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In this work, we propose a new method for modeling human reasoning about objects’ similarities. We assume that similarity depends on perceived intensities of objects’ attributes expressed by natural language expressions such as low, medium, and high. We show how to find the underlying structure of the matrix with intensities of objects’ similarities in the factor-analysis-like manner. The demonstrated approach is based on fuzzy logic and set theory principles, and it uses only maximum and minimum operators. Similarly to classic eigenvector decomposition, we aim at representing the initial linguistic ordinal-scale (LOS) matrix as a max–min product of other LOS matrix and its transpose. We call this reconstructing matrix a neuromatrix because we assume that such a process takes place at the neural level in our brain. We show and discuss on simple, illustrative examples, how the presented way of modeling grasps natural way of reasoning about similarities. The unique characteristics of our approach are treating smaller attribute intensities as less important in making decisions about similarities. This feature is consistent with how the human brain is functioning at a biological level. A neuron fires and passes information further only if input signals are strong enough. The proposal of the heuristic algorithm for finding the decomposition in practice is also introduced and applied to exemplary data from classic psychological studies on perceived similarities between colors and between nations. Finally, we perform a series of simulation experiments showing the effectiveness of the proposed heuristic.

Background Circular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown that circRNAs are closely related to the development of diseases. Because biological experiments to verify circRNA-disease associations are time-consuming and wasteful of resources, it is necessary to propose a reliable computational method to predict the potential candidate circRNA-disease associations for biological experiments to make them more efficient. Results In this paper, we propose a double matrix completion method (DMCCDA) for predicting potential circRNA-disease associations. First, we constructed a similarity matrix of circRNA and disease according to circRNA sequence information and semantic disease information. We also built a Gauss interaction profile similarity matrix for circRNA and disease based on experimentally verified circRNA-disease associations. Then, the corresponding circRNA sequence similarity and semantic similarity of disease are used to update the association matrix from the perspective of circRNA and disease, respectively, by matrix multiplication. Finally, from the perspective of circRNA and disease, matrix completion is used to update the matrix block, which is formed by splicing the association matrix obtained in the previous step with the corresponding Gaussian similarity matrix. Compared with other approaches, the model of DMCCDA has a relatively good result in leave-one-out cross-validation and five-fold cross-validation. Additionally, the results of the case studies illustrate the effectiveness of the DMCCDA model. Conclusion The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.

Let V be a finite dimensional vector space over the field with p elements, where p is a prime number. Given arbitrary$\\alpha ,\\beta \\in \\mathrm {GL}(V)$, we consider the semidirect products$V\\rtimes \\langle \\alpha \\rangle $and$V\\rtimes \\langle \\beta \\rangle $, and show that if$V\\rtimes \\langle \\alpha \\rangle $and$V\\rtimes \\langle \\beta \\rangle $are isomorphic, then$\\alpha $must be similar to a power of$\\beta $that generates the same subgroup as$\\beta $; that is, if H and K are cyclic subgroups of$\\mathrm {GL}(V)$such that$V\\rtimes H\\cong V\\rtimes K$, then H and K must be conjugate subgroups of$\\mathrm {GL}(V)$. If we remove the cyclic condition, there exist examples of nonisomorphic , let alone nonconjugate, subgroups H and K of$\\mathrm {GL}(V)$such that$V\\rtimes H\\cong V\\rtimes K$. Even if we require that noncyclic subgroups H and K of$\\mathrm {GL}(V)$be abelian, we may still have$V\\rtimes H\\cong V\\rtimes K$with H and K nonconjugate in$\\mathrm {GL}(V)$, but in this case, H and K must at least be isomorphic. If we replace V by a free module U over${\\mathbb {Z}}/p^m{\\mathbb {Z}}$of finite rank, with$m>1$, it may happen that$U\\rtimes H\\cong U\\rtimes K$for nonconjugate cyclic subgroups of$\\mathrm {GL}(U)$. If we completely abandon our requirements on V , a sufficient criterion is given for a finite group G to admit nonconjugate cyclic subgroups H and K of$\\mathrm {Aut}(G)$such that$G\\rtimes H\\cong G\\rtimes K$. This criterion is satisfied by many groups.

