Elements Of Vector Research Articles

ADVANCED MALICIOUS SOFTWARE DETECTION USING DNN

The special component of malicious software analysis is advanced malicious software analysis which implicates interested the main framework of malicious software that can be executed after executing it and aggressive malicious software investigation depend on inquisitive of the practice of malicious software after running it in a composed habitat. Advanced malicious software analysis is usually performed by contemporary anti-malicious software operating systems using signature-based analysis.
 The purpose of this research is to propose also decide a DNN for the progressive identification of portable files to study the features of portable executable malicious software to minimize the occurrence of distorted likeness when aware of advanced malicious software. The model proposed in this study is a NN with a Dropout model contrary to a resolution tree model to examine how well it performs in detecting real malicious PE files. Setup-skeptic methods are used to extract features from files. The dataset is used to train the proposed approach and measure outcomes by alternative common malicious software datasets.
 The results from this study illustrate that the use of simple DNNs to study PE vector elements is not only efficient but more fewer system comprehensive than the traditional interested disclosure approach. The model proposed in this study achieves an A-UC of ninety-nine point eight with ninety accurate specifics at one percent inaccurate specific on the R-OC curve. For shows that this model has the potential to complement or replace conventional anti-malicious software operating systems so for future research, it is proposed to implement this model practically.

Intelligent Monitoring System Based on Noise-Assisted Multivariate Empirical Mode Decomposition Feature Extraction and Neural Networks.

Because of the nonlinearity and nonstationarity in the vibration signals of some rotating machinery, the analysis of these signals using conventional time- or frequency-domain methods has some drawbacks, and the results can be misleading. In this paper, a couple of features derived from multivariate empirical mode decomposition (MEMD) are introduced, which overcomes the shortcomings of the traditional features. A wind turbine gearbox and its bearings are investigated as rotating machinery. In this method, two types of feature structures are extracted from the decomposed signals resulting from the MEMD algorithm, called intrinsic mode function (IMF). The first type of feature vector element is the energy moment of effective IMFs. The other type of vector elements is amplitudes of a signal spectrum at the characteristic frequencies. A correlation factor is used to detect effective IMFs and eliminate the redundant IMFs. Since the basic MEMD algorithm is sensitive to noise, a noise-assisted extension of MEMD, NA-MEMD, is exploited to reduce the effect of noise on the output results. The capability of the proposed feature vector in health condition monitoring of the system is evaluated and compared with traditional features by using a discrimination factor. The proposed feature vector is utilized in the input layer of the classical three-layer backpropagation neural network. The results confirm that these features are appropriate for intelligent fault detection of complex rotating machinery and can diagnose the occurrence of early faults.

Partial muon capture rates in A=3 and A=6 nuclei with chiral effective field theory

Searches for neutrinoless double-$\ensuremath{\beta}$ decay rates are crucial in addressing questions within fundamental symmetries and neutrino physics. The rates of these decays depend not only on unknown parameters associated with neutrinos, but also on nuclear properties. In order to reliably extract information about the neutrino, one needs an accurate treatment of the complex many-body dynamics of the nucleus. Neutrinoless double-$\ensuremath{\beta}$ decays take place at momentum transfers on the order of $100\phantom{\rule{0.28em}{0ex}}\mathrm{MeV}/c$ and require both nuclear electroweak vector and axial current matrix elements. Muon capture, a process in the same momentum transfer regime, has readily available experimental data to validate these currents. In this Letter, we present results of ab initio calculations of partial muon capture rates for $^{3}\mathrm{He}$ and $^{6}\mathrm{Li}$ nuclei using variational and Green's function Monte Carlo computational methods. We estimate the impact of the three-nucleon interactions, the cutoffs used to regularize two-nucleon $(2N)$ interactions, and the energy range of $2N$ scattering data used to fit these interactions.

