Elements Of Vector Research Articles

Birge ratio method for modeling dark uncertainty in multivariate meta-analyses and inter-laboratory studies

In the paper, we introduce a new approach for combining multivariate measurements obtained in individual studies. The procedure extends the Birge ratio method, a commonly used approach in physics in the univariate case, such as for the determination of physical constants, to multivariate observations. Statistical inference procedures are derived for the parameters of the multivariate location-scale model, which is related to the multivariate Birge ratio method. The new approach provides an alternative to the methods based on the application of the multivariate random effects model, which is commonly used for multivariate meta-analyses and inter-laboratory comparisons. In two empirical illustrations, we show that the introduced multivariate Birge ratio approach yields confidence intervals for the elements of the overall mean vector that are considerably narrower than those obtained by the methods derived under the multivariate random effects model.

Двоїстий алгоритм пошуку простих чисел на відрізках великих розмірностей

A matrix model of subsequence of natural numbers with a multiplicative basis from the first prime numbers is proposed. The matrix model is a square matrix, where vector columns are arithmetic progressions with difference and number of progressions equal to product of base elements. By crossing out arithmetic progressions with first terms which are multiples of base elements, we obtain a symmetric sparse matrix that contains all the prime numbers of subsequence of natural numbers, except for the base ones, which increases the density of prime numbers in sparse matrices. Sparse matrices are not formed clearly, only the vector of first terms of arithmetic progressions is formed. Properties of sparse matrices have been proven. Formulas that speed up the calculation of compound numbers in arithmetic progressions have been derived, the structures of vector elements of first terms of arithmetic progressions have been determined, the connectivity of symmetric parts of sparse matrices has been investigated. With the expansion of the base the number of pairs of elements with the difference equal to the power of two («twins», «fours», etc.) increases in the vector of first terms. This is a necessary condition for the existence of constants for which linear equations of two variables can have an infinite set of solutions in prime numbers. The irregularity of distribution of prime numbers in subsequences of natural numbers is related to the structure of elements of the vector of first terms. An algorithm for finding prime numbers on segments of large dimensions with a parallel calculation process has been built. The proposed algorithm is binary to algorithms for sifting subsequences of natural numbers by prime divisors. In these algorithms, it is not possible to parallelize the calculation process, since screening procedure requires storing the numerical information from the preceding steps (vector model of array processing). The binary algorithm calculates compound numbers in each pair of arithmetic progressions with symmetric first terms simultaneously, using only vector of the first terms of arithmetic progressions, which makes possible processing of large-dimensional arrays

Snapshot Multi-Wavelength Birefringence Imaging.

A snapshot multi-wavelength birefringence imaging measurement method was proposed in this study. The RGB-LEDs at wavelengths 463 nm, 533 nm, and 629 nm were illuminated with circularly polarized light after passing through a circular polarizer. The transmitted light through the birefringent sample was captured by a color polarization camera. A single imaging process captured light intensity in four polarization directions (0°, 45°, 90°, and 135°) for each of the three RGB spectral wavelength channels, and subsequently measured the first three elements of Stokes vectors (S0, S1, and S2) after the sample. The birefringence retardance and fast-axis azimuthal angle were determined simultaneously. An experimental setup was constructed, and polarization response matrices were calibrated for each spectral wavelength channel to ensure the accurate detection of Stokes vectors. A polymer true zero-order quarter-wave plate was employed to validate measurement accuracy and repeatability. Additionally, stress-induced birefringence in a PMMA arch-shaped workpiece was measured both before and after the application of force. Experimental results revealed that the repeatability of birefringence retardance and fast-axis azimuthal angle was better than 0.67 nm and 0.08°, respectively. This approach enables multispectral wavelength, high-speed, high-precision, and high-repeatability birefringence imaging measurements through a single imaging session.

Sparse reconstruction of sound field using pattern-coupled Bayesian compressive sensing.

Conventional near-field acoustic holography based on compressive sensing either does not fully exploit the underlying block-sparse structures of the signal or suffers from a mismatch between the actual and predefined block structure due to the lack of prior information about block partitions, resulting in poor accuracy in sound field reconstruction. In this paper, a pattern-coupled Bayesian compressive sensing method is proposed for sparse reconstruction of sound fields. The proposed method establishes a hierarchical Gaussian-Gamma probability model with a pattern-coupled prior based on the equivalent source method, transforming the sound field reconstruction problem into recovering the sparse coefficient vector of the equivalent source strengths within the compressive sensing framework. A set of hyperparameters is introduced to control the sparsity of each element in the sparse coefficient vector of the equivalent source strengths, where the sparsity of each element is determined by both its own hyperparameters and those of its immediate neighbors. This approach enables the promotion of block sparse solutions and achieves better performance in solving for the sparse coefficient vector of the equivalent source strengths without prior information of block partitions. The effectiveness and superiority of the proposed method in reconstructing sound fields are verified by simulations and experiments.

