Multidimensional Arrays Research Articles

Conversion practices in Aotearoa New Zealand: Developing a holistic response to spiritual abuse.

Research arising from conversion practices, also known as conversion therapy and sexual orientation and gender identity change efforts, has generally been underpinned by an emancipatory discourse that has evolved to counter harmful practices by evidencing associated harms and estimating prevalence. Little attention, however, has focused on what is required to support survivors, inclusive of those currently or those having previously experienced conversion efforts. Within a context of Aotearoa New Zealand having recently criminalised conversion practices, this study adopted an in-depth qualitative research design, informed by a dual adherence to life history and an empowerment methodology. Twenty-three religious conversion practice survivors, who had experienced religious conversion practices across a range of Christian identified faith settings, were interviewed. Participants had a median age of 34 and the majority identified as New Zealand European, cisgender, and gay. Participant narratives were discursively analysed. Three primary discourses were identified that inform the needed development of interventions and supports: 1) pervasive framing of conversion practices as harm, rather than spiritual abuse, has minimised the impacts of conversion practices. Rather, conceptualising conversion as spiritual abuse positions conversion practices as requiring urgent intervention and ongoing support, inclusive of the development of policy and operational responses; 2) the coercive nature of spiritual abuse needs to be appreciated in terms of spiritual, social, and structural entrapment; 3) the metaphor of a pipeline was enlisted to encapsulate the need for a multidimensional array of interventions to ensure those entrapped within spiritual abuse have a "pipeline to safety". Holistic survivor-centric conversion-related responses to spiritual abuse are required. These need to be informed by an understanding of entrapment and the associated need for holistic responses, inclusive of extraction pathways and support for those entrenched within abusive religious settings, support immediately after leaving abusive environments, and support throughout the survivors' healing journeys.

Introduction to the Concept and Practice of Using Arrays in Programming Languages ​​at Senior High School 2 Gunung Talang, Solok Regency

Introduction to the Concept and Practice of Using Arrays in Programming Languages ​​is an effort to provide education and guidance to teachers at Senior High School 2 Gunung Talang, Solok who are interested in computer programming. Knowledge of the use of arrays in programming languages ​​is an important basis in the increasingly developing world of information technology. This community service activity aims to provide a broader understanding of the concepts and practices of using arrays in programming languages, especially for those who do not have a technical background in programming. In this activity, participants will be invited to understand the basic concepts of arrays, how to declare, initialize and access array elements in programming languages. This activity will provide an introduction to various types of arrays, such as one-dimensional arrays, two-dimensional arrays, and multidimensional arrays, as well as provide examples of practical use in everyday programming. Participants will also be taught about the benefits of using arrays, including how they can simplify code, increase efficiency, and optimize data management. It is hoped that the results of this service will provide benefits to the community by increasing their understanding of the use of arrays in programming languages. With this knowledge, participants become better prepared to overcome programming challenges and apply them in various fields, such as software development, data analysis, and information technology in general

Abstract 4933: A workflow for cloud-based AI development of multiplex IF image analysis using the OMERO Plus platform

