitted] • A multi-assembly graph pangenome highlights structural variants in local adaptation • More than 50% of SVs are derived from transposable elements • Analysis of expression QTLs to explore the impact of SVs on gene expression regulation • SVs contribute to changes in adaptive traits, such as diapause Most variant analyses depend on a linear reference genome, which is presumed to be missing millions of bases found in the genomes of other populations. Peng et al. utilize a graph pan-genome to highlight the significant impact of structural variants on gene expression and adaptive traits such as diapause. [ABSTRACT FROM AUTHOR]

The grey wolf optimizer (GWO) is an optimization algorithm that draws inspiration from nature. It is an optimization algorithm based on population that iteratively searches for the optimal solution by simulating the social behavior and hunting behavior of grey wolves. It has recently been shown that GWO can be improved by the introduction of initializing, movement, selecting and updating. In this paper, we extend an improved grey wolf optimizer with weighting functions (IGWO-WFs), which include multi-modal adaptive function, sigmoid function and autoregressive function. The IGWO-WFs has 74% improved to the conventional algorithms. It can be resolved the instability and convergence issues of GWO and investigate the effectiveness of the methods through numerical simulations and the path planning of Unmanned Aerial Vehicles (UAVs). • The output range of sigmoid function can be thresholded to improve algorithmic convergence. • The Multi-modal function has multiple extrema and a strong global search capability. • The Autoregressive function automatically adjusts its parameters to optimize the performance. • Improve the efficiency of DLH strategy. [Display omitted] [ABSTRACT FROM AUTHOR]

The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. [Display omitted] • Autoregressive exogenous model can replace False Data Injection model. • Volterra series expand few data to meet the demand of input data volume. • Polynomial regressors ensure control systems run stably under False Data Injection attack. [ABSTRACT FROM AUTHOR]

Single-sample face recognition remains a significant challenge due to the difficulty in extracting discriminative features using only one facial image per individual in practical applications. In light of this, the paper introduces a new method for learning multiple subspace features using singular value decomposition (SVD). Specifically, we divide each facial image into two symmetrical halves to increase intra-class diversity. The SVD is subsequently applied to each half to create distinct geometric view subspaces. Then, discriminative features are learnt through performing 2-dimension linear discriminant analysis (2DLDA) in each subspace. Lastly, the identification of faces is accomplished by using a k-nearest neighbour classifier (k-NN) within each subspace followed by a majority voting strategy. The method proposed is extensively verified through rigorous experiments carried out on different databases such as CUHK, Extended Yale B, FRGCv2 and AR. The experimental outcomes unambiguously show that our approach consistently achieves competitive performance when compared to other methods. [ABSTRACT FROM AUTHOR]

Similarity measure generally exerts a crucial role in face recognition. Recently, regression analysis based similarity measure mechanism has demonstrated significant potential in robust face recognition. Nevertheless, most existing regression methods are far from perfect under few samples due to the poor performance of spanning the individual subspace. Previous works have been noticed that the singular value decomposition (SVD) of facial image can generate a set of complete base of individual subspace. Then we present a novel and efficient image similarity measure model named subspace cross representation (SCR) measure for face recognition with few samples. The power of our proposed SCR stems from the following facts. One is that the complete base can weaken the dependence of linear regression method on the number of labeled samples. The other is the cross linear representation can effectively use two-dimensional geometric features generated by SVD to distinguish facial images. The validity of SCR is tested by a large amount of experiments on AR, CUHK Sketch, Extended Yale B databases, etc. The experimental results demonstrate that SCR achieves satisfactory recognition accuracy compared with other methods, under few sample condition. [ABSTRACT FROM AUTHOR]

This paper introduces a deep retinex decomposition network for underwater image enhancement to conquer the color imbalance, blurring, low contrast, etc. Specifically, we first designed a novel convolutional neural network to estimate the illumination and get reflectance. Then we changed the general idea of processing low light enhancement based on retinex, we perform color balance and illumination correction on the decomposed reflectance and illumination respectively. Finally, the fused reflectance image and illumination image are produced by post-processing to get over blurring, etc. The experiments confirm that the proposed method can retain more details and edge information. Meanwhile, compared with other underwater image enhancement methods, the proposed method performs better in terms of visual effects with nearly 20% improvement in objective evaluation. [Display omitted] • This is Retinex-Net's first attempt in the field of underwater image enhancement. • The image decomposition method designed for underwater image is robust. • Good enhancement effect for inferior images in different environments. • The proposed method avoids complicated steps such as pretreatment and is simple and effective. [ABSTRACT FROM AUTHOR]

Generally, more than half of smaller singular values and corresponding singular vectors should be abandoned to achieve the image compression function for the compression method based on singular value decomposition (SVD). Although these discarded parts contain some noise and fuzzy factors, they also contain some detailed information to boost image reconstruction. To overcome this problem, we present a novel lossy image compression method named singular vector sparse reconstruction (SVSR) keeping the sparse representation data of more singular vector to boost the performance of SVD based image compression method in compression ratio and reconstruction quality. Specifically, we treat the singular vector as a signal and express it sparsely through sparse sampling based on the analysis of the characteristics of the singular vector. In particular, the compression ratio of the proposed method is about 70% higher than that of the traditional SVD method. Evaluation on several image data and the experimental results with different image compression algorithms clearly demonstrate the advantages of our proposed SVSR algorithm in compression ratio and reconstruction quality. [Display omitted] • A new lossy image compression method is proposed. • An effective sparse representation method of singular matrix is proposed. • An effective image interpolation method is designed. • Experiments on different kinds of datasets show the effectiveness of our method. [ABSTRACT FROM AUTHOR]