Intuitionistic fuzzy twin proximal SVM with fuzzy hyperplane and its application in EEG signal classification

Twin support vector machine (TSVM) is a contemporary machine learning technique to tackle classification and regression problems. However, TSVM lacks the ability to differentiate between support vectors and noises since it neglects the positional information of input data samples, and hence it is sensitive to noises. Additionally, it fails to consider uncertainties associated with the data, which reduces its capacity for generalization. To address these drawbacks, we propose a novel fuzzy hyperplane based intuitionistic fuzzy twin proximal support vector machine. The first significant feature of the proposed approach is that it gives an intuitionistic fuzzy number based on the relevance to each data vector. This efficiently reduces the impact of noise and outliers by leveraging the local neighborhood information within the data points by incorporating membership and non-membership weights. Secondly, all the parameters present in the model are fuzzy variables, including the offset term and the elements of the normal vector. The suggested fuzzy hyperplane successfully reflects the inherent ambiguity prevalent in real-world categorization problems by reflecting vagueness in the input data through the use of fuzzy variables. The model’s efficiency is enhanced by solving two systems of linear equations to obtain two non-parallel classifiers rather than solving two quadratic programming problems as in standard TSVM. Utilizing non-linear kernel functions within the feature space enables the method to effectively identify complex patterns or non-linear relationships within the datasets. In order to demonstrate the effectiveness of the suggested approach, extensive computer experiments have been conducted on a set of eighteen benchmark datasets with both linear and non-linear kernels. In addition, rigorous statistical analysis, including Friedman and post-hoc Nemenyi tests, have been employed to assess the significance of the observed performance differences. Furthermore, we also performed numerical experiments utilizing linear, Gaussian, and polynomial kernels to classify electroencephalogram (EEG) signals. The outcomes of the experiment are analyzed in terms of average accuracy, processing time, and F-measure. The results demonstrate that the proposed method outperforms existing methods and achieves better generalization.

Semantic aware-based instruction embedding for binary code similarity detection.

Binary code similarity detection plays a crucial role in various applications within binary security, including vulnerability detection, malicious software analysis, etc. However, existing methods suffer from limited differentiation in binary embedding representations across different compilation environments, lacking dynamic high-level semantics. Moreover, current approaches often neglect multi-level semantic feature extraction, thereby failing to acquire precise semantic information about the binary code. To address these limitations, this paper introduces a novel detection solution called BinBcla. This method employs an enhanced pre-training model to generate instruction embeddings with dynamic semantics for binary functions. Subsequently, multi-feature fusion technique is utilized to extract local semantic information and long-distance global features from the code, respectively, employing self-attention to comprehend the structure information of the code. Finally, an improved cosine similarity method is employed to learn relationships among all elements of the distance vectors, thereby enhancing the model's robustness to new sample functions. Experiments are conducted across different architectures, compilers, and optimization levels. The results indicate that BinBcla achieves higher accuracy, precision and F1 score compared to existing methods.

A Lambda Layer-Based Convolutional Sequence Embedding Model for Click-Through Rate Prediction

In the era of intelligent economy, the click-through rate (CTR) prediction system can evaluate massive service information based on user historical information, and screen out the products that are most likely to be favored by users, thus realizing customized push of information and achieve the ultimate goal of improving economic benefits. Sequence modeling is one of the main research directions of CTR prediction models based on deep learning. The user's general interest hidden in the entire click history and the short-term interest hidden in the recent click behaviors have different influences on the CTR prediction results, which are highly important. In terms of capturing the user's general interest, existing models paid more attention to the relationships between item embedding vectors (point-level), while ignoring the relationships between elements in item embedding vectors (union-level). The Lambda layer-based Convolutional Sequence Embedding (LCSE) model proposed in this paper uses the Lambda layer to capture features from click history through weight distribution, and uses horizontal and vertical filters on this basis to learn the user's general preferences from union-level and point-level. In addition, we also incorporate the user's short-term preferences captured by the embedding-based convolutional model to further improve the prediction results. The AUC (Area Under Curve) values of the LCSE model on the datasets Electronic, Movie & TV and MovieLens are 0.870 7, 0.903 6 and 0.946 7, improving 0.45%, 0.36% and 0.07% over the Caser model, proving the effectiveness of our proposed model.

