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This note gives a classification for the spectral measures of CMV matrices, which are special unitary five-diagonal matrices related to orthogonal polynomials on the unit circle, with respect to packing measures. [ABSTRACT FROM AUTHOR]

Our previous studies showed that SYISL is a negative regulator of muscle growth and regeneration in mice, pigs and humans. SYISL knockout resulted in an increase in the density of muscle fibers and muscle growth. However, it is unclear whether there are natural mutations in pig SYNPO2 intron sense‐overlapping lncRNA (pSYISL) that affect the expression of pSYISL and muscle growth traits. In this study, three SNPs in exons and six SNPs within the promoter of pSYISL were identified. Association analysis showed that the two SNPs in exons are significantly associated with loin muscle area (p < 0.05); the six SNPs in the promoter that show complete linkage are significantly associated with live backfat thickness and live loin muscle area in American Large White pigs. Bioinformatics and luciferase reporter assays as well as in vitro binding experiments indicated that the mutation of SNP rs702045770 (g.539G>A) leads to the loss of YY1 binding to the promoter, thus affecting the expression level of pSYISL, and we found that Jiangshan Black pigs with genotype GG have a higher expression level of pSYISL than genotype AA individuals, but the muscle fiber density was significantly lower than in genotype AA individuals. Furthermore, the association analysis showed that the carcass backfat thickness of genotype GG of SNP rs702045770 was significantly higher than that of other genotypes in (Pietrain × Duroc) × (Landrace × Yorkshire) crossbred pigs (p < 0.05). The glycolytic potential of genotype GG was significantly higher than that of other genotypes (p < 0.05). These results provide novel insight into the identification of functional SNPs in non‐coding genomic regions. [ABSTRACT FROM AUTHOR]

Domain adaptation solves the challenge of inadequate labeled samples in the target domain by leveraging the knowledge learned from the labeled source domain. Most existing approaches aim to reduce the domain shift by performing some coarse alignments such as domain-wise alignment and class-wise alignment. To circumvent the limitation, we propose a coarse-to-fine unsupervised domain adaptation method based on metric learning, which can fully utilize more geometric structure and sample-wise information to obtain a finer alignment. The main advantages of our approach lie in four aspects: (1) it employs a structure-preserving algorithm to automatically select the optimal subspace dimension on the Grassmannian manifold; (2) based on coarse distribution alignment using maximum mean discrepancy, it utilizes the smooth triplet loss to leverage the supervision information of samples to improve the discrimination of data; (3) it introduces structure regularization to preserve the geometry of samples; (4) it designs a graph-based sample reweighting method to adjust the weight of each source domain sample in the cross-domain task. Extensive experiments on several public datasets demonstrate that our method achieves remarkable superiority over several competitive methods (more than 1.5% improvement of the average classification accuracy over the best baseline). [ABSTRACT FROM AUTHOR]

Point cloud matching is an important procedure in a variety of computer vision tasks. Traditional point cloud matching methods have made great progress, while neural network‐based approaches are becoming a trend, powered by their strong capabilities of feature extraction. Existing point matching neural networks, however, mainly focus on the rigid transformation. More complex transformations should also be considered in many scenarios. In this regard, the authors extend the rigid registration to non‐rigid cases and propose a network called the Scale Robust Point Matching (SRPM)‐Net for scale point matching. This robust structure‐preserving network is implemented by incorporating Lie group parametrisation. It is conducted by Lie group linearisation representation with the constraints of parameters under the corresponding basis of Lie algebra. SRPM‐Net preserves the structure of the solution and avoids degeneration. The contributions of this paper lie in two aspects: Most importantly, SRPM‐Net provides an extendable framework for handling complicated transformations. Secondly, it introduces a new feature learning module, which better preserves the shape structure by aggregating the high‐dimensional feature and calculating the normal vector of point cloud surface automatically. Experimental results show that SRPM‐Net is more robust and accurate than existing traditional and recent deep learning methods under various situations. [ABSTRACT FROM AUTHOR]

KPNA7 is an essential nuclear protein for early embryogenesis and normal fertility. The aim of our study was to determine the associations between single nucleotide polymorphisms (SNPs) in the KPNA7 gene and the reproductive traits in France Large White pigs. Six new SNPs were identified by sequencing. Sows with CT genotype of rs81308652 had a significantly higher total number born than sows with CC and TT genotype in first parity. Litters from sows with TT genotype of rs81308652 had a significantly lower number of weak births than litters from sows with CC and CT genotype in multi parity. Individuals with GG genotype of rs327848277 showed higher litter weight at birth (LWB) and number of healthy births than individuals with genotype GT and TT in first parity, and individuals with genotype GG had lower LWB compared to other genotypes in multi parity. However, we obtained no statistically significant results for association between the SNPs in other four loci and reproductive traits in both primiparous and multiparous pigs. In conclusion, our results show that there are significant association of identified SNPs located in KPNA7 gene with pig reproductive traits, and provided a theoretical basis for genetic improvement of pig reproductive traits. [ABSTRACT FROM AUTHOR]

