Search Results (51)

In this paper, an alignment-free bioinformatics technique, termed the 20D-Dynamic Representation of Protein Sequences, is utilized to investigate the similarity/dissimilarity between Baculovirus and Echinococcus multilocularis genome sequences. In this method, amino acid sequences are depicted as 20D-dynamic graphs, comprising sets of “material points” in a 20-dimensional space. The spatial distribution of these material points is indicative of the sequence characteristics and is quantitatively described by sequence descriptors akin to those employed in dynamics, such as coordinates of the center of mass of the 20D-dynamic graph and the tensor of the moment of inertia of the graph (defined as a symmetric matrix). Each descriptor unveils distinct features of similarity and is employed to establish similarity relations among the examined sequences, manifested either as a symmetric distance matrix (“similarity matrix”), a classification map, or a phylogenetic tree. The classification maps are introduced as a new way of visualizing the similarity relations obtained using the 20D-Dynamic Representation of Protein Sequences. Some classification maps are obtained using the Principal Component Analysis (PCA) for the center of mass coordinates and normalized moments of inertia of 20D-dynamic graphs as input data. Although the method operates in a multidimensional space, we also apply some visualization techniques, including the projection of 20D-dynamic graphs onto a 2D plane. Studies on model sequences indicate that the method is of high quality, both graphically and numerically. Despite the high similarity observed among the sequences of E. multilocularis, subtle discrepancies can be discerned on the 2D graphs. Employing this approach has led to the discovery of numerous new similarity relations compared to our prior study conducted at the DNA level, using the 4D-Dynamic Representation of DNA/RNA Sequences, another alignment-free bioinformatics method also introduced by us. Full article

Infrared small target detection is an important and core problem in infrared search and track systems. Many infrared small target detection methods work well under the premise of a static background; however, the detection effect decreases seriously when the background changes dynamically. In addition, the spatiotemporal information of the target and background of the image sequence are not fully developed and utilized, lacking long-term temporal characteristics. To solve these problems, a novel long-term spatial–temporal tensor (LSTT) model is proposed in this paper. The image registration technique is employed to realize the matching between frames. By directly superimposing the aligned images, the spatiotemporal features of the resulting tensor are not damaged or reduced. From the perspective of the horizontal slice of this tensor, it is found that the background component has similarity in the time dimension and correlation in the space dimension, which is more consistent with the prerequisite of low rank, while the target component is sparse. Therefore, we transform the problem of infrared detection of a small moving target into a low-rank sparse decomposition problem of new tensors composed of several continuous horizontal slices of the aligned image tensor. The low rank of the background is constrained by the partial tubal nuclear norm (PTNN), and the tensor decomposition problem is quickly solved using the alternating-direction method of multipliers (ADMM). Our experimental results demonstrate that the proposed LSTT method can effectively detect small moving targets against a dynamic background. Compared with other benchmark methods, the new method has better performance in terms of detection efficiency and accuracy. In particular, the new LSTT method can extract the spatiotemporal information of more frames in a longer time domain and obtain a higher detection rate. Full article

Reportedly, structural failures in membrane structures have occurred frequently, mostly originating from localized damage caused by intense loads on the membrane surface. It is thus necessary to investigate the nonlinear behaviors and load-carrying capacity of membranes with local damage. This study has conducted uniaxial tensile tests for membranes with a variety of original defects by using a specialized experimental setup and photogrammetry technique. The nonlinear relationship between the mechanical properties and the deforming angle of membranes, which portrays the principal axis, tensor, tensile stress, and position of the original defects, is investigated. The entire process of membrane failure has been recorded, and the strain and stress during each test specimen are compared. The new results indicate that the membranes exhibit predominantly elastic deformation before failure but surprisingly impart brittle fracture upon failure. Finally, a novel approach for estimating the load-bearing capacity of initially damaged membranes was proposed through the analysis of the load-bearing capacity of the damaged membranes under various conditions, positions, angles, and other influential factors. Full article

Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs. Full article

The research on the quality of employment in China holds immense significance for attaining high-quality employment development. Firstly, enhancing the quality of employment facilitates the optimization of labor resource allocation and enhances economic efficiency. Secondly, high-quality employment serves as a fundamental pillar for social equity and stability. Lastly, continual enhancement of employment quality caters to the requirements of social development and plays a crucial role in promoting economic transformation and achieving sustainable development. However, what is the current situation of employment quality in China? How can we scientifically measure employment quality? What are the key factors for the development of employment quality? This study aimed to use spatiotemporal tensor data to measure the level of employment quality in China’s provinces and analyzed the magnitude and direction of its influencing factors in the spatiotemporal dimension. Taking thirty provinces, autonomous regions, and municipalities directly under the central government in China from 2011 to 2020 as the research objects, the employment quality evaluation system was constructed from six dimensions: employment environment, employment status, employability, labor remuneration, social security, and labor relations. The employment quality index data were expressed in the form of three-order, high-dimensional tensor spatiotemporal data, and the employment quality of China’s provinces was measured from the spatiotemporal perspective by using principal tensor analysis. Then, the visual analysis of the development and change process of employment quality was carried out. The spatial autocorrelation analysis of employment quality was carried out, and the time–space dual-fixed-effect model of the spatial Durbin model was selected to analyze the direction and magnitude of the influence factors of employment quality on the selected and neighboring provinces. The research showed that: (1) The overall level of employment quality in China was not high, the employment quality varied greatly among provinces, and the employment quality development gap among provinces showed a trend of widening. (2) The development of employment quality in western China was relatively fast, while the development of employment quality in central China showed insufficient stamina. (3) Sichuan Province had a strong radiation effect on the development of employment quality in neighboring provinces, and Beijing and Tianjin had a strong siphon effect on the development of employment quality in neighboring provinces. (4) The level of industrialization and informatization promoted the development of employment quality in China’s provinces, while the industrial structure had a significant negative effect on the development of employment quality. According to the research findings, the following policy recommendations are proposed: (1) strengthen inter-provincial cooperation and exchange, (2) emphasize support for the central and western regions, (3) fully leverage the radiation effect of Sichuan while optimizing the siphon effect of Beijing and Tianjin, and (4) enhance industrialization and information technology levels, as well as adjust the industrial structure. Full article

GPUs have been broadly used to accelerate big data analytics, scientific computing and machine intelligence. Particularly, matrix multiplication and convolution are two principal operations that use a large proportion of steps in modern data analysis and deep neural networks. These performance-critical operations are often offloaded to the GPU to obtain substantial improvements in end-to-end latency. In addition, multifarious workload characteristics and complicated processing phases in big data demand a customizable yet performant operator library. To this end, GPU vendors, including NVIDIA and AMD, have proposed template and composable GPU operator libraries to conduct specific computations on certain types of low-precision data elements. We formalize a set of benchmarks via CUTLASS, NVIDIA’s templated library that provides high-performance and hierarchically designed kernels. The benchmarking results show that, with the necessary fine tuning, hardware-level ASICs like tensor cores could dramatically boost performance in specific operations like GEMM offloading to modern GPUs. Full article

It is difficult to identify histone modification from datasets that contain high-throughput sequencing data. Although multiple methods have been developed to identify histone modification, most of these methods are not specific to histone modification but are general methods that aim to identify protein binding to the genome. In this study, tensor decomposition (TD) and principal component analysis (PCA)-based unsupervised feature extraction with optimized standard deviation were successfully applied to gene expression and DNA methylation. The proposed method was used to identify histone modification. Histone modification along the genome is binned within the region of length L. Considering principal components (PCs) or singular value vectors (SVVs) that PCA or TD attributes to samples, we can select PCs or SVVs attributed to regions. The selected PCs and SVVs further attribute p-values to regions, and adjusted p-values are used to select regions. The proposed method identified various histone modifications successfully and outperformed various state-of-the-art methods. This method is expected to serve as a de facto standard method to identify histone modification. For reproducibility and to ensure the systematic analysis of our study is applicable to datasets from different gene expression experiments, we have made our tools publicly available for download from gitHub. Full article

