Multidimensional Vector Space Research Articles

Analysis of addiction craving onset through natural language processing of the online forum Reddit.

Alcohol cravings are considered a major factor in relapse among individuals with alcohol use disorder (AUD). This study aims to investigate the frequency and triggers of cravings in the daily lives of people with alcohol-related issues. Large amounts of data are analyzed with Artificial Intelligence (AI) methods to identify possible groupings and patterns. For the analysis, posts from the online forum "stopdrinking" on the Reddit platform were used as the dataset from April 2017 to April 2022. The posts were filtered for craving content and processed using the word2vec method to map them into a multi-dimensional vector space. Statistical analyses were conducted to calculate the nature and frequency of craving contexts and triggers (location, time, social environment, and emotions) using word similarity scores. Additionally, the themes of the craving-related posts were semantically grouped using a Latent Dirichlet Allocation (LDA) topic model. The accuracy of the results was evaluated using two manually created test datasets. Approximately 16% of the forum posts discuss cravings. The number of craving-related posts decreases exponentially with the number of days since the author's last alcoholic drink. The topic model confirms that the majority of posts involve individual factors and triggers of cravings. The context analysis aligns with previous craving trigger findings related to the social environment, locations and emotions. Strong semantic craving similarities were found for the emotions boredom, stress and the location airport. The results for each method were successfully validated on test datasets. This exploratory approach is the first to analyze alcohol cravings in the daily lives of over 24,000 individuals, providing a foundation for further AI-based craving analyses. The analysis confirms commonly known craving triggers and even discovers new important craving contexts.

On usage of the neural network technologies in the it- structure components’ diagnosing.

The idea of using neural network technologes to prove electrophysical diagnostic methods based on the integral physical effects of IT structure components is considered. It is proposed to transform the received information using a discrete Karhunen-Loeve expansion, which gives the minimum root mean square error of packing a priory vectors in multidimensional space. The use of neural networks: MLP, self-organizing (Kohonen Maps) and RBF in MATLAB environment is verified. The best result for microcircuits was obtained using probabilistic RBF-neural networks. A new neural network approach to diagnostics made it possible to perform individual sorting of elements and ststistical evaluation of the IT structure components batch.

Exploring the Emotional Characteristics of Chinese Language Literature Based on the Thinking Operation Model

Abstract In this paper, each text is mapped as a point in a multidimensional vector space in a thought operation model to represent the emotion feature vocabulary in Chinese language literary works in the form of word embedding. The bootstrapping technique is utilized to collect initial seed set vocabulary and target vocabulary, and a corpus of literary works with six basic emotion categories is established. Based on the emotional features of Chinese language literary works, the memory network model for textual emotion analysis based on multiple attention is constructed, and the textual emotion features of Chinese language literary works are empirically analyzed. The results show that the precision is 82.19%, 93.12%, and 94.1%, and the F1Score is 81.93%, 93.4%, and 93.76%, respectively, and the precision and F1Score are better than the effect of the control group model, i.e., it indicates that the effect of sentiment analysis is more excellent. The ratio values of positive emotion, negative emotion, and neutral emotion are, respectively, 63.16%, 15.49%, and 21.35%, i.e., the expression of emotion in Chinese language literary works is more rational. This study provides new perspectives and theoretical references for the study of traditional literary works and has the potential to expand the study in depth.

Finding the presence of borrowings in scientific works based on Markov chains

The study aims to develop optimal approaches to the search for borrowings in scientific works. The article discusses the stages of searching for the presence of borrowings, such as preprocessing, rough filtering of texts, searching for similar texts, and searching for borrowings. The main focus is on the description of approaches and techniques that can be effectively implemented at each stage. For example, for the preprocessing stage, it may be converting text characters from uppercase to lowercase, removing punctuation marks, and removing stop words. For the stage of rough text filtering, it is filters by topic and word frequency. It may be calculating the importance of words in the context of the text and representing the word as a vector in multidimensional space to determine the proximity measure for the stage of finding similar texts. Finally, it is a search for an exact match, paraphrases and a measure of similarity of expressions for the stage of finding borrowings. The scientific novelty lies in using Markov chains to find the similarity of texts for the second and third stages of the search for borrowings proposed by authors. As a result, the example shows the technique of using Markov chains for text representation, searching for the most frequently occurring words, building a graph of a Markov chain of words, and the prospects for using Markov chains of texts for rough filtering and searching for similar texts.