Effectiveness of vector intensity measures in probabilistic seismic demand assessment

The vector intensity measures (IMs) has been recommended by many researchers for probabilistic seismic demand assessment (PSDA) because they claimed that using vector IMs could reduce the uncertainty in engineering demand parameters (EDPs) estimates. However, the inclusion of additional vector elements in vector IMs also increases the IM uncertainty, which in return brings additional uncertainty to the EDPs estimates. Thus, the benefit of implementing the vector IMs for PSDA is unclear. The objective of this study will clarify the effectiveness of vector IMs for PSDA. The study discusses the framework of PSDA using scalar IM and vector IMs. Based on the uncertainty in EDPs estimates involved in the framework, we applies theoretical derivative to clarify the hidden aleatory uncertainty in EDPs estimates, which offers a fresh view on PSDA. Using numerical examples of three buildings and Monte Carlo simulation, we further investigate the effectiveness of vector IMs in EDPs estimates by comparing structural response hazards determined by vector IMs and scalar IM. Our study reveals that, for cases when collapse effect is not considered, the use of vector IMs cannot benefit PSDA. Under cases when the collapse effect should be considered in PSDA, the use of vector IMs may improve accuracy of structural response hazard curves. Such improvement is associated with the capability of vector IMs to predict the structural collapse.

Mixed-type multivariate response regression with covariance estimation.

We propose a new method for multivariate response regression and covariance estimation when elements of the response vector are of mixed types, for example some continuous and some discrete. Our method is based on a model which assumes the observable mixed‐type response vector is connected to a latent multivariate normal response linear regression through a link function. We explore the properties of this model and show its parameters are identifiable under reasonable conditions. We impose no parametric restrictions on the covariance of the latent normal other than positive definiteness, thereby avoiding assumptions about unobservable variables which can be difficult to verify in practice. To accommodate this generality, we propose a novel algorithm for approximate maximum likelihood estimation that works “off‐the‐shelf” with many different combinations of response types, and which scales well in the dimension of the response vector. Our method typically gives better predictions and parameter estimates than fitting separate models for the different response types and allows for approximate likelihood ratio testing of relevant hypotheses such as independence of responses. The usefulness of the proposed method is illustrated in simulations; and one biomedical and one genomic data example.

Power balance control of RES integrated power system by deep reinforcement learning with optimized utilization rate of renewable energy

A power balance control method is proposed for renewable energy source (RES) integrated power systems based on deep reinforcement learning (DRL), with the reasonable utilization rate of renewable energy optimized. This method considers the dispatching problems of a high-proportion renewable energy grid from a new perspective. It is proposed that the dispatching of a high-proportion renewable energy grid must consider the reasonable utilization rate of renewable energy and conduct reasonable abandonment of wind and light. And in the offline training of DRL scheduling, the reasonable utilization rate is used as the element of the state vector to train the final power grid DRL control strategy. The control strategy has verified its effectiveness in the IEEE 14-bus system with the supporting datasets.

Globin vector regulatory elements are active in early hematopoietic progenitor cells

The globin genes are archetypal tissue-specific genes that are silent in most tissues but for late-stage erythroblasts upon terminal erythroid differentiation. The transcriptional activation of the β-globin gene is under the control of proximal and distal regulatory elements located on chromosome 11p15.4, including the β-globin locus control region (LCR). The incorporation of selected LCR elements in lentiviral vectors encoding β and β-like globin genes has enabled successful genetic treatment of the β-thalassemias and sickle cell disease. However, recent occurrences of benign clonal expansions in thalassemic patients and myelodysplastic syndrome in patients with sickle cell disease call attention to the non-erythroid functions of these powerful vectors. Here we demonstrate that lentivirally encoded LCR elements, in particular HS1 and HS2, can be activated in early hematopoietic cells including hematopoietic stem cells and myeloid progenitors. This activity is position-dependent and results in the transcriptional activation of a nearby reporter gene in these progenitor cell populations. We further show that flanking a globin vector with an insulator can effectively restrain this non-erythroid activity without impairing therapeutic globin expression. Globin lentiviral vectors harboring powerful LCR HS elements may thus expose to the risk of trans-activating cancer-related genes, which can be mitigated by a suitable insulator.

In-depth 3D magnetic inversion of basement relief

We have developed an iterative, efficient, and stable 3D magnetic inversion method to estimate the depth to the basement of a sedimentary basin, the volume of which is approximated by a grid of rectangular prisms with tops at an arbitrary surface and thicknesses estimated iteratively. To compute the thicknesses corrections at the kth iteration, we first compute an approximate sparse sensitivity matrix in which the nonnull elements of a given line are obtained by differentiating the fitting function at the observation position (which occupies the center of a small moving data window) relative to the thicknesses of the prisms situated inside the window. By moving the data window around, all nonnull elements of all lines are computed. This sparse matrix is used to calculate an approximate Gauss-Newton gradient and an approximate diagonal Gauss-Newton Hessian matrix. Finally, by dividing each element of the gradient vector by the corresponding diagonal element of the approximate Hessian matrix, we obtain the corrections of all parameters at a given iteration. The solution is stable because of (1) the small parameter corrections inherent to the method, combined with the initial estimate at the surface and (2) the application of a moving average operator to the solution. Any magnetization orientation, except horizontal (and parallel to any vertical side of the prisms), yields good results. In the case of magnetization inclinations of approximately 45°, the rectangular prisms may induce spurious ripples on the surface of the estimated relief, which can be eliminated by simultaneously reducing the horizontal dimensions of the prisms and applying an appropriate moving average to the solution. Application of our method to real data in a rift area discloses a confined basin with steep and asymmetric borders, confirming the practical utility of the method.

Choosing optimal model parameterization for improving the accuracy of refraction seismic tomography

ABSTRACTSeismic ray tomography is a popular tool for reconstructing seismic velocity models from traveltime data. Here we study how the model parameterization affects the resolution and accuracy of the tomographic inversion for the near‐surface model building. In particular, we consider the weighting of the elements of the model perturbation vector based on the values of the initial velocity model. When the model parameters are defined in terms of velocities, then the tomographic‐inversion resolution is better for the shallow part but degrades for the deeper part of the model. The opposite is true when the model parameters are defined in terms of slowness values. This effect is associated with the method of forming the tomographic matrix. When linearizing the tomography problem for different model parameters, the matrix elements have different weight coefficients. This affects the inversion results and can lead to large errors. We suggest a new parameterization (in‐between the velocity and the slowness) that provides better quality of the tomographic inversion and balanced resolution between the shallow and deeper part of the model. The good performance of this new parameterization is confirmed by a series of synthetic tests and one real‐data example.

Interferometric weak value of polarization observable and differential Jones matrix algebra

The quantification of simultaneously present weak polarization anisotropy effects are of practical interest from polarimetric and metrological perspective. Recently, in Modak et al (2021 Phys. Rev. A 103 053518), we experimentally demonstrated a classical analog of post-selected quantum weak measurement through optical interferometry to amplify all possible weak polarization anisotropy effects individually. Here, we propose an extension of this interferometric framework for applying it in a realistic scenario to quantify simultaneously present polarization anisotropy effects. Moreover, a clear correspondence of differential Jones matrix approach with the present scheme is indicated. The proposed scheme enables the measurement of differential Jones matrices through characteristic Stokes vector elements. Our proposal leads to a new class of polarimeter for experimental detection of differential Jones matrix of non-depolarizing anisotropic medium exhibiting simultaneous multiple polarimetric effects of tiny magnitude.

Imported Dengue Case Numbers and Local Climatic Patterns Are Associated with Dengue Virus Transmission in Florida, USA.

Simple SummaryDengue virus transmission from Aedes aegypti mosquitoes to humans is a growing issue in the state of Florida in the United States of America. The majority of cases have occurred in the southernmost counties, even though Ae. aegypti are present throughout much of Florida. To understand this geographic partitioning of dengue fever cases in Florida, we compiled ten years (2009–2019) of county-level data on human, environmental, and vector elements and identified risk factors for laboratory-confirmed cases of dengue virus incidence in the state. Counties with the highest average temperatures, highest minimum temperatures, and lowest maximum temperatures were significantly more likely to have local dengue virus transmission. Additionally, moderate rainfall and an increasing number of travel-related dengue cases were also significantly associated with local transmission. This is the first study of its kind to identify county-level risk factors for dengue incidence in Florida. This study provides a parsimonious model that may be useful for prediction of future dengue occurrence based on routinely collected, publicly available data sources. Our findings also highlight the importance of travel-related dengue fever cases to the state as well as environmental conditions that promote dengue virus transmission in Florida by Ae. aegypti.Aedes aegypti mosquitoes are the main vector of dengue viruses globally and are present throughout much of the state of Florida (FL) in the United States of America. However, local transmission of dengue viruses in FL has mainly occurred in the southernmost counties; specifically Monroe and Miami-Dade counties. To get a better understanding of the ecologic risk factors for dengue fever incidence throughout FL, we collected and analyzed numerous environmental factors that have previously been connected to local dengue cases in disease-endemic regions. We analyzed these factors for each county-year in FL, between 2009–2019, using negative binomial regression. Monthly minimum temperature of 17.5–20.8 °C, an average temperature of 26.1–26.7 °C, a maximum temperature of 33.6–34.7 °C, rainfall between 11.4–12.7 cm, and increasing numbers of imported dengue cases were associated with the highest risk of dengue incidence per county-year. To our knowledge, we have developed the first predictive model for dengue fever incidence in FL counties and our findings provide critical information about weather conditions that could increase the risk for dengue outbreaks as well as the important contribution of imported dengue cases to local establishment of the virus in Ae. aegypti populations.

Elementary vectors and autocatalytic sets for resource allocation in next-generation models of cellular growth.

Traditional (genome-scale) metabolic models of cellular growth involve an approximate biomass “reaction”, which specifies biomass composition in terms of precursor metabolites (such as amino acids and nucleotides). On the one hand, biomass composition is often not known exactly and may vary drastically between conditions and strains. On the other hand, the predictions of computational models crucially depend on biomass. Also elementary flux modes (EFMs), which generate the flux cone, depend on the biomass reaction. To better understand cellular phenotypes across growth conditions, we introduce and analyze new classes of elementary vectors for comprehensive (next-generation) metabolic models, involving explicit synthesis reactions for all macromolecules. Elementary growth modes (EGMs) are given by stoichiometry and generate the growth cone. Unlike EFMs, they are not support-minimal, in general, but cannot be decomposed “without cancellations”. In models with additional (capacity) constraints, elementary growth vectors (EGVs) generate a growth polyhedron and depend also on growth rate. However, EGMs/EGVs do not depend on the biomass composition. In fact, they cover all possible biomass compositions and can be seen as unbiased versions of elementary flux modes/vectors (EFMs/EFVs) used in traditional models. To relate the new concepts to other branches of theory, we consider autocatalytic sets of reactions. Further, we illustrate our results in a small model of a self-fabricating cell, involving glucose and ammonium uptake, amino acid and lipid synthesis, and the expression of all enzymes and the ribosome itself. In particular, we study the variation of biomass composition as a function of growth rate. In agreement with experimental data, low nitrogen uptake correlates with high carbon (lipid) storage.

Stop codon readthrough contexts influence reporter expression differentially depending on the presence of an IRES.

Background:Previously we reported the discovery of stop codon readthrough in AMD1 mRNA followed by ribosome stalling at the end of a conserved Open Reading Frame (ORF) that we termed AMD1. To explain the severe suppression of reporters fused to AMD1 tail we proposed a mechanism invoking ribosome queueing. In the original study, we tested this hypothesis, by placing the reporter stop codon in the context of readthrough permissive sequences in a dual reporter vector with downstream reporter expression driven by the EMCV IRES. In accordance with our hypothesis, we observed a striking disproportional reduction of upstream reporter activity in response to increased readthrough levels. Methods:Here we employ dual luciferase assays, western blotting and RT-qPCR to explore the effects of test sequences downstream to the reporter stop codon on its expression in dual and monocistronic reporter vectors. Results:With the dual reporter system, the disproportionate reduction of upstream reporter activity is not specific to AMD1 tail and occurs as long as the readthrough stop codon context is present at the end of the reporter's ORF. In a monocistronic vector without an IRES, the test sequences had distinct effects which were reflective of their properties e.g., AMD1 tail inhibitory effect. We further show by employing RT-qPCR that in the IRES vectors, the Fluc activity levels measured by the luciferase assay are an accurate proxy of RNA levels. Conclusions:While our findings provide little new information regarding the functional role of AMD1 tail, they raise caution for the use of viral IRES elements in expression vectors for studying mechanisms of mRNA translation. These findings may also be pertinent to the natural properties of readthrough permissive sequences and of IRES elements, though these require a separate investigation.

Evaluation of Chinese freight train bearing condition based on spatiotemporal feature extraction

Bearing failure in freight trains can directly affect operational safety. Therefore, bearing condition evaluation is of practical and critical significance. In this paper, a new method of evaluating the condition of the bearings is proposed based on spatiotemporal feature extraction with bearing temperature data. First, the temperature time series is divided into many sub-sequences with a sliding time window. Second, based on the forms of series anomaly, point anomaly, and pattern anomaly in time series, the spatiotemporal features extraction framework is presented, which combines manual feature extraction with machine learning methods. Specifically, series classification, distribution-based and increment-based outlier detection, model-based time series anomaly detection methods are utilized to extract abnormal features in temporal, spatial, and spatiotemporal dimensions. Third, a penalty vector is constructed by an ergodic accumulation of penalty values for the outliers. And the weights to each element in the penalty vector are allocated using correlation analysis. The penalty vector and the weights are then employed to calculate the bearing health indicator, which can quantify the health condition and determine the severity of the potential fault. Finally, the validity of the proposed evaluation method is verified with on-site historical temperature data of 15,120 bearings on 35 trains, which demonstrates an accuracy of more than 94%. The method can provide early warning for an average of 161 h before hotbox alarms. The results indicate the proposed method can effectively evaluate the bearing condition and provide supportive information for condition-based maintenance.

PIANO: A fast parallel iterative algorithm for multinomial and sparse multinomial logistic regression

Multinomial Logistic Regression is a well-studied tool for classification and has been widely used in fields like image processing, computer vision and, bioinformatics, to name a few. Under a supervised classification scenario, a Multinomial Logistic Regression model learns a weight vector to differentiate between any two classes by optimizing over the likelihood objective. With the advent of big data, the inundation of data has resulted in large dimensional weight vector and has also given rise to a huge number of classes, which makes the classical methods applicable for model estimation not computationally viable. To handle this issue, we here propose a parallel iterative algorithm: Parallel Iterative Algorithm for MultiNomial LOgistic Regression (PIANO) which is based on the Majorization Minimization procedure, and can parallely update each element of the weight vectors. Further, we also show that PIANO can be easily extended to solve the Sparse Multinomial Logistic Regression problem - an extensively studied problem because of its attractive feature selection property. In particular, we work out the extension of PIANO to solve the Sparse Multinomial Logistic Regression problem with ℓ1 and ℓ0 regularizations. We also prove that PIANO converges to a stationary point of the Multinomial and the Sparse Multinomial Logistic Regression problems. Simulations were conducted to compare PIANO with the existing methods, and it was found that the proposed algorithm performs better than the existing methods in terms of speed of convergence.

Analyzing GCN Aggregation on GPU

Graph convolutional neural networks (GCNs) are emerging neural networks for graph structures that include large features associated with each vertex. The operations of GCN can be divided into two phases - aggregation and combination. While the combination just performs matrix multiplications using trained weights and aggregated features, the aggregation phase requires graph traversal to collect features from adjacent vertices. Even though neural network applications rely on GPU’s massively parallel processing, GCN aggregation kernels exhibit rather low performance since graph processing using compressed graph structures provokes frequent irregular accesses in GPUs. In order to investigate the performance hurdles of GCN aggregation on GPU, we perform an in-depth analysis of the aggregation kernels using real GPU hardware and a cycle-accurate GPU simulator. We first analyze the characteristics of the popular graph datasets used for GCN studies. We reveal the fractions of non-zero elements in feature vectors are diverse among datasets. Based on the observation, we build two types of aggregation kernels that handle uncompressed and compressed feature vectors. Our evaluation exhibits the performance of aggregation can be significantly influenced by kernel design approaches and feature density. We also analyze the individual loads that access the data arrays of the aggregation kernels to specify critical loads. Our analysis reveals the performance of GPU memory hierarchy is influenced by access patterns and feature size of graph datasets. Based on our observations we discuss possible kernel design approaches and architectural ideas that can improve the performance of GCN aggregation.

Biased compensation adaptive gradient algorithm for rational model with time-delay using self-organising maps

This paper develops a biased compensation adaptive gradient decent algorithm for rational models with unknown time-delay. Owing to the unknown time-delay, traditional identification methods cannot be directly applied for such models. To overcome this difficulty, the self-organised maps are proposed, which can obtain the estimates of the time-delay based on the residual errors. Then, an adaptive gradient descent algorithm is introduced to obtain the parameter estimates. Compared with the traditional gradient descent and redundant rule based methods, the proposed method has two advantages: (1) each element in the parameter vector has its own step-size, thus it is more effective than the traditional gradient descent method; (2) the number of the unknown parameters is unchanged, therefore, it has less computational effort than the redundant rule based method. Finally, a simulation experiment is given to show the effectiveness of the proposed algorithm.

SCHOUTEN'S FORCE STRESS TENSOR AND AFFINOR DENSITIES OF POSITIVE WEIGHT

The paper deals with the concept of the force stress pseudotensor and the derivation of equilibrium equations in terms of the Schouten's stress pseudotensor being an affinor density. The definition of Schouten's force stress pseudotensor is mainly based on the notion of a pseudoinvariant element of area. The requisute equations and notions from algebra and the analysis of pseudotensors is revisited. A fundamental orienting pseudoscalar is introduced and discussed. Conventional and non-conventional definitions of the force stress tensor are given. A unit normal vector to a level surface of a pseudoscalar field is introduced. The exceptional importance of using the theory of orientable manifolds in modeling micropolar continua in mechanics of solids is noted. The notion of M-cell and its orientation algorithm are recalled. Algorithms for constructing the tensor elements of the area of M-manifold immersed in N-dimensional space are discussed. The notions of vector, pseudovector, invariant and pseudoinvariant elements of surface area in three-dimensional space are revisited. The possibility of using pseudotensor volume elements of a given integer weight due to the formula for a pseudotensor field transformation to an absolute tensor field by a fundamental orienting pseudoscalar is discussed. Various realisations of covariant differentiation of pseudotensors are considered. Covariant derivatives are given for a pseudoscalar and a contravariant pseudotensor of the second rank of an arbitrary integer weight. The principle of virtual displacements is formulated in terms of pseudo-invariant volume and area elements. The hypothesis of the absolute invariance of the virtual work is assumed, i.e. insensitive to rotations, 3D inversion and mirror reflections. Equations of equilibrium and dynamics are derived in terms of the affinor density of Schouten's force stresses. Equilibrium equations are obtained for the case of using pseudo-invariant volume and area elements of an arbitrary integer weight.

Derivations of Vector Area and Volume Elements in Curved Coordinate Systems for Flux Vector Fields Helping Eye Disease

Our aim in this paper is to interest retinal eye specialists in preventing dry macula degeneration by a special flurry vector field through open or closed curved surfaces. The flux of vector fields through surfaces is based on vector element area and volume element. Therefore, we explain a few geometrical derivations of area and volume elements in curved orthogonal coordinate systems. We hope that by derivation of a spatial vector field flurry against drusen through open or closed surfaces due to the Gauss theorem might select drusen under eye retina cells without destroying the cells and prevent macula degeneration. A changed flurry of a magnetic or electric vector field through a closed line causes an electric or magnetic vector field on the surface closed by the line. We also hope that derivation by Stokes’ and Greens’ theorems, with the help of iron, might help eye cells to get in life.

An Improved Geomagnetic Navigation Method Based on Two-Component Gradient Weighting

During the geomagnetic vector navigation, there are three attitude angles required for coordinate transformation in the process of geomagnetic vector elements’ calculation. However, the yaw angle provided by inertial navigation system (INS) has accumulated error which is inevitable and will seriously reduce navigation accuracy. An improved geomagnetic navigation method based on two-component gradient weighting (TCGW) is proposed in this paper. The horizontal and vertical geomagnetic components can be calculated with roll angle and pitch angle based on the implicit trigonometric theorem in the process of coordinate transformation. And the gradient weights are used as the coefficients to improve the accuracy and universality. The simulations based on four typical types have been performed, and the Monte Carlo simulation results indicated that the proposed method has higher matching accuracy than traditional vector ICCP (VICCP) method. Furthermore, the airborne navigation experiments have been carried out to verify the accuracy and effectiveness of the proposed method.