Row Column Research Articles

A Survey of Co-Clustering

Co-clustering is to cluster samples and features simultaneously, which can also reveal the relationship between row clusters and column clusters. Therefore, lots of scientists have drawn much attention to conduct extensive research on it, and co-clustering is widely used in recommendation systems, gene analysis, medical data analysis, natural language processing, image analysis, and social network analysis. In this article, we survey the entire research aspect of co-clustering, especially the latest advances in co-clustering, and discover the current research challenges and future directions. First, due to different views from researchers on the definition of co-clustering, this article summarizes the definition of co-clustering and its extended definitions, as well as related issues, based on the perspectives of various scientists. Second, existing co-clustering techniques are approximately categorized into four classes: information-theory-based, graph-theory-based, matrix-factorization-based, and other theories-based. Third, co-clustering is applied in various aspects such as recommendation systems, medical data analysis, natural language processing, image analysis, and social network analysis. Furthermore, 10 popular co-clustering algorithms are empirically studied on 10 benchmark datasets with 4 metrics—accuracy, purity, block discriminant index, and running time, and their results are objectively reported. Finally, future work is provided to get insights into the research challenges of co-clustering.

Row Column Reduction Method for Solving Trapezoidal Fuzzy Transportation Problem

The primary goal of the transportation issue is to reduce the cost of moving goods from several warehouses to various showrooms. Fuzzy approaches are frequently employed to eliminate uncertainty. When the cost of transportation is uncertain, we sometimes use a fuzzy approach to resolve the issue. In this paper the values used in constraint equation are trapezoidal number and applied our proposed method, row column reduction method to get an optimal transportation cost.

Investigation on the wave focusing effects and energy capture of Oscillating Water Column array

The study of OWC array hydrodynamic performance has been a focus of attention. However, due to the complex flow and strong nonlinearity generated during its interaction with waves, the main challenge and key focus in current research is how to simulate this process both quickly and accurately. A fast-numerical model of the OWC is developed using the open-source software package OpenFOAM. The numerical model replaces the orifice plate with Forchheimer-flow and the vertical walls with an immersed boundary. Compared to the traditional numerical model, the fast-numerical model can speed up the simulation by at least 10 times. The impact of row spacing and column spacing on the array gain is systematically investigated through the simulations of two-OWCs array, four-OWCs lattice array, and five-OWCs interlaced array. The results show that a two-OWCs array can generate a wave focusing area at its rear, and the location of this area changes with row spacings. For the four-OWCs lattice array, the row spacing increasing causes the wave focusing area to move closer to the array. The proximity of the wave focusing area enhances the power output of the second row of OWCs. After increasing the row and column spacing, the reflection of waves from the second row of OWCs to the first row is decreased, resulting in a reduction in the power output of the first row of OWCs. In contrast to the four-OWC lattice array, the five-OWCs interlaced array demonstrates a lower array gain in the first row of OWCs, with the second row displaying a higher array gain.

TSRDet: A Table Structure Recognition Method Based on Row-Column Detection

As one of the most commonly used and important data carriers, tables have the advantages of high structuring, strong readability and strong flexibility. However, in reality, tables usually present various forms, such as Excel, images, etc. Among them, the information in the table image cannot be read directly, let alone further applied. Therefore, the research related to image-based table recognition is crucial. It contains the table structure recognition and the table content recognition. Among them, table structure recognition is the most important and difficult task because the table structure is abstract and changeable. In order to address this problem, we propose an innovative table structure recognition method, named TSRDet (Table Structure Recognition based on object Detection). It includes a row-column detection method, named SACNet (StripAttention-CenterNet) and the corresponding post-processing. SACNet is an improved version of the original CenterNet. The specific improvements include the following: firstly, we introduce the Swin Transformer as the encoder to obtain the global feature map of the image. Then, we propose a plug-and-play row-column attention module, including a channel attention module and a row-column spatial attention module. It improves the detection accuracy of rows and columns by capturing long-range row-column feature maps in the image. After completing the row-column detection, this paper also designs a simple and fast post-processing to generate the table structure based on the row-column detection results. Experimental results show that for row-column detection, SACNet has high detection accuracy, even at a high IoU threshold. Specifically, when the threshold is 0.75, its mAP of row detection and column detection still exceeds 90%, which is 91.40% and 92.73% respectively. In addition, in the comparative experiment with the existing object detection methods, SACNet’s performance was significantly better than that of all others. For table structure recognition, the TEDS-Struct score of TSRDet is 95.7%, which shows competitive performance in table structure recognition, and verifies the rationality and superiority of the proposed method.

Probing 4 × 4 quark mixing matrix

Without adhering to any specific model, we have presented 4 [Formula: see text] 4 quark mixing matrix as an extension of the 3 [Formula: see text] 3 Particle Data Group (PDG) parametrization of the Cabibbo–Kobayashi–Maskawa (CKM) matrix Using unitarity constraints as well as the hierarchy among the elements of the 3 [Formula: see text] 3 CKM matrix, we have found the hierarchy among the fourth row and fourth column elements of the 4 [Formula: see text] 4 quark mixing matrix. Further, for the fourth generation case, we have explicitly found the nine independent rephasing invariant parameters [Formula: see text]. Also, using phenomenological estimates of the fourth row and fourth column elements, we have numerically evaluated these nine parameters.

Stone column strategies for mitigating liquefaction-induced uplift of tunnels

Tunnels embedded in liquefiable soil are frequently subjected to uplift and sustain serious damage during major earthquakes. Mitigation methods to prevent the flotation of these tunnels must be developed and implemented in the natural environment. For this purpose, this study proposes a new method that uses stone columns to enhance soil drainage and mitigate soil liquefaction around tunnels. A circular tunnel in liquefied soil is simulated using a 2D finite element model, PLAXIS 2D, and subjected to a sinusoidal input motion. The tunnel's pre-construction and post-construction scenarios are examined. Parametric studies are carried out to investigate the effect of changing several parameters, such as the distance between stone columns and tunnel springing, the diameter of stone columns, the spacing between stone columns, the number of stone column rows, and the stone column arrangement patterns on the effectiveness of liquefaction mitigation. The study reveals that liquefaction mitigation is enhanced by using stone columns closer to tunnel springing, with larger diameters, less spacing, and more rows of stone columns that are arranged in a square pattern. It also emphasizes the importance of timely implementation of stone columns for maximum benefit.

Row–Column Separated Attention Based Low‐Light Image/Video Enhancement

AbstractU‐Net structure is widely used for low‐light image/video enhancement. The enhanced images result in areas with large local noise and loss of more details without proper guidance for global information. Attention mechanisms can better focus on and use global information. However, attention to images could significantly increase the number of parameters and computations. We propose a Row–Column Separated Attention module (RCSA) inserted after an improved U‐Net. The RCSA module's input is the mean and maximum of the row and column of the feature map, which utilizes global information to guide local information with fewer parameters. We propose two temporal loss functions to apply the method to low‐light video enhancement and maintain temporal consistency. Extensive experiments on the LOL, MIT Adobe FiveK image, and SDSD video datasets demonstrate the effectiveness of our approach.

Research on the strategy for average consensus control of flywheel energy storage array system based on lifecycle

In the domain of clean energy, the flywheel energy storage array system (FESAS) is widely employed for efficient and renewable energy storage to stabilize power grids and smooth power variations. However, managing the lifecycle of FESAS and achieving coordinated control presents a considerable challenge. This study integrates a lifecycle parameter based on the average consensus algorithm, enabling flywheels with longer lifecycles to handle additional charging and discharging tasks, while those with shorter lifecycles participate less. This approach effectively balances flywheel lifecycles and extends the overall lifespan of FESAS. Experimental findings reveal that when the P matrix satisfies the condition of equal row sums or column sums, the power distribution parameters tend toward average consensus, enabling FESAS to efficiently smooth unbalanced power. In undirected graphs, the symmetry of the P matrix causes the auxiliary variables to tend toward average consensus, effectively suppressing unbalanced power. For directed unbalanced graphs, this study proposes Error Balancing Iterative Algorithm (EBTA) to construct a doubly stochastic matrix P, facilitating the convergence of auxiliary variables to average consensus and effectively suppressing unbalanced power. The experimental results validate the efficacy of our proposed approach. This research establishes a theoretical foundation for the intelligent coordinated control of FESAS, highlighting the pivotal role of lifecycle parameters, and offers valuable insights for enhancing the performance and lifespan of FESAS.