Multiscale collaborative representation for face recognition via class-information fusion

One of the most challenging issues in face recognition is having only a limited number of training images. Multiscale patch collaborative representation (MSPCRC) is an effective approach to address this problem. However, the existing MSPCRC methods only defined a single patch-scale weight vector for all classes to indicate the importance of different patch scales, ignoring the role of class information when fusing multiscale recognition results. In this work, we consider the effect of class information on face recognition and propose a novel multiscale collaborative-representation face recognition method. Specifically, we first construct the multiscale decision matrices of image subsets from different classes according to patch collaborative representation, and define a patch-scale weight vector for each class to describe the importance of different patch scales. Each element in a scale-weight vector represents the weight value of a certain scale in the corresponding class. Then, we construct the respective optimization objective function for each class, which takes into account the classification information both from the same class and from different classes. Finally, we propose a multiscale fusion-recognition output rule based on the patch-weight vectors. Experimental results demonstrate that the proposed method enhances classification accuracy by approximately 2% to 5% across multiple datasets, surpassing the majority of the competing methods.

The optimal metric for viral genome space

Understanding the structural similarity between genomes is pivotal in classification and phylogenetic analysis. As the number of known genomes rockets, alignment-free methods have gained considerable attention. Among these methods, the natural vector method stands out as it represents sequences as vectors using statistical moments, enabling effective clustering based on families in biological taxonomy. However, determining an optimal metric that combines different elements in natural vectors remains challenging due to the absence of a rigorous theoretical framework for weighting different k-mers and orders. In this study, we address this challenge by transforming the determination of optimal weights into an optimization problem and resolving it through gradient-based techniques. Our experimental results underscore the substantial improvement in classification accuracy achieved by employing these optimal weights, reaching an impressive 92.73% on the testing set, surpassing other alignment-free methods. On one hand, our method offers an outstanding metric for virus classification, and on the other hand, it provides valuable insights into feature integration within alignment-free methods.

A Novel Hybrid Binary Bat Algorithm for Global Optimization

In this article, a novel hybrid binary bat algorithm named HBBA is proposed for global optimization problems. First, to avoid simultaneous updating of bat velocity's dimensional components, i.e., elements of velocity vector, a random black hole model is modified to adapt to binary algorithm for updating in unknown spaces for each dimensional component individually. Through this way, the search ability of bats around the current group best is increased greatly. Second, a time-varying v-shaped transfer function, rather than a time-invariant one as in closely related works, is proposed to map velocity in continuous search space to a binary one. This accelerates the speed to switch individuals' positions, i.e., solutions in binary space. Third, a chaotic map is utilized to replace monotonous parameters in original binary bat algorithm, which is beneficial for avoiding premature convergence. Simulation results demonstrate the effectiveness of the proposed algorithm by three types of benchmark functions and unit commitment problem.

Positive definiteness of infinite and finite dimensional generalized Hilbert tensors and generalized Cauchy tensor

An Infinite and finite dimensional generalized Hilbert tensor with a is positive definite if and only if a>0. The infinite dimensional generalized Hilbert tensor related operators F∞ and T∞ are bounded, continuous and positively homogeneous. A generalized Cauchy tensor of which generating vectors are c,d is positive definite if and only if every element of vector d is not zero and each element of vector c is positive and mutually distinct. The 4th order n-dimensional generalized Cauchy tensor is matrix positive semi-definite if and only if every element of generating vector c is positive. Finally, the other properties of generalized Cauchy tensor are presented.

Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy

The palmprint recognition system has gained significant attention in security and law enforcement due to its unique features, such as principle lines, ridges, and wrinkles. However, many existing methods for extracting these features have limited accuracy, especially when the image illumination varies or the size of the processed pixels increases. Previous studies have shown that the local binary patterns (LBP) algorithm is effective for palmprint recognition due to the rich texture characteristics of a palmprint. In this paper, we propose a new technique for a robust contact-based palmprint identification system using vertical-LBP and KAZE feature detection. Our technique aims to improve recognition accuracy by using KAZE, which is a nonlinear diffusion approach that extracts nonlinear features from the evolution of the illuminance of an image. We also utilize principal component analysis (PCA) to reduce the dimensionality of the generated descriptor vector elements. The proposed method was tested on the PolyU database and achieved recognition accuracy of 99.7%.