Abstract Background: The advancement of AI in digital pathology is essential for analyzing image data containing billions of cells. This is true for digital pathology workflows using bright-field images, but this is even more critical for multiplex immunofluorescence (mIF) whole-slides due to the increased number of fluorescent channels and bit-depth per pixel. These complexities necessitate improved data management, and efficient visualization for both the mIF image and AI-based analysis results. We present a workflow using OMERO Plus, AWS SageMaker, and Ultivue software for AI model development in mIF. Method: Images were uploaded to AWS S3 and imported to the data management system OMERO Plus, where they were organized into training and validation groups. Annotations: PathViewer was used to place rectangular fields of view (FOV) in locations selected by the pathologist where all positive cells for each target marker were manually annotated. Data Preparation: Using the OMERO Plus API, annotation coordinates, image tiles, and image metadata were extracted and formatted for model training. All training data is under a version control system using Gitlab and Data Version Control tools using AWS S3. Additionally, strict quality checks were performed to ensure data integrity. Training: For both semantic segmentation and cell object detection, the training data was used in a customized training workflow using AWS SageMaker, including modern deep learning architectures, data augmentation, training logging, evaluation, and deployment. All AI models were trained using Pytorch and deployed with the open model format ONNX. Results Visualization: AI segmentation results of cells and regions are visualized using PathViewer as overlays on the original images by converting them to the OME-NGFF label image format that uses Zarr multidimensional arrays. Results: We utilized 58 and 24 mIF images for training and validation, respectively, on 15 markers using ISP Ultivue technology with 1681 and 296 FOVs, encompassing over 60K cells. Using this data and methodology we trained 3 semantic segmentation models and over five object detection models suitable for all twenty-four OmniVUE markers. For instance, the CD3 positive cell detection model, trained with 390 FOVs, achieved highly accurate results: f1-score of 0.915 evaluated on 34 FOVs. Conclusions: The combination of OMERO Plus, PathViewer, AWS SageMaker, and internal tools can successfully address mIF Big Data data management complexities in a cloud environment for digital pathology. The architecture is highly scalable, cost effective, and reliable, enabling a very efficient AI workflow. Citation Format: Ruben Cardenes, Erin Diel, Douglas Wood, Je Lee, Angela Vasaturo, Martin Schulze, Lorenz Rognoni. A workflow for cloud-based AI development of multiplex IF image analysis using the OMERO Plus platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4933.

EPDFpy: A Python-based interactive GUI tool for electron pair distribution function analysis of amorphous materials

ePDFpy is an interactive analysis program with a graphical user interface (GUI), designed to process the electron Pair Distribution Function (PDF) analysis of diffraction patterns from Transmission Electron Microscope (TEM), to identify the local atomic structure of amorphous materials. The program offers a user-friendly Python-based interface, providing a straightforward and adaptable workflow for PDF analysis. Various optimization and fitting processes were implemented to accurately reduce the electron diffraction data, including center-fitting and elliptical correction of diffraction data. An improved parameter-estimation feature is available to enhance the efficiency of the fitting process, along with an interactive GUI. ePDFpy will be freely distributed for academic purposes, with additional features, including a beam mask drawing module. Program summaryProgram Title: ePDFpyCPC Library link to program files:https://doi.org/10.17632/sym3sfnh7w.1Developer's repository link:https://github.com/GWlab-SKKU/ePDFpyLicensing provisions: GNU GPLv3Programming language: PythonNature of problem: The general process of pair distribution function analysis consists of two major steps: image process on diffraction pattern and fitting appropriate parameters. Both of the procedures are affected by the user's proficiency, which can be responsible for producing inconsistent results and inefficiency. Thus, accurate calculation methods along with fully automated feature are required to enhance the quality of the analysis result.Solution method: ePDFpy offers an unbiased automated image process based on a computer vision algorithm to produce the consistent output of intensity profiles from diffraction patterns. In addition, converting the data structures into a multi-dimensional array enables efficient multi-parameter fitting features by performing parallel computation. All of these features are accomplished using various open-source libraries in the Python community, along with an interactive GUI.

Analysis and control strategy design for PEMFC purging process

In this study, a lumped-parameter model for shutdown purging has been developed using MATLAB/Simulink®. Data gleaned from actual operational scenarios on a fuel cell experimental platform serve as the basis for parameter identification within the model. The study encompasses a comparative analysis of purging processes under various operational parameters, including stack temperatures, purging currents, and purging flow rates. Moreover, an innovative purging strategy is proposed, employing an open-loop control mechanism to modulate the purging flow rate. The analysis reveals that an elevated stack temperature and a reduced purging current markedly enhance purging efficiency and diminish the water content at equilibrium. A negative correlation is observed between purging time and purging flow rate, with energy consumption demonstrating a U-shaped pattern across different flow rates. According to the proposed purging strategy, the optimal purging flow rate is ascertained through a Multi-dimensional Array Programming (MAP) lookup. This lookup is contingent upon stack temperature, purging current, and initial membrane water content. Validation confirms that this strategy not only truncates the purging duration but also curtails the system energy expenditure. These analytical insights and the newly-formulated purging strategy hold both theoretical and pragmatic implications for optimizing stack shutdown purging operations and advancing future purging methodologies.

Information about the book by Joseph Dubrovkin “Data Compression IN Spectroscopy”, Cambridge Scholars Publishing. 2022, 355 PP. ISBN (10): 1-5275-8620-0 ISBN (13)

Widespread use of instrumental methods of analysis, in particular analytical spectroscopy, in recent years has led to increasing volume of information about the objects being studied. Experimental results are stored on a computer in the form of multidimensional digital data arrays that need to be processed in a special way, for example, to remove noise, visualize, analyze, and compress for efficient storage. For these purposes, there are theoretical methods and algorithms of data compression. At the same time compression (decompression) algorithms must ensure minimum distortion of the original signals for processing hyperspectral data. Large volume of data implies a comprehensive analysis of information. When working with multidimensional arrays, it is important to use special methods and technologies for processing, analyzing and interpreting hyperspectral data. This article provides information about the monograph “Data Compression in Spectroscopy” by Joseph Dubrovkin. The book was published by Cambridge Scholars Publishing. The book was written by an expert with extensive experience in the field of multivariate data analysis and chemometrics. The book consists of a preface, information about the structure of the book, a list of abbreviations and symbols, an introduction for each of 4 chapters, 8 appendices, a list of references and a subject index. A large number of examples and exercises are illustrated with MATLAB programs, and bibliographic tables clearly demonstrate the use of compression methods in industrial and research laboratories. The material in the book is discussed chapter by chapter. This modern monograph on data compression in spectroscopy will be useful as a teaching aid for students and teaching staff, as well as for specialists in analytical laboratories.

Kiwaku, a C++20 library for multidimensional arrays. Application to ACTS tracking

C++ is a vital part of particle physics as it allows for high level abstractions while offering state of the art performance. In this article we will first introduce the C++ 20 concepts, a major C++ usability enhancement. We will then introduce Kiwaku, a new multidimensional arrays library taking advantage of the most advanced C++ features at the time of writing, providing the user with intuitive API while retaining state of the art performance. Multidimensional arrays are a basic building block for many scientific experiments and simulations, and particle physics is no exception. Using examples borrowed from Covfie and ACTS libraries, and using data from the ATLAS CERN experiment, we will show how Kiwaku offers good usability while having a negligible impact on performance compared to using classic C++ std::arrays.

Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization.

Gene expression data are used to discover meaningful hidden information in gene datasets. Cancer and other disorders may be diagnosed based on differences in gene expression profiles, and this information can be gleaned by gene sequencing. Thanks to the tremendous power of artificial intelligence (AI), healthcare has become a significant user of deep learning (DL) for predicting cancer diseases and categorizing gene expression. Gene expression Microarrays have been proved effective in predicting cancer diseases and categorizing gene expression. Gene expression datasets contain only limited samples, but the features of cancer are diverse and complex. To overcome their dimensionality, gene expression datasets must be enhanced. By learning and analyzing features of input data, it is possible to extract features, as multidimensional arrays, from the data. Synthetic samples are needed to strengthen the range of information. DL strategies may be used when gene expression data are used to diagnose and classify cancer diseases.

СВЕРТОЧНАЯ НЕЙРОСЕТЕВАЯ МОДЕЛЬ АКУСТИЧЕСКОГО ОБНАРУЖЕНИЯ АВАРИЙНО-СПАСАТЕЛЬНЫХ МАШИН

This article solves the problem of developing a neural network model for the acoustic detection of rescue vehicles. To solve this problem, it is proposed to use the mathematical apparatus of convolutional neural networks. Emergency vehicles such as ambulances, fire engines and police vehicles play an important role in life-threatening situations. Timely response is a key factor in the work of emergency services, which confirms the relevance of developing a neural network to solve the problem. The development of a convolutional neural network model required solving the following problems: searching and preparing initial data for analysis, selecting the necessary libraries for software implementation of the neural network, building an architecture, training, testing and assessing the adequacy of the neural network model. The “Emergency Vehicle Siren” data set from the public Kaggle Datasets database was used as the initial data for building the model. The data is presented in the form of a set of audio recordings of the sound of sirens of ambulances, fire trucks and city traffic noise, with 200 examples of each class. The data has been prepared for analysis. Training, validation and test samples were formed. The volume of the training sample was 384 audio recordings, the validation sample was 96, and the test sample was 120. The following libraries were selected for training the convolutional neural network: Librosa (for analyzing and processing audio data), NumPy (for working with multidimensional data arrays), Matplotlib (for creating graphs, charts, histograms and other forms of data visualization), Seaborn (for creating statistical graphs built on the basis of the Matplotlib library), TensorFlow (for machine learning) and Keras (for interacting with artificial neural networks). The architecture of a neural network was built, consisting of three convolution layers, three subsampling layers and a fully connected part responsible for classification and consisting of two layers. A neural network model was trained in the Kaggle notebook environment. Training was carried out for 50 epochs. The accuracy of the neural network model on the training data set was 99.22%, on the validation set - 93.75%. To assess the adequacy of the constructed neural network model, a test data sample was used. The classification accuracy for data from this sample was 99.17%. The results obtained indicate the effectiveness of the constructed convolutional neural network model and the possibility of its practical use for automating the solution of the problem of acoustic detection of rescue vehicles.

A tensor based varying-coefficient model for multi-modal neuroimaging data analysis.

All neuroimaging modalities have their own strengths and limitations. A current trend is toward interdisciplinary approaches that use multiple imaging methods to overcome limitations of each method in isolation. At the same time neuroimaging data is increasingly being combined with other non-imaging modalities, such as behavioral and genetic data. The data structure of many of these modalities can be expressed as time-varying multidimensional arrays (tensors), collected at different time-points on multiple subjects. Here, we consider a new approach for the study of neural correlates in the presence of tensor-valued brain images and tensor-valued covariates, where both data types are collected over the same set of time points. We propose a time-varying tensor regression model with an inherent structural composition of responses and covariates. Regression coefficients are expressed using the B-spline technique, and the basis function coefficients are estimated using CP-decomposition by minimizing a penalized loss function. We develop a varying-coefficient model for the tensor-valued regression model, where both covariates and responses are modeled as tensors. This development is a non-trivial extension of function-on-function concurrent linear models for complex and large structural data, where the inherent structures are preserved. In addition to the methodological and theoretical development, the efficacy of the proposed method based on both simulated and real data analysis (e.g., the combination of eye-tracking data and functional magnetic resonance imaging (fMRI) data) is also discussed.

Fast Integration of Poisson Distributions for Dead Sensor Marginalization

In dual-phase time-projection chambers there are photosensor arrays arranged to allow for inference of the positions of interactions within the detector. If there is a gap in data left by a broken or saturated photosensors, the inference of the position is less precise and less accurate. As we are unable to repair or replace photosensors once the experiment has begun, we develop methods to estimate the missing signals. Our group is developing a probabilistic graphical model of the correlations between the number of photons detected by adjacent photosensors that represents the probability distribution over photons detected as a Poisson distribution. Determining the posterior probability distribution over a number of photons detected by a sensor then requires integration over a multivariate Poisson distribution, which is computationally intractable for high-dimensions. In this work, we present an approach to quickly calculate and integrate over a multidimensional Poisson distribution. Our approach uses Zarr, a Python array compression package, to manage large multi-dimensional arrays and approximates the log factorial to quickly calculate the Poisson distribution without overflow.

Bayesian tensor-on-tensor regression with efficient computation.

We propose a Bayesian tensor-on-tensor regression approach to predict a multidimensional array (tensor) of arbitrary dimensions from another tensor of arbitrary dimensions, building upon the Tucker decomposition of the regression coefficient tensor. Traditional tensor regression methods making use of the Tucker decomposition either assume the dimension of the core tensor to be known or estimate it via cross-validation or some model selection criteria. However, no existing method can simultaneously estimate the model dimension (the dimension of the core tensor) and other model parameters. To fill this gap, we develop an efficient Markov Chain Monte Carlo (MCMC) algorithm to estimate both the model dimension and parameters for posterior inference. Besides the MCMC sampler, we also develop an ultra-fast optimization-based computing algorithm wherein the maximum a posteriori estimators for parameters are computed, and the model dimension is optimized via a simulated annealing algorithm. The proposed Bayesian framework provides a natural way for uncertainty quantification. Through extensive simulation studies, we evaluate the proposed Bayesian tensor-on-tensor regression model and show its superior performance compared to alternative methods. We also demonstrate its practical effectiveness by applying it to two real-world datasets, including facial imaging data and 3D motion data.

AN ORDER-P TENSOR MULTIPLICATION WITH CIRCULANT STRUCTURE

Research on mathematical operations involving multidimensional arrays or tensors has increased along with the growing applications involving multidimensional data analysis. The -product of order- tensor is one of tensor multiplications. The -product is defined using two operations that transform the multiplication of two tensors into the multiplication of two block matrices, then the result is a block matrix which is further transformed back into a tensor. The composition of both operations used in the definition of -product can transform a tensor into a block circulant matrix. This research discusses the -product of tensors based on their circulant structure. First, we present a theorem of the -product of tensors involving circulant matrices. Second, we use the definition of identity, transpose, and inverse tensors under -product operation and investigate their relationship with circulant matrices. Third, we manifest the computation of the -product involving circulant matrices. The results of the discussion show that the -product of tensors fundamentally involves circulant matrix multiplication, which means that the operation at its core relies on multiplying circulant matrices. This implies the -product operation of tensors having properties analogous to standard matrix multiplication. Furthermore, since the -product of tensors fundamentally involves circulant matrix multiplication, its computation can be simplified by diagonalizing the circulant matrix first using the discrete Fourier transform matrix. Finally, based on the obtained results, an algorithm is constructed in MATLAB to calculate the -product.

Implementation of an Intelligent EMG Signal Classifier Using Open-Source Hardware

This paper presents the implementation of an intelligent real-time single-channel electromyography (EMG) signal classifier based on open-source hardware. The article shows the experimental design, analysis, and implementation of a solution to identify four muscle movements from the forearm (extension, pronation, supination, and flexion), for future applications in transradial active prostheses. An EMG signal acquisition instrument was developed, with a 20–450 Hz bandwidth and 2 kHz sampling rate. The signals were stored in a Database, as a multidimensional array, using a desktop application. Numerical and graphic analysis approaches for discriminative capacity were proposed for feature analysis and four feature sets were used to feed the classifier. Artificial Neural Networks (ANN) were implemented for time-domain EMG pattern recognition (PR). The system obtained a classification accuracy of 98.44% and response times per signal of 8.522 ms. Results suggest these methods allow us to understand, intuitively, the behavior of user information.

Measurement Method for the Flight Parameters of a Small Flying Object Using a Multi-Dimensional LED Detection Array

This article proposes a measurement method using a multi-dimensional LED detection array, which can be used to obtain the flight parameters (i.e., impact coordinates, flight velocity, and incident angle) of a small flying object. Firstly, the composition of the proposed system and its detection principle are described in detail. Then, a calculation model is derived according to the geometrical relationship between the different LED detection arrays, which can calculate the above flight parameters. Furthermore, numerical simulations are performed to analyze the change trend in the measurement error of the proposed system, and it can be verified that its measurement performance meets the related requirements in theory. Finally, we use the proposed system and a high-speed camera system to carry out comparison experiments with two different reference velocities of 900 m/s and 700 m/s, and the results show that the maximum deviation in the measured absolute velocity is always less than 1 m/s and that the maximum deviations of the measured coordinates of x and y are not more than 2 mm within the effective measurement range. Therefore, the proposed measurement method is feasible and effective, and it can also meet the requirements of the measurement system.

Current Situation and Mode Innovation of Physical Education Teaching in Colleges and Universities under the Perspective of Deep Learning

Abstract This paper analyzes the current situation of physical education teaching in colleges and universities from the perspective of deep learning and innovates the teaching methodology. A multidimensional data array is used to quantify students’ interests and hobbies to create a recommendation module for intelligent sports learning materials. Using the SIFT feature extraction algorithm, identify the extreme value point of the detection target and develop the sports target extraction module. Through distance transformation and morphological features, determine the skeleton image features to construct a college sports teaching model using deep learning. The results show that the model in this paper enables the coverage rate of sports courses to reach over 97.20%, with a fluctuation of no more than 1.5%. Deep learning has contributed to some innovation in college sports teaching.

The Zero Cubes Free and Cubes Unique Multidimensional Constraints

This paper studies two families of constraints for two-dimensional and multidimensional arrays. The first family requires that a multidimensional array will not contain a cube of zeros of some fixed size and the second constraint imposes that there will not be two identical cubes of a given size in the array. These constraints are natural extensions of their one-dimensional counterpart that have been rigorously studied recently. For both of these constraints we present conditions on the size of the cube for which the asymptotic rate of the set of valid arrays approaches 1 as well as conditions for the redundancy to be at most a single symbol. For the first family we present an efficient encoding algorithm that uses a single redundant symbol to encode arbitrary information into a valid array and for the second family we present a similar encoder for the two-dimensional case. The results in the paper are also extended to similar constraints where the sub-array is not necessarily a cube, but a box of arbitrary dimensions and only its volume is bounded.

Comparative Evaluation of the Accuracy of Numerical Methods on Reference Solutions

When organizing a mass practical solution of computational problems of gas dynamics with the help of mathematical modeling, information on the comparative accuracy of the numerical methods used is now increasingly in demand. As a rule, calculators need information not only for a particular combination of the defining gas-dynamic parameters of the problem (characteristic Mach, Reynolds numbers, etc.), but also for the variation of these parameters in certain ranges. This work presents numerical studies devoted to a comparative assessment of the accuracy of numerical methods for a number of problems with reference solutions. The calculation of these problems is carried out for ranges of characteristic numbers using various numerical methods. The results obtained are compared with the reference solution and make it possible to estimate the error for each of the numerical methods. Calculations are carried out using the construction of a generalized computational experiment. A generalized computational experiment is a computational technology that combines the solution of mathematical modeling problems, parallel technologies and visual analytics technologies. The results of the generalized computational experiment are multidimensional arrays, where the dimension of the arrays corresponds to the defining parameters. Analysis and visual representation of the obtained results provide information on the comparative accuracy of the numerical method for the selected class of problems.

Mathematical Analyzing of Laser Triangulation System without Scheimpflug Condition via C++ and Qt Framework

In this paper, the mathematical characteristics of a laser triangulation system has been analyzed without considering Scheimpflug condition. The system parameters have been investigated and detailed information about the behavior of the system due to the changing some initial parameters just before designing the profile measurement system have been obtained. To be able to evaluate the system, C++ and Qt framework have been used for calculation of multi-dimensional arrays and visualization of the results to investigate the system parameters. The characteristics of the triangulation systems (both for our model and the model that proposed by Lamott and Noll, 2011) and characteristics of laser beam (laser beam thickness change for given measurement field range) for different laser sources with different wavelengths have been investigated. After analyzing the parameters of the triangulation system successfully, an adjustable system will be built in the laboratory to be able to get a better understanding the usage of this system in both 2D and 3D laser cutting systems.

Multidimensional Costas Arrays and Their Periodicity

A novel higher-dimensional definition for Costas arrays is introduced. This definition works for arbitrary dimensions and avoids some limitations of previous definitions. Some non-existence results are presented for multidimensional Costas arrays preserving the Costas condition when the array is extended periodically throughout the whole space. In particular, it is shown that three-dimensional arrays with this property must have the least possible order; extending an analogous two-dimensional result by H. Taylor. Said result is conjectured to extend for Costas arrays of arbitrary dimensions.