Purpose: Partial alignment for 3 D point sets is a challenging problem for laser calibration and robot calibration due to the unbalance of data sets, especially when the overlap of data sets is low. Geometric features can promote the accuracy of alignment. However, the corresponding feature extraction methods are time consuming. The purpose of this paper is to find a framework for partial alignment by an adaptive trimmed strategy. Design/methodology/approach: First, the authors propose an adaptive trimmed strategy based on point feature histograms (PFH) coding. Second, they obtain an initial transformation based on this partition, which improves the accuracy of the normal direction weighted trimmed iterative closest point (ICP) method. Third, they conduct a series of GPU parallel implementations for time efficiency. Findings: The initial partition based on PFH feature improves the accuracy of the partial registration significantly. Moreover, the parallel GPU algorithms accelerate the alignment process. Research limitations/implications: This study is applicable to rigid transformation so far. It could be extended to non-rigid transformation. Practical implications: In practice, point set alignment for calibration is a technique widely used in the fields of aircraft assembly, industry examination, simultaneous localization and mapping and surgery navigation. Social implications: Point set calibration is a building block in the field of intelligent manufacturing. Originality/value: The contributions are as follows: first, the authors introduce a novel coarse alignment as an initial calibration by PFH descriptor similarity, which can be viewed as a coarse trimmed process by partitioning the data to the almost overlap part and the rest part; second, they reduce the computation time by GPU parallel coding during the acquisition of feature descriptor; finally, they use the weighted trimmed ICP method to refine the transformation. [ABSTRACT FROM AUTHOR]

The leaderless consensus of fractional-order multi-agent systems (FOMASs) by intermittence sampled data control method is investigated in this brief, for which a distributed intermittence sampled data control protocol is presented to reduce the updating rate and working time of controllers. Subsequently, the Laplace transform and the stability theory are utilized to derive some necessary and sufficient consensus criteria that show the relations among the fractional order, sampling period, communication width, coupling strengths, and network topology. What is more, it can be discovered that both the communication width and sampling period have the lower and upper bounds for given networks to attain leaderless consensus. Finally, a numerical simulation example is presented to prove the theoretical results. [ABSTRACT FROM AUTHOR]

Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods. [ABSTRACT FROM AUTHOR]

Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal. [ABSTRACT FROM AUTHOR]

Solvent effect is one of the important factors in sample preparation which may affect matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectra of synthetic polymers. MALDI imaging, a useful imaging tool for discovering biomarkers in tissues, is applied here for better comprehension of solvent effect in polymer analysis by MALDI-TOF mass spectrometry. Nylon-6 was chosen as a model polymer for the study of solvent effect. Its MALDI mass spectra in different solvents were performed. MALDI imaging analysis was performed for studying the incorporation of analytes into matrix crystals in different solvent combinations. Specifically, the colocalization of matrix and analyte was obtained through Pearson’s correlation (PC) coefficient analysis of their MALDI images. The results demonstrated that satisfactory spectra were obtained in higher PC value conditions. PC decreased along with an increase in the ratio of poor solvent, which suggested that we should minimize the poor solvent ratio to obtain better MALDI spectra. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an object(s)-of-interest (OOI) segmentation method for images with inhomogeneous intensities. First, we define a discrimination function for each pixel, labelling whether the pixel belongs to OOI based on the characteristics of OOI. This function is then integrated with image gradient to construct a stopping function in an energy functional. Finally, this energy functional is minimized by means of level set evolution, which guides the motion of the zero level set toward object boundaries. The results demonstrate that our model is effective. [ABSTRACT FROM AUTHOR]

In the field of autonomous driving, carriers are equipped with a variety of sensors, including cameras and LiDARs. However, the camera suffers from problems of illumination and occlusion, and the LiDAR encounters motion distortion, degenerate environment and limited ranging distance. Therefore, fusing the information from these two sensors deserves to be explored. In this paper, we propose a fusion network which robustly captures both the image and point cloud descriptors to solve the place recognition problem. Our contribution can be summarized as: (1) applying the trimmed strategy in the point cloud global feature aggregation to improve the recognition performance, (2) building a compact fusion framework which captures both the robust representation of the image and 3D point cloud, and (3) learning a proper metric to describe the similarity of our fused global feature. The experiments on KITTI and KAIST datasets show that the proposed fused descriptor is more robust and discriminative than the single sensor descriptor. [ABSTRACT FROM AUTHOR]

In this paper, numerical methods to compute the Karcher means on the n -order rotation group SO( n ) are considered. First, after recalling Karcher means as solutions of a kind of minimization problems on SO( n ), a super-linearly convergent numerical method, namely conjugate gradient method, has been used to deal with them. By the geometric structure of SO( n ), the proposed algorithm is structure preserving. Then, a variety of numerical experiments are presented to demonstrate the performance and efficiency of the proposed algorithm by comparing with a recent structure preserving method. [ABSTRACT FROM AUTHOR]

Virus image classification is an important issue in clinical virology, highly accurate algorithm of automatic virus image classification is very helpful. In this paper, instead of extracting virus feature from the original image, we propose a novel method that extracts the virus feature from the filtered images by multi-scale principal component analysis (PCA). Firstly, multi-scale PCA filters are learned from all original images in the data set. Secondly, the original images are convolved with the learned filters. Therefore, the filtered images can capture the principal texture information from different perspectives. Then, the completed local binary pattern (CLBP) descriptor is firstly utilized to depict the features of all filtered virus images. The multi-scale CLBP features extracted from filtered images by multi-scale PCA are combined as the feature MPMC (Multi-scale PCA and Multi-scale CLBP), which is proposed in this paper. Finally, support vector machine (SVM) with polynomial kernel is used for classification. Experiments show that the classification accuracy based on MPMC outperforms the previous methods in the literature for the same virus image data set. [ABSTRACT FROM AUTHOR]