Adjustment of mutant vector elements in the differential evolution method for solving the problem of power shortage minimization in electric power systems

The article is aimed at increasing the effectiveness of numerical optimization methods in calculations of problems concerning power shortage minimization in electric power systems, specifically the differential evolution (DE) method and its variations–aDE and jDE. Experimental studies and testing of the proposed adjustments were carried out on complex electric power systems of different order. These systems are represented and realized by means of mathematical models of power shortage minimization with the possibility of their analysis using the developed software package as part of adequacy assessment. The performed analysis of the DE method elements and the existing variants of the mutation process revealed that the existing approaches can be further modified. This can subsequently increase the speed of problem-solving. It is shown that the main changes include an additional check that the mutant vectors meet the upper and lower bounds, and if they fail to do so, three adjustment options are considered. Existing approaches propose to generate new vector elements beyond the bounds by applying random numbers within the bounds. The present authors propose to use the “projections” of the found vector elements, i.e., to use the values of upper or lower bounds when they are exceeded for a particular element as mutant vector values. The implemented method involving the adjustment of mutant vector elements is shown to offer an advantage of a 47% reduction in problem-solving time over existing adjustment methods while maintaining the same accuracy. It is noted that aDE and jDE are the most effective variations for solving stated problems. The obtained results of experimental studies confirm the effectiveness and advantages of applying the proposed adjustment method in mutation process of in the form of “projections”, as well as using aDE and jDE variations of the DE method to solve the problems of power shortage minimization in electric power systems.

Exploiting Discrepancy in Feature Statistic for Out-of-Distribution Detection

Recent studies on out-of-distribution (OOD) detection focus on designing models or scoring functions that can effectively distinguish between unseen OOD data and in-distribution (ID) data. In this paper, we propose a simple yet novel ap- proach to OOD detection by leveraging the phenomenon that the average of feature vector elements from convolutional neural network (CNN) is typically larger for ID data than for OOD data. Specifically, the average of feature vector elements is used as part of the scoring function to further separate OOD data from ID data. We also provide mathematical analysis to explain this phenomenon. Experimental evaluations demonstrate that, when combined with a strong baseline, our method can achieve state-of-the-art performance on several OOD detection benchmarks. Furthermore, our method can be easily integrated into various CNN architectures and requires less computation. Source code address: https://github.com/SYSU-MIA-GROUP/statistical_discrepancy_ood.

Condition monitoring for bridge cables using time-history area of cable forces under stochastic traffic flow

Damage or condition anomalies of stay cables will directly affect the safety of bridges in operation. As an important load type, the cyclic action of stochastic traffic flow (STF) is a main cause of cable performance degradation. However, due to its time-varying and complexity, there have been few studies on the anomaly diagnosis of stay cables under STF loading. Therefore, this paper proposes a novel condition monitoring method for stay cables based on the time-history area (THA) of cable forces under STF loading. First, mechanical analysis was performed to establish the theoretical relationship between THA and STF. The feature vector and matrix to characterize the normal condition of stay cables were developed based on the above relationship. Second, an online diagnosis model was established using the constant correlation between feature vector elements in the health state. On this basis, the Mahalanobis-squared distance (MSD) is employed to define an early warning index, which is used to inspect abnormal changes in cable performance. Third, according to numerical analysis, the rationality of the theoretical derivation was further confirmed and the feature vector variations in different simulated cases were presented. Finally, the effectiveness and practicability of the proposed method was verified on an actual cable-stayed bridge. The results demonstrate that the established online model has satisfactory diagnostic capabilities, which can accurately monitor the service condition of stay cables.

Extension VM: Interleaved Data Layout in Vector Memory

While vector architecture is widely employed in processors for neural networks, signal processing, and high-performance computing; however, its performance is limited by inefficient column-major memory access. The column-major access limitation originates from the unsuitable mapping of multidimensional data structures to two-dimensional vector memory spaces. In addition, the traditional data layout mapping method creates an irreconcilable conflict between row- and column-major accesses. Ideally, both row- and column-major accesses can take advantage of the bank parallelism of vector memory. To this end, we propose the Interleaved Data Layout (IDL) method in vector memory, which can distribute vector elements into different banks regardless of whether they are in the row- or column-major category, so that any vector memory access can benefit from bank parallelism. Additionally, we propose an Extension Vector Memory (EVM) architecture to achieve IDL in vector memory. EVM can support two data layout methods and vector memory access modes simultaneously. The key idea is to continuously distribute the data that needs to be accessed from the main memory to different banks during the loading period. Thus, EVM can provide a larger spatial locality level through careful programming and the extension ISA support. The experimental results showed a 1.43-fold improvement of state-of-the-art vector processors by the proposed architecture, with an area cost of only 1.73%. Furthermore, the energy consumption was reduced by 50.1%.

Land Use Change from Natural Tropical Forests to Managed Ecosystems Reduces Gross Nitrogen Production Rates and Increases the Soil Microbial Nitrogen Limitation.