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

In the field of user feature segmentation, the currently adopted segmentation methods have the defect of low segmentation accuracy. To address this problem, the study introduces the K-prototypes algorithm for user feature segmentation to improve the segmentation accuracy of user feature segmentation. The study first improves the traditional K-prototypes algorithm using fuzzy similarity matrix. The improved K-prototypes algorithm can effectively select the initial clustering center and fuzzy coefficients and weight coefficients, and pre-set the number of clusters in order to realize the accurate segmentation of user feature. After that, user feature segmentation model is constructed based on the improved K-prototypes algorithm to plan the best marketing methods for users with different characteristics. The study selected 605, 3200, and 684 data objects from the R15, D13, and credit approval datasets as experimental subject. Moreover, it compared the improved K-prototypes algorithm with the fuzzy C-means clustering algorithm and the density peak clustering algorithm in terms of clustering accuracy, root mean square error, mean absolute error, and clustering recall rate to evaluate the performance of the three algorithms. The performance advantages and disadvantages of the three algorithms were evaluated by accuracy, root mean square error, mean absolute error, and recall. The accuracy of the improved K-prototypes algorithm reached 0.9438, which was significantly higher than the other two algorithms. Moreover, the mean square error and mean absolute error of this algorithm were significantly lower than the other two algorithms, indicating that the clustering effect of this algorithm was significantly better than the other two algorithms. The recall of the improved K-prototypes algorithm reached 0.953, and the variation of recall was small, indicating the efficiency of this algorithm in dividing user features. All three algorithms were able to select the correct initial clustering center point for the improved K-prototypes algorithm under different dataset conditions, and the clustering purity of this algorithm was always maintained in the interval of 0.81-0.84. The outcomes reveal that the improved K-prototypes algorithm is able to accurately classify different users according to their characteristic requirements and can plan the best marketing methods for them.

Point cloud is usually used to construct the surface shape of three-dimensional geometric objects. Due to the disorder and irregularity of the point cloud, it is still a challenge to fully acquire the semantic features of the point cloud. With the development of graph neural network and graph convolution neural network, researchers are integrating point cloud and graph structure to better represent the semantic features of the point cloud. In this paper, we propose a novel graph convolutional neural network that integrates distance and direction (DDGCN), which constructs a dynamic neighborhood graph by obtaining the similarity matrix of the point cloud, and then uses several multi-layer perceptrons to obtain the local features of the point cloud. For the sake of making the intra-classes in the point cloud data more compact and the spacing between classes larger than the intra-class spacing, we propose a new loss function combined with center loss. The proposed DDGCN has been tested on ModelNet40 dataset, ShapeNet Part dataset and S3DIS dataset, and has achieved state-of-the-art performance in both classification and segmentation tasks.

Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals.

Traditional spectral clustering algorithms are sensitive to the similarity matrix, which impacts their performance. To address this, a local adaptive fuzzy spectral clustering (FSC) method is introduced, incorporating a fuzzy index to reduce this sensitivity. FSC also simplifies the traditional process through a local adaptive framework, optimizing the similarity matrix’s use. Experimental results show that FSC outperforms traditional methods, particularly on high-dimensional datasets with complex structures.

The increasing availability of multi-omics data is promising in enhancing genomic prediction in breeding and human genetics. However, integrating multi-omics data into genomic prediction models remains challenging due to complex relationships between omics layers and phenotypic outcomes. We propose Fusion Similarity Best Linear Unbiased Prediction (FSBLUP), a novel strategy that integrates genomic and intermediate omics data using a unified similarity matrix approach. FSBLUP systematically estimates how different omics layers contribute to phenotypic variation via machine-learning-optimized parameters that capture underlying genetic architecture of complex traits. FSBLUP demonstrates greater predictive accuracy than existing methods, as validated through theoretical and practical evaluations.

Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability.