Elements Of Vector Research Articles

A direct method for solving inverse Sturm–Liouville problems* *Research was supported by CONACYT, Mexico via the projects 222478 and 284470. Research of Vladislav Kravchenko was supported by the Regional mathematical center of the Southern Federal University, Russia.

We consider two main inverse Sturm–Liouville problems: the problem of recovery of the potential and the boundary conditions from two spectra or from a spectral density function. A simple method for practical solution of such problems is developed, based on the transmutation operator approach, new Neumann series of Bessel functions representations for solutions and the Gelfand–Levitan equation. The method allows one to reduce the inverse Sturm–Liouville problem directly to a system of linear algebraic equations, such that the potential is recovered from the first element of the solution vector. We prove the stability of the method and show its numerical efficiency with several numerical examples.

Vector ordering and regression learning‐based ranking for dynamic summarisation of user videos

Dynamic video summarisation (video skimming) is a process of generating a shorter video (video skim) as a summary of a given video, which helps in its easier and quicker comprehension. In this study, an efficient dynamic summarisation approach for user videos is proposed using vector ordering for ranking video units (frames/shots). User videos are casually shot unscripted videos, where skimming involves the selection of its interesting part(s) ignoring many uninteresting ones. The concept of R-ordering of vectors is employed to find a representative frame, which is used to perform relative ranking of the video frames. It is theoretically shown that significance is given to each element of a frame's feature vector while computing the importance scores that lead to the frame ranks used for skimming. Furthermore, the allocation of different weights to the features involved is also achieved using linear and Gaussian process regressions. Through extensive experiments considering several standard datasets with human-labelled ground truth, the proposed approach is demonstrated to be efficient and to perform better than the relevant state-of-the-art.

Bayesian Matching Pursuit-Based Distributed FMCW MIMO Radar Imaging

In this article, to get a high-resolution radar image with distributed frequency modulated continuous waveform multiple-input-multiple-output (FMCW MIMO) radar, Bayesian matching pursuit (BMP)-based imaging methods are proposed, in which the received signals at the distributed FMCW MIMO radars are reformulated in terms of the (azimuth, range) patches in the image region of interest and the maximum a posterior (MAP) estimator that can estimate the azimuth angles and ranges of multiple targets is then derived. For a single FMCW MIMO radar, we first propose the BMP-based imaging, in which the support vector (indicating the presence of targets in the radar image patches) and the associated coefficients (the reflection coefficients of the associated targets) are iteratively updated. Because MAP estimation needs the combinatorial search over all possible candidates to find the nonzero elements in the support vector, an approximated MAP estimation method is newly proposed. In addition, by extending the BMP-based imaging for a single FMCW MIMO radar, we develop a new distributed BMP-based imaging method when multiple FMCW MIMO radars are spatially distributed. Through the simulation, we confirm that the proposed BMP-based radar imaging methods outperform the conventional backprojection method or the orthogonal matching pursuit-based method with slightly increased computational complexity.

Classification of Biomedical Texts for Cardiovascular Diseases with Deep Neural Network Using a Weighted Feature Representation Method.

This study aims to improve the performance of multiclass classification of biomedical texts for cardiovascular diseases by combining two different feature representation methods, i.e., bag-of-words (BoW) and word embeddings (WE). To hybridize the two feature representations, we investigated a set of possible statistical weighting schemes to combine with each element of WE vectors, which were term frequency (TF), inverse document frequency (IDF) and class probability (CP) methods. Thus, we built a multiclass classification model using a bidirectional long short-term memory (BLSTM) with deep neural networks for all investigated operations of feature vector combinations. We used MIMIC III and the PubMed dataset for the developing language model. To evaluate the performance of our weighted feature representation approaches, we conducted a set of experiments for examining multiclass classification performance with the deep neural network model and other state-of-the-art machine learning (ML) approaches. In all experiments, we used the OHSUMED-400 dataset, which includes PubMed abstracts related with specifically one class over 23 cardiovascular disease categories. Afterwards, we presented the results obtained from experiments and provided a comparison with related research in the literature. The results of the experiment showed that our BLSTM model with the weighting techniques outperformed the baseline and other machine learning approaches in terms of validation accuracy. Finally, our model outperformed the scores of related studies in the literature. This study shows that weighted feature representation improves the performance of the multiclass classification.

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 (AMD1 tail). To explain the severe suppression of reporters fused to AMD1 tail we proposed a mechanism invoking ribosome queueing. To test this hypothesis, we placed the reporter stop codon in the context of readthrough permissive sequences in a dual reporter vector with downstream reporter expression governed by 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 assay and western blotting to explore the effects of AMD1 tail and control sequences on reporter 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. The decreased reporter activity that appears to be induced by the readthrough sequence occurs only in reporters containing EMCV IRES. Monocistronic reporters with the same readthrough context sequence exhibit only a modest reduction in reporter activity. Furthermore, in monocistronic vectors, the disproportionate reduction of reporter levels greatly increased when AMD1 tail was translated as a result of readthrough. Such readthrough-mediated reduction was not observed when AMD1 tail was substituted with unrelated sequences in agreement with our original hypothesis. 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.

Constraint Inversion Model of Core Science Complex Network

In order to reduce the research costs, increase the efficiency of scientific researches, and improve the inversion abilities of core technologies, in the paper, a constrained inversion model aiming at tracking the source of core technology was proposed. Firstly, a complex network was built and it treated the most important product or technology as the core node vector. In the network, the social and technical attributes of the product were assigned as the elements of the core node vector. Therefore, the relationships among the core node and other nodes represented connective strength which were fairly important for calculating the path weights. Essentially, the weights were integrated attributes derive from a called score matrix. Then a constrained optimization criterion was employed to evaluate the scores of paths so that the best optimal path was determined. For reducing the number of searching and the times of calculating, all the constraints relationships of node vectors were classified into several types. The experimental results demonstrate that the proposed approach has a superior performance against the current state-of-the art methods over a real case.

Creation of a genetic vector carrying a synthesis bacterial protein gene CAS9 for plant genome editing

Aim. To create a genetic construct carrying the bacterial protein Cas9 gene, the reporter β-glucuronidase gus gene, as well as the marker phosphinotricin-N-acetyltransferase bar gene for plant genome editing. Methods. Molecular-biological, biotechnological, microbiological and bioinformatic methods were used in the study; Golden Gate molecular cloning method was used to create genetic constructs. Results. The genetic construct pSPE2053 which carries the Cas9 endonuclease gene, the gus and bar genes was created; the assembly correctness of all vector elements was confirmed by polymerase chain reaction; the construct was transferred to Escherichia coli and Agrobacterium tumefaciens cells; β-glucuronidase gene expression was verified by histochemical analysis after Nicotiana rustica L transient genetic transformation. Conclusions. The created genetic construct can be used to edit the plant genome for both stable and transient genetic transformation to accumulate recombinant Cas9 protein. The guide RNA sequences may be subsequently transferred into such plants using either stable or transient genetic transformation or traditional crossing methods.
 Keywords: cloning, genetic construction, gus and bar genes, Cas9 endonuclease protein, transient expression.

Ant colony optimization for variable selection in discriminant linear analysis

AbstractA new algorithm using ant colony optimization (ACO) for selection of variables in linear discriminant analysis (LDA) is presented. The role of ACO is explored in the context of LDA classification in which spectral variable multicollinearity is a known cause of generalization problems. The proposed ACO‐LDA presents a metaheuristic that mimics the ant's cooperative behavior, randomly depositing pheromones at vector elements corresponding to the most relevant variables. Such cooperative ant‐like behavior, which is absent in the genetic algorithm, increases the probability of discarding noninformative variables, favoring construction of more parsimonious models than genetic algorithm–linear discriminate analysis (GA‐LDA). The classification performance of ACO‐LDA is assessed in two case studies: (i) classification of edible vegetable oils (with respect to base oil) via ultraviolet–visible (UV‐Vis) spectrometry and (ii) simultaneous classification of tea samples with respect to type and geographic origin via near‐infrared (NIR) spectrometry. In the first study, ACO‐LDA was tested in a data set involving wide absorption bands in the UV region with low‐resolution and strong spectral overlapping. In the second study, its capacity to manage a data matrix with high dimensionality was evaluated. In both studies, ACO‐LDA selected a small subset of variables, which led to correct classifications for almost all of the samples, achieving a performance level similar to the well‐established partial least squares–discriminant analysis (PLS‐DA), and considerably better than GA‐LDA. The use of ACO to select LDA classification variables can minimize generalization problems commonly associated with multicollinearity.

A Capacitor Voltage Balancing Control Strategy for Modular Multilevel Converter

Modular multilevel converter (MMC) is a promising highly modular converter. However, the problems of voltage balancing control strategies in the MMC should be studied in detail. In order to improve the effect of voltage balancing, reduce the amount of calculation, and simplify the switch control of the submodules (SMs), this paper proposes a method that using a matrix to control the switch signals of the SMs based on the nearest level control (NLC) method. First, by sorting the capacitor voltage of the SMs, the required SMs are selected according to the NLC method. Since each arm contains N SMs, it is assumed that each SM corresponds to an N-dimensional column vector. The nth element of column vector corresponding to the nth SM is 1, and the others are 0. Then, the column vectors corresponding to all the SMs are used to form an N × N identity matrix. Secondly, assuming that the identity matrix is E, when the i-th SM is selected, a column vector corresponding to it is selected, such as a(i)= E(:, i), where E(:, i) is the i -th column of E; then, the sum of a(i) is calculated to obtain the switch signals of all SMs of each arm, so as to control the SMs. Finally, the simulation was set up on the Matlab/Simulink, and the proposed method is studied and analyzed.

Sparse system identification for stochastic systems with general observation sequences

Focusing on identification, this paper develops techniques to reconstruct zero and nonzero elements of a sparse parameter vector θ of a stochastic dynamic system with general observation sequences, including stationary time series and feedback control, for which the current input may depend on the past inputs and outputs, system noises as well as exogenous dithers. First, a sparse parameter identification algorithm is introduced based on L2 norm with L1 regularization, where the adaptive weights are adopted in the optimization variables of L1 term. Second, estimates generated by the algorithm are shown to have both set and parameter convergence. That is, sets of the zero and nonzero elements in the parameter θ can be correctly identified with probability one using a finite number of observations, and further estimates of the nonzero elements converge to the true values almost surely. Third, it is shown that the results are applicable to a large number of applications, including variable selection, open-loop identification, and closed-loop control of stochastic systems. Finally, numerical examples are given to support the theoretical analysis.

AdaDB: An adaptive gradient method with data-dependent bound

Adaptive gradient learning methods such as Adam, RMSProp, and AdaGrad play an essential role in training a very deep neural network. The learning rates of these optimizers are adaptively changed to accelerate the training process. And the convergence speed is much faster than SGD in many deep learning tasks such as classification and NLP tasks. However, recent works have pointed out that adaptive learning methods can not converge to a critical point under some situations and suffer poor generalization in many deep learning tasks. In our study, we propose AdaDB, an adaptive learning optimizer with data-dependent bound on the learning rate. Every element in the learning rate vector is constrained between a dynamic upper bound and a constant lower bound. And the upper bound is dependent on the data. We also give a theoretical proof of the convergence of AdaDB in the non-convex setting. Our experiments show that AdaDB is capable of eliminating the generalization gap between Adam and SGD. Experiments also reveal the convergence speed of AdaDB is much faster than Adam.

Local terrain modification method considering physical feature constraints for vector elements

ABSTRACT Many studies have been focused on rendering 2D vector elements on 3D terrain, and a series of algorithms have been proposed. Most of these algorithms struggle to provide a seamless overlay between vector elements and an irregular terrain surface. Despite their importance, the physical characteristics of vector elements are often ignored, which distorts the surface of vector elements. For example, if vector elements that represent roads and rivers are simply overlaid on terrain, the phenomena of uneven surfaces and rivers going uphill may occur because of elevation fluctuation. To correct these deficiencies, terrain should be modified according to the physical characteristics of vectors. We propose a local terrain modification method: First, the elevation of terrain covered by vector elements is recalculated according to vectors’ physical characteristics. Second, the multigrid method is used to realize a smooth transition between the modified terrain and its surrounding area. Finally, by setting different transition ranges and comparing the visualization effects, rules are given for the selection of a suitable range. After modification, the terrain conforms to vectors’ physical characteristics, and the overall relief is undamaged. The proposed method was applied to a CPU–GPU parallel heterogeneous model and demonstrated a high level of performance.

Boosting Answer Set Optimization with Weighted Comparator Networks

AbstractAnswer set programming (ASP) is a paradigm for modeling knowledge-intensive domains and solving challenging reasoning problems. In ASP solving, a typical strategy is to preprocess problem instances by rewriting complex rules into simpler ones. Normalization is a rewriting process that removes extended rule types altogether in favor of normal rules. Recently, such techniques led to optimization rewriting in ASP, where the goal is to boost answer set optimization by refactoring the optimization criteria of interest. In this paper, we present a novel, general, and effective technique for optimization rewriting based on comparator networks which are specific kinds of circuits for reordering the elements of vectors. The idea is to connect an ASP encoding of a comparator network to the literals being optimized and to redistribute the weights of these literals over the structure of the network. The encoding captures information about the weight of an answer set in auxiliary atoms in a structured way that is proven to yield exponential improvements during branch-and-bound optimization on an infinite family of example programs. The used comparator network can be tuned freely, for example, to find the best size for a given benchmark class. Experiments show accelerated optimization performance on several benchmark problems.

Knowledge-Based Semantic Relatedness measure using Semantic features

Measuring semantic relatedness has received much attention for uses in many fields such as information retrieval and natural language processing. For handling synonymous problem in distributional-based measures, many researchers are investigating how to exploit semantic features in lexical sources to form knowledge-based measures. In the knowledge-based measures, a hierarchy model is used to measure the relatedness between words based on only the taxonomical features extracted from a provided lexical source. In this paper, a new knowledge feature-based measure is proposed to build the semantic vector of a word construct on taxonomical and non-taxonomical feature of relation words. The proposed measure utilised the topological parameters that weight the importance of each element in the semantic vector. One of the gold dataset used to assess the proposed model and compare the findings with other related works. The results demonstrated the effectiveness of the proposed model on measuring semantic relatedness between words. In this paper, the research framework is identified based on the observations made on the previous related works that have been conducted for semantic representation and semantic relatedness measures. The required data in this research includes the semantic knowledge-based approach and the evaluation datasets. The semantic knowledge that will be used throughout of this research is extracted from English WordNet 3.1. On the other hand, the evaluation datasets covers the gold standard benchmarks which have been used for evaluating the semantic relatedness measurements and text mining tasks. Finally, the evaluation is preform to evaluate the proposed method (PM) based on approach in this research, in which obtained the result have been analyzed, to discuss and compare based on different performance measure and finding the strength and weakness in this paper, to alternative the semantic representation correlated to this research, to designing and develop the topical-based on the semantic representation method for text mining from Social media.

Genome-wide analysis reveals a regulatory role for G-quadruplexes during Adenovirus multiplication

The G-quadruplex (GQ) motifs have recently been gaining prominence because of their role as gene cis-regulatory elements in a variety of organisms and as potential druggable targets for anti-cancer therapy and ageing. Several studies have demonstrated the existence of GQs in the genomes of emerging and re-emerging human pathogens, such as hepatitis virus, herpesviruses, Ebola virus, Zika virus and Nipah virus. Human Adenovirus (HAdV) exhibits a large number of clinical manifestations especially infecting the children and the immunocompromised patients. Moreover, the HAdV-based vectors have been widely used to deliver foreign DNAs to cells in gene therapy. However, the DNA secondary structural elements in AdV-based vectors could significantly determine the gene delivery efficacy of the vectors. In this study, using a combination of whole genome sequence analysis, biochemical, biophysical and interaction assays, we revealed fifteen putative GQs that are conserved across the different species of HAdV. We further showed that the GQs are embedded in the sequences of essential viral genes, namely E1B, E2B, and L3 genes (among others), which are involved in the early and late stages of the viral life cycle. Notably, Braco-19 (a well-known GQ binding ligand) interacted specifically with the HAdV GQs and increased their stability and further blocked the HAdV multiplication in human cells. Taken together, our data strongly supported the existence of G-quadruplex structures in the HAdV genome that affect the virus multiplication and posit that such structures may influence the efficacy of the gene-delivery vectors or even the HAdV virus life-cycle.