Induction Motors Technical Condition In-Service Testing Methods

The article describes methods for in-service diagnostics of the technical condition of induction motors, which are the most widely used type of electromechanical systems. By now, a large number of various studies on this matter have been published. It is stated that the accomplished studies implement an "external" approach to solving the problem, which consists in deriving a diagnostic conclusion based on an analysis of changes in the electric motor external characteristics in one of its operation modes. In addition, these studies are based on two-dimensional models of the classical theory of electrical machines, which does not allow them to adequately reflect situations of multidimensional distribution of electromagnetic quantities in a vector space. These circumstances generated the need to develop more advanced diagnostic methods, called topological ones. Topological diagnostics methods are theoretically substantiated based on the ideas about the IM multidimensional vector space. It is shown that the parametric properties of the vector space and first of all of its working area, have a key influence on the electromechanical energy conversion efficiency. Two methods for testing different areas of the vector space are outlined. In the first case, the space working area is tested for parametric uniformity and operational stability. The results of three experiments provide unbiased and sufficient information about the degree of its homogeneity, about the changes that have occurred during the operation, and about their rates. The testing makes it possible to draw well-grounded conclusions about the electric motor active part technical condition and the degree of its criticality. In the second case, the vector space phase coordinates are tested for parametric uniformity and operational stability. The results presented testify that the proposed testing scheme is sensitive to phase parameter deviations. This is facilitated by the differential principle of its construction. The signal in a short-circuited phase, which appears in the case of a parametric phase imbalance, serves as its clear indicator. The presented mathematical modeling data confirm the efficiency of the proposed testing methods.

Deep preconditioners and their application to seismic wavefield processing

Seismic data processing heavily relies on the solution of physics-driven inverse problems. In the presence of unfavourable data acquisition conditions (e.g., regular or irregular coarse sampling of sources and/or receivers), the underlying inverse problem becomes very ill-posed and prior information is required to obtain a satisfactory solution. Sparsity-promoting inversion, coupled with fixed-basis sparsifying transforms, represent the go-to approach for many processing tasks due to its simplicity of implementation and proven successful application in a variety of acquisition scenarios. Nevertheless, such transforms rely on the assumption that seismic data can be represented as a linear combination of a finite number of basis functions. Such an assumption may not always be fulfilled, thus producing sub-optimal solutions. Leveraging the ability of deep neural networks to find compact representations of complex, multi-dimensional vector spaces, we propose to train an AutoEncoder network to learn a nonlinear mapping between the input seismic data and a representative latent manifold. The trained decoder is subsequently used as a nonlinear preconditioner for the solution of the physics-driven inverse problem at hand. Through synthetic and field data examples, the proposed nonlinear, learned transformations are shown to outperform fixed-basis transforms and converge faster to the sought solution for a variety of seismic processing tasks, ranging from deghosting to wavefield separation with both regularly and irregularly subsampled data.

Real-time prediction of the deformation of microcapsules using Proper Orthogonal Decomposition

Microcapsules are liquid droplets enclosed by a thin elastic membrane. Being suspended in an external fluid, they undergo large deformations when flowing. Their deformation can be solved numerically, but the resolution of the fluid–structure interactions (FSI) requires extremely long computation times. This is a major constraint for instance when identifying the membrane mechanical properties from experimentations of microcapsules flowing in a microfluidic channel of similar size (Hu et al. PFE 2013). We propose to apply Model Order Reduction (MOR) to predict in real time the steady-state capsule deformed shape, needed to determine the membrane elasticity. A database of the capsule deformed shapes was obtained numerically by solving the three-dimensional (3D) FSI through a finite element-boundary integral method coupling, and by varying systematically the two non-dimensionalized parameters of the problem: the capillary number, ratio of the viscous to the elastic forces, and the capsule-to-tube size ratio. Among the MOR techniques, we chose to apply Proper Orthogonal Decomposition (POD) onto the database, which provides a vector basis of principal components, defining a multi-dimensional vector space. The advantage is that, when all the capsule shapes of the database are mapped into this new vector space, they form a manifold (smooth hypersurface) that represents all the admissible solutions of the problem. We show that POD with a manifold walking technique can be successfully applied to 3D microcapsule data sets, whether they are Lagrangian (e.g. known position vector fields) or Eulerian (i.e. when data is acquired experimentally or numerically using methods like level sets). In both cases, the problem dimensionality is reduced, the predicted capsule shapes are obtained within computation times of milliseconds, and they accurately fit the full FSI simulations. This paves the way to real-time computations for capsules in flow, while retaining all the physical ingredients of the FSI problem.

Generating geometric body shapes with electromagnetic source scattering techniques

<p style='text-indent:20px;'>In this paper, we are concerned with the three-dimensional (3D) geometric body shape generation with several well-selected characteristic values. Since 3D human shapes can be viewed as the support of the electromagnetic sources, we formulate a scheme to regenerate 3D human shapes by inverse scattering theory. With the help of vector spherical harmonics expansion of the magnetic far field pattern, we build on a smart one-to-one correspondence between the geometric body space and the multi-dimensional vector space that consists of all coefficients of the spherical vector wave function expansion of the magnetic far field pattern. Therefore, these coefficients can serve as the shape generator. For a collection of geometric body shapes, we obtain the inputs (characteristic values of the body shapes) and the outputs (the coefficients of the spherical vector wave function expansion of the corresponding magnetic far field patterns). Then, for any unknown body shape with the given characteristic set, we use the multivariate Lagrange interpolation to get the shape generator of this new shape. Finally, we get the reconstruction of this unknown shape by using the multiple-frequency Fourier method. Numerical examples of both whole body shapes and human head shapes verify the effectiveness of the proposed method.</p>

Computational modeling and numerical analysis of dynamics of sustainable mining of mineral complexes

Understanding the sustainable mining dynamics prevents economic uncertainties, operational errors and severe environmental degradation during mining. The mine dynamics vary depending on multiple factors that can affect overall efficiency. A computational model would help in assessing mining operations. This paper proposes a set of novel computational normed linear vector space models to determine various aspects of mining operations and the estimation of mineral associations in an ore complex by employing matrix algebra. The proposed computational models consider that, distribution of minerals and mine operations can be formulated based on multidimensional vector space, which is computable in nature. Analysis of range of influence interaction matrix (RIIM) basing on a 3-mineral model is carried out to assess the mining dynamics. In addition to RIIM, the cause–effect (C–E) interaction model is employed to assess the interaction intensity and dominance of minerals in an ore deposit in terms of availability as well as extraction. The C–E interrelationships between minerals are evaluated basing on the generic coding method. The transformations of structural domains resulting in multidimensional space are further assessed to envision C–E variations between Cu–Ni–Pb as a mineral complex. The projection analysis based on nonlinear regression and numerical evaluations of computational models provide an insight of the life time of a mine. The proposed computational approaches can be applied in combination on n-mineral models to aid in decision making during all stages of sustainable mining.

Semiotic analysis of narrative legal texts using asymmetric document divergency

In the paper the case is studied then semiotic signs can be represented as language constructs in the same language as the text for the interpretation. The goal is to obtain estimates of the depth of interpretability with the respect to each of the signs by finding the projections of the narrative on these language constructs. That is, we find the probability of the presence of each of the signs. By use the embedding of linguistic constructions into a multidimensional numerical vector space and the grid layout, which is the image of the system of signs. At the stage of the inference, our model computes the nodes of the grid, which are the most adequate to the input text by the method of new proposed asymmetric similarity. The constructed model was studied using Russian criminal, civil and labour legislation. We suggest using the models called the constellation as for the grid of normative acts and the narrative together with the corresponding divergence. In this paper, exanimated the pre-trained models known in NLP as doc2vec and Fast Text. The evaluation of the quality of models was carried out using open databases of court decisions.

Chemical Kinetics Roots and Methods to Obtain the Probability Distribution Function Evolution of Reactants and Products in Chemical Networks Governed by a Master Equation.

In this paper first, we review the physical root bases of chemical reaction networks as a Markov process in multidimensional vector space. Then we study the chemical reactions from a microscopic point of view, to obtain the expression for the propensities for the different reactions that can happen in the network. These chemical propensities, at a given time, depend on the system state at that time, and do not depend on the state at an earlier time indicating that we are dealing with Markov processes. Then the Chemical Master Equation (CME) is deduced for an arbitrary chemical network from a probability balance and it is expressed in terms of the reaction propensities. This CME governs the dynamics of the chemical system. Due to the difficulty to solve this equation two methods are studied, the first one is the probability generating function method or z-transform, which permits to obtain the evolution of the factorial moment of the system with time in an easiest way or after some manipulation the evolution of the polynomial moments. The second method studied is the expansion of the CME in terms of an order parameter (system volume). In this case we study first the expansion of the CME using the propensities obtained previously and splitting the molecular concentration into a deterministic part and a random part. An expression in terms of multinomial coefficients is obtained for the evolution of the probability of the random part. Then we study how to reconstruct the probability distribution from the moments using the maximum entropy principle. Finally, the previous methods are applied to simple chemical networks and the consistency of these methods is studied.

Identification of Distribution Laws Using the Correlation Coefficient Using Python

The article is devoted to the development of a new method for identifying the distribution laws when evaluating the results of multiple measurements. The identification of the distribution laws is today an urgent metrological task, since the adopted restrictions on the number of measurements and assumptions about the distribution law of random error may introduce additional uncertainty in the assessment of the measurement result.
 The use of well-known classical approaches to the identification of distribution laws is associated with a number of difficulties associated with the need to use the completeness of the considered set of models and the correct application of the corresponding statistical methods. The main limitation associated with the use of classical approaches to the identification of distribution laws is that they are designed for use in data processing systems based on Gaussian distribution (normal) and, thus, are not universal. The imperfection of mathematical models of processing measurement information leads to the possible erroneous identification of the distribution law.
 The paper proposes a method for identifying the distribution laws for data outside the Gaussian distribution region. The model is based on the calculation of correlation coefficients for data with different distribution laws. The correlation coefficient is used to estimate the proximity of probability density functions and is calculated for pairs of different probability densities represented by histograms in a multidimensional vector space on an orthonormal basis of unit sampling intervals. Based on the obtained matrix of the values ​​of the correlation coefficients, a classification estimate of the unknown distribution laws is performed based on the experimental data of the simulated samples. A listing of the software implementation of the model in the Python software environment is given.

An Approach to the Programs Security Analysis using Vector Representation of Machine Code

In this article, the authors propose an approach to the security analysis of program code using vector representations of machine instructions. The article also proposes a method for constructing multidimensional vector spaces for a set of program code instructions. The construction of semantically expressive vector representations of machine instructions is considered as one of the important tasks in constructing a neural network code classifier for vulnerabilities. The applicability of the principle of transfer learning to machine code is demonstrated experimentally.

Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.

A Collaborative Reputation-Based Vector Space Model for Email Spam Filtering

In this paper, we propose a novel Collaborative Reputation-based Vector Space Model (CRVSM) for detection of spam email. CRVSM uses a vector space model for representing the feature vectors in multidimensional vector space in order to detect the spam emails in large space. We cluster the emails into five clusters so as to reduce the email spam detection time. To reduce the number of false positives and false negatives, we calculate maximum similarity measure with maximum and minimum threshold range. Moreover we use a reputation evaluation function which determines the reporter's trust level in validating an email as spam or non-spam. The CRVSM approach achieves good efficiency while obtaining good reputation result in Email spam detection. The performance of CRVSM model has been evaluated using metrics such as false positive rate, false negative rate, detection accuracy and detection time. The performance results clearly show that CRVSM accurately detects the incoming emails as spam or non-spam with less FPR and FNR values thereby achieving a high efficiency with short detection time and outperforms the existing detection protocols.

Diagnostics of the Phase Composition of Lead-Zirconate-Titanate Films by Raman Spectroscopy

The problem of diagnostics of the phase composition of lead-zirconate-titanate (PZT) layers under conditions of overlapped Raman spectra is solved by means of applied mathematical statistics. The spectra measured on the surface of the multiphase material are treated as points in multidimensional vector spaces. Particular attention is paid to narrowing of the spectral range where the analysis is carried out. For this purpose, regions for the first principal components are used, where the greatest relative changes are observed. The concentration of the perovskite and pyrochlore phases in the growing PZT films is determined.

P088 Multidimensional identification of anti-HLA antibodies

Aim Although single-antigen-bead Luminex technology has revolutionised the detection of anti-HLA antibodies, it is still difficult to characterise with precision the specificity of these antibodies. In order to improve this characterisation we have tried to the expose the various properties of different antibodies by using different methods with the hope that they could be told apart from each other. Methods Sera with known presence of anti-HLA antibodies were analysed using two different methods: (A) the standard One Lambda single-antigen bead protocol, and (B) a modification of this protocol by Dr. Robert Liwski (Dalhousie University, Halifax, Nova Scotia). Each bead was assigned two values, one by each method, creating a 2-dimensional vector space in which each bead is represented by a vector. The results of each sample were represented in a vector space. It would be possible to use more than two methods to create a higher order multidimensional vector space. Results The 2-dimensional vector space for each sample revealed distinct clusters of reactivity, which could not be separated by any single method by itself. These clusters of reactivity closely corresponded to known epitopes. For example, the DRB1 specificities defined by {181}:(M), {13}:(R) and {57–58}:(AA) in one sample were clearly distinct as separate clusters, whereas in any single method they were all clustered together. Actually the corresponding DRB1 alleles with the {181}:(M) isoform (DRB1 ∗ 07, ∗ 09, ∗ 10) formed a separate cluster from the DRB5 alleles, suggesting, contrary to current belief, that there are actually two distinct antibodies at work here. Conclusions – Current methods of antibody detection and identification are not precise in regard to measurements of levels of reactivity. – We should not be looking at reactivity levels, but at specificity patterns and reactivity clusters. – These reactivity clusters are better revealed by using different analytical methods simultaneously.

한국 청소년의 대표 인체치수 및 3D 인체형상자료에 관한 연구

3D body scan data are used widely in various fields to make products and living spaces for superior human body fitness. Based on the 3D measurements of human bodies for teens in Size Korea 2013, this research provides a way of finding the representative body sizes and 3D body scan data. First, a multi-dimensional vector space consisting of many measurement items was projected onto a 2D vector space with circumference and length components via factor analysis. The representative body sizes and 3D scan data close to these values were obtained via the Mahalanobis distance in 2D space. Considering the adolescent growth pattern shown on this 2D space, males were divided into 4 age groups and females were divided into 3 age groups. Using the eigenbodies corresponding to the column vectors of the component score coefficient matrix, the representative body sizes of 13 measurement items (male) and 14 measurement items (female) for each age group were calculated. The representative body sizes and 3D scan data are very useful for modeling representative 3D human figures.

A Self-Tester for Linear Functions over the Integers with an Elementary Proof of Correctness

We present simple, self-contained proofs of correctness for algorithms for linearity testing and program checking of linear functions on finite subsets of integers represented as n-bit numbers. In addition we explore a generalization of self-testing to homomorphisms on a multidimensional vector space. We show that our self-testing algorithm for the univariate case can be directly generalized to vector space domains. The number of queries made by our algorithms is independent of domain size.

Acronym Disambiguation Using Word Embedding

According to the website AcronymFinder.com which is one of the world's largest and most comprehensive dictionaries of acronyms, an average of 37 new human-edited acronym definitions are added every day. There are 379,918 acronyms with 4,766,899 definitions on that site up to now, and each acronym has 12.5 definitions on average. It is a very important research topic to identify what exactly an acronym means in a given context for document comprehension as well as for document retrieval. In this paper, we propose two word embedding based models for acronym disambiguation. Word embedding is to represent words in a continuous and multidimensional vector space, so that it is easy to calculate the semantic similarity between words by calculating the vector distance. We evaluate the models on MSH Dataset and ScienceWISE Dataset, and both models outperform the state-of-art methods on accuracy. The experimental results show that word embedding helps to improve acronym disambiguation.