Developing a Complete Sentence Severity Scale using Extended Goodman RC models

Abstract Purpose The aim of this paper is to construct a single sentence severity scale incorporating the full range of custodial and non-custodial sentences meted out by the courts. Such a scale would allow us to measure and rank the severity of sentences, relative to other sentences. Methods We use disaggregated individual level sentencing data to model the association between offenses and their associated sentences using the Goodman Row Column (RC) Association Model. We then extend this model to control for three legal factors; conviction history, offense plea, and number of offenses, to produce a series of standardised scores. We use linear interpolation and extrapolation to convert the scores to equivalent days in custody. Results The scores from the model enable the sentences to be ranked in order of severity; longer custodial sentences dominate at the severe end whilst non-custodial sentences congregate towards the lower end. In the middle of the scale, non-custodial and shorter custodial sentences interweave. We then demonstrate one use of the scale by applying it to Crown Court data, illustrating change in sentencing severity over time. Conclusions The Goodman RC Association Model provides a suitable methodology for scoring sentence severity on a single scale. The study found that by extending the model, we were also able to control for three legal factors. The sentence severity scale, as a research tool is specific to England and Wales but the method is universal and can be applied in any jurisdiction where the relevant data is available.

New families of quaternionic Hadamard matrices

A quaternionic Hadamard matrix (QHM) of order n is an n×n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n\ imes n$$\\end{document} matrix H with non-zero entries in the quaternions such that HH∗=nIn\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$HH^*=nI_n$$\\end{document}, where In\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$I_n$$\\end{document} and H∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H^*$$\\end{document} denote the identity matrix and the conjugate-transpose of H, respectively. A QHM is dephased if all the entries in its first row and first column are 1, and it is non-commutative if its entries generate a non-commutative group. The aim of our work is to provide new constructions of infinitely many (non-commutative dephased) QHMs; such matrices are used by Farkas et al. (IEEE Trans Inform Theory 69(6):3814–3824, 2023) to produce mutually unbiased measurements.

MCML-BF: A Metal-Column Embedded Microstrip Line Transmission Structure with Bias Feeders for Beam-Scanning Leakage Antenna Design.

This article proposes a novel fixed-frequency beam scanning leakage antenna based on a liquid crystal metamaterial (LCM) and adopting a metal column embedded microstrip line (MCML) transmission structure. Based on the microstrip line (ML) transmission structure, it was observed that by adding two rows of metal columns in the dielectric substrate, electromagnetic waves can be more effectively transmitted to reduce dissipation, and attenuation loss can be lowered to improve energy radiation efficiency. This antenna couples TEM mode electromagnetic waves into free space by periodically arranging 72 complementary split ring resonators (CSRRs). The LC layer is encapsulated in the transmission medium between the ML and the metal grounding plate. The simulation results show that the antenna can achieve a 106° continuous beam turning from reverse -52° to forward 54° at a frequency of 38 GHz with the holographic principle. In practical applications, beam scanning is achieved by applying a DC bias voltage to the LC layer to adjust the LC dielectric constant. We designed a sector-blocking bias feeder structure to minimize the impact of RF signals on the DC source and avoid the effect of DC bias on antenna radiation. Further comparative experiments revealed that the bias feeder can significantly diminish the influence between the two sources, thereby reducing the impact of bias voltage introduced by LC layer feeding on antenna performance. Compared with existing approaches, the antenna array simultaneously combines the advantages of high frequency band, high gain, wide beam scanning range, and low loss.

The statistical rate for support matrix machines under low rankness and row (column) sparsity

The statistical rate for support matrix machines under low rankness and row (column) sparsity

Construction of 2fi-optimal row–column designs

Row–column designs that provide unconfounded estimation of all main effects and the maximum number of two-factor interactions (2fi’s) are called 2fi-optimal. This issue has been paid great attention recently for its wide application in industrial or physical experiments. The constructions of 2fi-optimal two-level and three-level full factorial and fractional factorial row–column designs have been proposed. However, the results for higher prime levels have not been achieved yet. In this paper, we give theoretical constructions of 2fi-optimal sn full factorial row–column designs for any odd prime level s and any parameter combination, and theoretical constructions of 2fi-optimal sn−1 fractional factorial row–column designs for any prime level s and any parameter combination.

Development of Random Forest Model for Stroke Prediction

Stroke is a significant cause of mortality and morbidity worldwide, and early detection and prevention of stroke are essential for improving patient outcomes. Machine learning algorithms have been used in recent years to predict the risk of stroke by leveraging large amounts of clinical and demographic data. The development of a stroke prediction system using Random Forest machine learning algorithm is the main objective of this thesis. The primary goal of the project is to increase the accuracy of stroke detection while addressing the shortcomings of the current system, which include real- time deployment and interpretability issues with logistic regression. The development and use of an ensemble machine learning-based stroke prediction system, performance optimization through the use of ensemble machine learning algorithms, performance assessment, and real-time model deployment through the use of Python Django are among the goals of the research. The study's potential to improve public health by lessening the severity and consequences of strokes through early diagnosis and treatment makes it significant. Data collection, preprocessing, model selection, evaluation, and real-time deployment using Python Django are all part of the research technique. Our dataset consists of 5110 rows of tuples and columns with total size of 69kg. The performance of our stroke prediction algorithm was evaluated using confusion metrics-consisting of accuracy, precision, recall and F1-score. At the end of the research, Random Forest model gave an accuracy of 98.5% compared to the existing model logistic regression which has 86% accuracy.

Optimal Leveraging of a Gifford-McMahon Cryocooler’s Regenerative Cooling Power for SNSPD Applications

Superconducting nanowire single photon detectors (SNSPDs) offer unparalleled efficiency, minimal dark count rates, and picosecond jitter, making them ideal for single photon detector applications across the visible to mid-IR spectrum. A common cryogenic system used to reach these detectors’ optimal operating temperatures (>1 K) consists of Sumitomo’s compact RDK101 Gifford McMahon Cryocooler (GMC) running on an Zephyr air cooled compressor, coupled with a helium four (4He) adsorption stage. In this work, our aim is to provide measurements of the RDK101 GMC second stage regenerator tube cooling power at several locations along its length. We then characterise the performance of the adsorption cooler with heat loads applied to the regenerator tube. Our measurements demonstrate that heat loads of 1.2 W can be intercepted at the tube’s section near the GMC’s first cooling stage, with a negligible effect on the performance of the adsorption cooler. The thermal conductivity of yellow brass coaxial was characterised from 4 K to 40 K. Here we show that the heat load from 64 coaxial cable can be optimally intercepted with the defined regenerator cooling power. These results indicate that a 1024-pixel SNSPD array using a 32x32 row column multiplexing architecture could be successfully implemented in this cryogenic platform.

Clonal Evaluation and Genetic Divergence Studies in Mulberry Genotypes

Aims: This study aims to evaluate the wood quality and leaf quality traits of selected mulberry clones and assess the genetic divergence among them, providing valuable insights for the development of superior genotypes with enhanced economic value for the sericulture and agroforestry sectors.
 Study design: Twenty-one genetic resources of mulberry were collected from various regions in India and evaluated through clonal test in a Row Column Design to assess growth attributes and genetic divergence.
 Place and Duration of Study: The study was conducted in India, and the systematic progeny test and clonal evaluation were carried out in Forest College and Research Institute, Mettupalayam during 2018-2022.
 Methodology: The selected clones were evaluated for growth attributes such as plant height, diameter at breast height (DBH), volume, number of branches, leaf length, leaf width, petiole length, number of leaves, and leaf area. Genetic divergence was estimated using D2 statistics, and clustering of clones was performed using the 'GENRES' statistical package.
 Results: The study identified several clones with significantly higher growth attributes, highlighting their potential for selection in breeding and cultivation programs. Variability and heritability studies indicated that volume exhibited the highest heritability, suggesting strong genetic control and potential for improvement.
 Conclusion: Clones MI-0718, MI-0807, and MI-0845 showed superior growth performance and genetic divergence, indicating their suitability for further breeding and improvement programs. The study provides insights into the genetic variability of mulberry clones and emphasizes selecting superior clones for enhancing wood volume and overall productivity.

Flatness detection of super-flat emery floor based on terrestrial laser scanning

Traditional flatness detection method with 2-m ruler and feeler gauge can only manually measure to obtain data at sparse locations. Terrestrial Laser Scanning (TLS) has been widely used in the detection task of small-scale components. To overcome the time-consuming and laborious problem of traditional method and extend the application of TLS-based method, an efficient, full-coverage and intelligent TLS-based flatness detection method applicable to large-scale floors is presented in this paper to help with the acceptance of emery floor. An algorithm is designed to find the optimal position of ruler above the ground profile and measure the deviation between ruler and ground. The results of two comparison experiments demonstrates that the TLS-based method proposed in this paper has a high detection accuracy. In the detection task of a super-flat emery floor, floor plan labeled with pass rates can be easily obtained. Color maps marked with unqualified areas can be used as a guide for concrete polishing. By comparing row detection and column detection results, the pass rate is significantly directional in some conditions.

LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation

A fundamental task in the realms of computer vision, Low-Rank Matrix Recovery (LRMR) focuses on the inherent low-rank structure precise recovery from incomplete data and/or corrupted measurements given that the rank is a known prior or accurately estimated. However, it remains challenging for existing rank estimation methods to accurately estimate the rank of an ill-conditioned matrix. Also, existing LRMR optimization methods are heavily dependent on the chosen parameters, and are therefore difficult to adapt to different situations. Addressing these issues, A novel LEarning-based low-rank matrix recovery with Rank Estimation (LERE) is proposed. More specifically, considering the characteristics of the Gerschgorin disk's center and radius, a new heuristic decision rule in the Gerschgorin Disk Theorem is significantly enhanced and the low-rank boundary can be exactly located, which leads to a marked improvement in the accuracy of rank estimation. According to the estimated rank, we select row and column sub-matrices from the observation matrix by uniformly random sampling. A 17-iteration feedforward-recurrent-mixed neural network is then adapted to learn the parameters in the sub-matrix recovery processing. Finally, by the correlation of the row sub-matrix and column sub-matrix, LERE successfully recovers the underlying low-rank matrix. Overall, LERE is more efficient and robust than existing LRMR methods. Experimental results demonstrate that LERE surpasses state-of-the-art (SOTA) methods. The code for this work is accessible at https://github.com/zhengqinxu/LERE.

Column Row Convolutional Neural Network: Reducing Parameters for Efficient Image Processing.

Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications.

Brain magnetic resonance images segmentation via improved mixtures of factor analyzers based on dynamic co-clustering

The main goal of this paper is to attain accurate and automatic detection of brain tumors in gray magnetic resonance images using reducing the local dimensions. We propose a novel model called Improved Mixtures of Factor Analyzers based on Dynamic Co-Clustering (IMFADCC) for the detection and localization of brain tumors. After image preprocessing and enhancement, the optimal numbers of row clusters and column clusters corresponding to each row cluster, alongside other model parameters, are determined concurrently based on the size of the tumor by our model. Then, using the optimal values obtained, the image is co-clustered by simultaneously clustering rows and columns until the block containing the tumor is identified. The output image is divided into a certain number of blocks, one of which contains the tumor. For post-processing, the identified block is binarized using minimum error thresholding. The proposed model accurately detects and locates the block binarized within the original image while considering the remainder of the image as background. The effectiveness of the proposed method was assessed by evaluating its accuracy, Dice, intersection on union, geometric mean, receiver operating characteristic curve, specificity, and sensitivity criteria on the BraTS2018, BraTS2019, and BraTS2020 datasets. The results show that our method has significant performance and is highly accurate in detecting tumors of different sizes in complex and high-size images. Moreover, our method outperforms the existing diagnostic methods.