Magnetotelluric (MT) and magnetovariational (MV) sounding are two principal geophysical methods used to determine the electrical structure of the earth using natural electromagnetic signals. The complex relationship between the alternating electromagnetic fields can be defined by transfer functions, and their proper selection is crucial in a 3-D inversion. A synthetic case was studied to assess the capacity of these transfer functions to recover the electrical resistivity distribution of the subsurface and to evaluate the advantages and disadvantages of using the tipper vector W to complement the impedance tensor Z and the phase tensor Φ. The analysis started with two sensitivity tests to appraise the sensitivity of each type of transfer function, which is calculated for an oblique conductor model, showing that the resistivity perturbation of the same model will produce distinct perturbations to different transfer functions; the transfer function sensitivity is significantly different. A 3-D inversion utilizing the quasi-Newton method based on the L-BFGS formula was performed to invert different transfer functions and their combinations, along with quantifying their accuracy. The synthetic case study illustrates that a 3-D inversion of either the Z or Φ responses presents a superior ability to recover the subsurface electrical resistivity; joint inversions of the Z or Φ responses with the W responses possess superior imaging of the horizontal continuity of the conductive block. The appraisal of the 3-D inversion results of different transfer functions can facilitate assessing the advantages of different transfer functions and acquiring a more reasonable interpretation. Full article

Photoelasticity is a fast and powerful technique for internal stress detection and quality control in crystals; to fully exploit its possibilities, an appropriate theoretical analysis must be developed for different crystallographic structure and observation planes. For a cubic crystal specimen whose geometry is non-coherent with its crystallographic directions (i.e., observation planes and crystallographic directions are not parallel), we write a set of equations that allow an estimate of the refraction indices as a function of the residual stress. This is obtained upon the assumption that the residual stress may be represented by a plane stress parallel to the observation face. For cubic crystals, we obtain an explicit estimate of the residual stress intensity; this can be achieved provided we know the piezo-optic tensor component, the orientation of two non-parallel specimen faces with respect to the crystallographic axes, and that we can measure the principal directions of the refractive indices on the observation face. Full article

Molecular-scale understanding of rheological properties of small-molecular liquids and polymers is critical to optimizing their performance in practical applications such as lubrication and hydraulic fracking. We combine nonequilibrium molecular dynamics simulations with two unsupervised machine learning methods: principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), to extract the correlation between the rheological properties and molecular structure of squalane sheared at high strain rates (106–1010s−1) for which substantial shear thinning is observed under pressures P∈0.1–955 MPa at 293 K. Intramolecular atom pair orientation tensors of 435×6 dimensions and the intermolecular atom pair orientation tensors of 61×6 dimensions are reduced and visualized using PCA and t-SNE to assess the changes in the orientation order during the shear thinning of squalane. Dimension reduction of intramolecular orientation tensors at low pressures P=0.1,100 MPa reveals a strong correlation between changes in strain rate and the orientation of the side-backbone atom pairs, end-backbone atom pairs, short backbone-backbone atom pairs, and long backbone-backbone atom pairs associated with a squalane molecule. At high pressures P≥400 MPa, the orientation tensors are better classified by these different pair types rather than strain rate, signaling an overall limited evolution of intramolecular orientation with changes in strain rate. Dimension reduction also finds no clear evidence of the link between shear thinning at high pressures and changes in the intermolecular orientation. The alignment of squalane molecules is found to be saturated over the entire range of rates during which squalane exhibits substantial shear thinning at high pressures. Full article

Infrared small target detection (ISTD) plays a significant role in earth observation infrared systems. However, some high reflection areas have a grayscale similar to the target, which will cause a false alarm in the earth observation infrared system. For the sake of raising the detection accuracy, we proposed a cirrus detection measure based on low-rank sparse decomposition as a supplementary method. To better detect cirrus that may be sparsely insufficient in a single frame image, the method treats the cirrus sequence image with time continuity as a tensor, then uses the visual saliency of the image to divide the image into a cirrus region and a cirrus-free region. Considering that the classical tensor rank surrogate cannot approximate the tensor rank very well, we used a non-convex tensor rank surrogate based on the Laplace function for the spatial-temporal tensor (Lap-NRSSTT) to surrogate the tensor rank. In an effort to compute the proposed model, we used a high-efficiency optimization approach on the basis of alternating the direction method of multipliers (ADMM). Finally, final detection results were obtained by the reconstructed cirrus images with a set threshold segmentation. Results indicate that the proposed scheme achieves better detection capabilities and higher accuracy than other measures based on optimization in some complex scenarios. Full article