Estetica aromatica

In this paper, we aim to show how flavors – specifically food flavors – and the atmospheres they help create have a strong sociopolitical value. Smells are here dealt with as vector elements inscribed in the collective space; they, on the one hand, affect the way in which refusal or acceptance occur and, on the other, mold perceptual and fruition model. By «aromatic aesthetics», we refer to the dimension in which smells are related to peculiar atmospheric policies. Stemming from this framework, we present the notions of sterilization, deodorization, and flavoring anew in the light of bodily, sensory and cognitive effects on the individual and on the relations with the surrounding world, particularly with food. The concoction of synthetic flavor mixtures and its subsequent perception cause atmospheric manipulations able to affect both collectively and individually the sense of smell. In contrast to this aggressive and standardizing practice, we try to outline an alternative approach based on the idea of engagement aiming to contribute to a complete ecological aesthetics.

Epstein-Barr virus-derived vector suitable for long-term expression in neurons

Exogenous gene expression is a fundamental and indispensable technique for testing gene function in neurons. Several ways to express exogenous genes in neurons are available, but each method has pros and cons. The lentivirus vector is useful for high efficiency gene transfer to neurons and stabilizes gene expression via genome integration, but this integration may destroy the host genome. The Epstein-Barr virus (EBV)-derived vector (EB vector) is an accessible and useful vector in human cell lines because the vector is not integrated into the host genome but stays in the nucleus as an episome. However, there has been no report on this process in rodent neurons.We examined the usefulness of the EB vector for testing gene function in neurons. We found that EB vector-derived exogenous proteins such as green fluorescent protein (GFP) and GFP-tagged actin were easily detectable even after three weeks of transfection. Second, a tetracycline-induced gene expression system in the EB vector was active after three weeks of transfection, indicating that plasmids were retained in neurons for up to three weeks. Third, we determined that only Family of repeat element of the plasmid vector is essential for its long-term presence in neurons. These results show that the modified EB vector is a useful tool for examining gene function in neurons.

Accurate diagnosis of term–preterm births by spectral analysis of electrohysterography signals

Preterm delivery contributes to an increased risk of fetal and maternal death as well as several health deficiencies, thereby requiring special care and treatment that result in high financial costs. It is therefore of key importance to diagnose preterm delivery in advance in order to avoid or minimize its undesirable consequences.This paper proposes a novel method for non-invasive diagnosis of preterm delivery based on the classification of electrohysterography (EHG) signals. First, the EHG signal, which is related to the electrical activity of uterine muscles is recorded from the maternal fundus using surface electrodes. Then, the signal is sliced into frames for spectral analysis. Next, spectral analyses of the individual EHG signal frames are carried out and centroid frequencies of the frames are computed, establishing the elements of a feature vector that represents the time-varying spectral content of the EHG signal. Finally, this feature vector is employed for the classification of the underlying EHG signal for term–preterm diagnosis.The efficiency of the proposed approach is evaluated and compared with representative methods from the literature. Our results demonstrate that the proposed approach exhibits superior performance over other methods.

Novel targets and strategies to combat borreliosis.

Lyme borreliosis is a bacterial infection that can be spread to humans by infected ticks and may severely affect many organs and tissues. Nearly four decades have elapsed since the discovery of the disease agent called Borrelia burgdorferi. Although there is a plethora of knowledge on the infectious agent and thousands of scientific publications, an effective way on how to combat and prevent Lyme borreliosis has not been found yet. There is no vaccine for humans available, and only one active vaccine program in clinical development is currently running. A spirited search for possible disease interventions is of high public interest as surveillance data indicates that the number of cases of Lyme borreliosis is steadily increasing in Europe and North America. This review provides a condensed digest of the history of vaccine development up to new promising vaccine candidates and strategies that are targeted against Lyme borreliosis, including elements of the tick vector, the reservoir hosts, and the Borrelia pathogen itself.

Efficiency Optimization of Capsule Network Model Based on Vector Element

Currently, Deep Learning and Convolutional Neural Network (CNN) have been widely used in many fields and have generated very high value in these fields, especially in the field of image recognition. But there are some deficiencies in certain issues of image recognition. For example, CNN’s recognizing performance is not good at different angles of objects and overlapping objects. Also, CNN is sometimes very sensitive to slight perturbations, modifying one pixel of a recognized image may cause recognition errors. For these problems, the capsule network (CapsNet) proposed by Geoffrey Hinton can solve the problems of traditional convolutional networks. Shortly after CapsNet was proposed, the model structure was relatively simple, and many aspects could be explored for improvement. This paper will optimize CapsNet from two aspects: “optimization of routing mechanism” and “increase Dropout operation.” And carry out experiments and results analysis on these optimizations.