Understanding the underlying mechanisms of soil microbial nitrogen (N) utilization under land use change is critical to evaluating soil N availability or limitation and its environmental consequences. A combination of soil gross N production and ecoenzymatic stoichiometry provides a promising avenue for nutrient limitation assessment in soil microbial metabolism. Gross N production via 15N tracing and ecoenzymatic stoichiometry through the vector and threshold element ratio (Vector-TER) model were quantified to evaluate the soil microbial N limitation in response to land use changes. We used tropical soil samples from a natural forest ecosystem and three managed ecosystems (paddy, rubber, and eucalyptus sites). Soil extracellular enzyme activities were significantly lower in managed ecosystems than in a natural forest. The Vector-TER model results indicated microbial carbon (C) and N limitations in the natural forest soil, and land use change from the natural forest to managed ecosystems increased the soil microbial N limitation. The soil microbial N limitation was positively related to gross N mineralization (GNM) and nitrification (GN) rates. The decrease in microbial biomass C and N as well as hydrolyzable ammonium N in managed ecosystems led to the decrease in N-acquiring enzymes, inhibiting GNM and GN rates and ultimately increasing the microbial N limitation. Soil GNM was also positively correlated with leucine aminopeptidase and β-N-acetylglucosaminidase. The results highlight that converting tropical natural forests to managed ecosystems can increase the soil microbial N limitation through reducing the soil microbial biomass and gross N production.

Evolving Paradigms of Recombinant Protein Production in Pharmaceutical Industry: A Rigorous Review

Many beneficial proteins have limited natural availability, which often restricts their supply and thereby reduces their potential for therapeutic or industrial usage. The advent of recombinant DNA (rDNA) technology enables the utilization of different microbes as surrogate hosts to facilitate the production of these proteins. This microbial technology continues to evolve and integrate with modern innovations to develop more effective approaches for increasing the production of recombinant biopharmaceuticals. These strategies encompass fermentation technology, metabolic engineering, the deployment of strong promoters, novel vector elements such as inducers and enhancers, protein tags, secretion signals, synthetic biology, high-throughput devices for cloning, and process screening. This appraisal commences with a general overview regarding the manufacture of recombinant proteins by microbes and the production of biopharmaceuticals, their trends towards the development of biopharmaceuticals, and then discusses the approaches adopted for accomplishing this. The design of the upstream process, which also involves host selection, vector design, and promoter design, is a crucial component of production strategies. On the other hand, the downstream process focuses on extraction and purification techniques. Additionally, the review covers the most modern tools and resources, methods for overcoming low expression, the cost of producing biopharmaceuticals in microbes, and readily available recombinant protein products.

Mouse Gene-Targeting Strategies for Maximum Ease and Versatility.

Well-planned strategies are an essential prerequisite for any mutational analysis involving gene targeting. Consideration of the advantages or disadvantages of different methods will aid in the production of a final product that is both technically feasible and versatile. Strategies for gene-targeting experiments in the mouse are discussed, including the rationale behind some of the common elements of gene-targeting vectors, such as homologous DNA and the use of different site-specific recombinases. We detail positive and negative selection as well as screening strategies for homologous recombination events in embryonic stem (ES) cells. For the planning stages of making different types of alleles, we first consider general strategies and then provide details specific to either homologous recombination in ES cells or making alleles by gene editing with CRISPR-Cas in preimplantation embryos. The types of alleles considered are null or knockout alleles, reporter gene knock-in alleles, point mutations, and conditional null alleles.

A neural tensor decomposition model for high-order sparse data recovery

Tensor decomposition has attracted wide attention in the low-rank tensor completion (LRTC) problem because of its marvelous recovering ability to missing entries. However, previous LRTC methods are generally based on linear and shallow models, which are prone to overfitting when the data is sparse, resulting in significantly degraded performance. Meanwhile, the models are highly sensitive and suffer from the difficult rank selection problem. To address these issues, we propose an effective and novel tensor-ring (TR) decomposition method based on the convolutional computation (ConvTR), which can be regarded as a natural extension of deep learning models for the LRTC problem. Specifically, ConvTR employs a multi-layer convolutional neural network (CNN) to model the complex interactions between TR factors. Each element in the index vector of the observation tensor can be embedded as a corresponding tensor slice in the factor tensor decomposed by the TR model. These individual slice matrices are then concatenated to get a wider matrix used for extracting the nonlinear features by feeding them into a 2D convolutional layer. A fully-connected layer is utilized to aggregate the final convoluted features to a scalar value, which is the desired missing entry indexed by the original index vector exactly. Extensive experiments on various common datasets verified the effectiveness of the proposed method and demonstrated its superior to the traditional TR-based completion methods and other state-of-the-art network-based methods.