With the construction of supertall buildings such as high earth dams, the linear envelope of the Mohr-Coulomb (M-C) failure criterion fitted to lower confined pressure would significantly underestimate the loading capacity of foundations, causing a huge increase in the amount of earthwork. Given that the M-C criterion has dominated in the stability analysis of geotechnical structures, it is proposed in this study that the M-C criterion remain invariant in form but the cohesion c and the frictional factor f be related to the coefficient of intermediate principal stress b, called the Generalized Mohr-Coulomb (GMC) criterion. In other words, c and f are both functions of b, written as c(b) and f(b). In the simplest way, the GMC criterion for soils, a true three-dimensional failure criterion, can be established by using a piece of conventional triaxial apparatus. The GMC has a non-smooth strength surface like its conventional version. However, we prove from true triaxial tests and the characteristic theory of stress tensors that the failure surfaces in the stress space should be non-smooth per se for b = 0 or 1. Comparisons with other prominent failure criteria indicate that the GMC fits the test data best. Full article

Due to the rapid development of imaging technology, a large number of biological images have been obtained with three-dimensional (3D) spatial information, time information, and spectral information. Compared with the case of two-dimensional images, the framework for analyzing multidimensional bioimages has not been completely established yet. WDDD is an open biological image database. It dynamically records 3D developmental images of 186 samples of nematodes C. elegans. In this study, based on WDDD, we constructed a framework to analyze the multidimensional dataset, which includes image segmentation, image registration, size registration by the length of main axes, time registration by extracting key time points, and finally, using generalized N-dimensional principal component analysis (GND-PCA) to analyze the phenotypes of bioimages. As a data-driven technique, GND-PCA can automatically extract the important factors involved in the development of P1 and AB in C. elegans. A 3D bioimage can be regarded as a third-order tensor. Therefore, GND-PCA was applied to the set of third-order tensors, and a set of third-order tensor bases was iteratively learned to linearly approximate the set. For each tensor base, a corresponding characteristic image is built to reveal its geometric meaning. The results show that different bases can be used to express different vital factors in development, such as the asymmetric division within the two-cell stage of C. elegans. Based on selected bases, statistical models were built by 50 wild-type (WT) embryos in WDDD, and were applied to RNA interference (RNAi) embryos. The results of statistical testing demonstrated the effectiveness of this method. Full article

A collection of 24 hexacoordinate Co(II) complexes was investigated by ab initio CASSCF + NEVPT2 + SOC calculations. In addition to the energies of spin–orbit multiplets (Kramers doublets, KD) their composition of the spins is also analyzed, along with the projection norm to the effective Hamiltonian. The latter served as the evaluation of the axial and rhombic zero-field splitting parameters and the g-tensor components. The fulfilment of spin-Hamiltonian (SH) formalism was assessed by critical indicators: the projection norm for the first Kramers doublet N(KD1) > 0.7, the lowest g-tensor component g₁ > 1.9, the composition of KDs from the spin states |±1/2> and |±3/2> with the dominating percentage p > 70%, and the first transition energy at the NEVPT2 level ⁴Δ₁. Just the latter quantity causes a possible divergence of the second-order perturbation theory and a failure of the spin Hamiltonian. The data set was enriched by the structural axiality D_str and rhombicity E_str, respectively, evaluated from the metal–ligand distances Co-O, Co-N and Co-Cl corrected to the mean values. The magnetic data (temperature dependence of the molar magnetic susceptibility, and the field dependence of the magnetization per formula unit) were fitted simultaneously, either to the Griffith–Figgis model working with 12 spin–orbit kets, or the SH-zero field splitting model that utilizes only four (fictitious) spin functions. The calculated data were analyzed using statistical methods such as Cluster Analysis and the Principal Component Analysis. Full article

Infrared small target detection is widely used for early warning, aircraft monitoring, ship monitoring, and so on, which requires the small target and its background to be represented and modeled effectively to achieve their complete separation. Low-rank sparse decomposition based on the structural features of infrared images has attracted much attention among many algorithms because of its good interpretability. Based on our study, we found some shortcomings in existing baseline methods, such as redundancy of constructing tensors and fixed compromising factors. A self-adaptive low-rank sparse tensor decomposition model for infrared dim small target detection is proposed in this paper. In this model, the entropy of image block is used for fast matching of non-local similar blocks to construct a better sparse tensor for small targets. An adaptive strategy of low-rank sparse tensor decomposition is proposed for different background environments, which adaptively determines the weight coefficient to achieve effective separation of background and small targets in different background environments. Tensor robust principal component analysis (TRPCA) was applied to achieve low-rank sparse tensor decomposition to reconstruct small targets and their backgrounds separately. Sufficient experiments on the various types data sets show that the proposed method is competitive. Full article