GBSVM: Granular-ball Support Vector Machine

GBSVM (Granular-ball Support Vector Machine) is an important attempt to use the coarse granularity of a granular-ball as the input to construct a classifier instead of a data point. It is the first classifier whose input contains no points, i.e., $x_i$, in the history of machine learning. However, on the one hand, its dual model is not derived, and the algorithm has not been implemented and can not be applied. On the other hand, there are some errors in its existing model. To address these problems, this paper has fixed the errors of the original model of GBSVM, and derived its dual model. Furthermore, an algorithm is designed using particle swarm optimization algorithm to solve the dual model. The experimental results on the UCI benchmark datasets demonstrate that GBSVM has good robustness and efficiency

GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing

Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST

GBG++: A Fast and Stable Granular Ball Generation Method for Classification

Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular ball generation (GBG) and multi-granularity learning based on the granular ball (GB). However, the stability and efficiency of existing GBG methods need to be further improved due to their strong dependence on $k$-means or $k$-division. In addition, GB-based classifiers only unilaterally consider the GB's geometric characteristics to construct classification rules, but the GB's quality is ignored. Therefore, in this paper, based on the attention mechanism, a fast and stable GBG (GBG++) method is proposed first. Specifically, the proposed GBG++ method only needs to calculate the distances from the data-driven center to the undivided samples when splitting each GB instead of randomly selecting the center and calculating the distances between it and all samples. Moreover, an outlier detection method is introduced to identify local outliers. Consequently, the GBG++ method can significantly improve effectiveness, robustness, and efficiency while being absolutely stable. Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary. Finally, the experimental results indicate that the proposed method outperforms several existing GB-based classifiers and classical machine learning classifiers on $24$ public benchmark datasets

Fingerprint Image Segmentation Algorithm Based on Contourlet Transform Technology

This paper briefly introduces two classic algorithms for fingerprint image processing, which include the soft threshold denoise algorithm of wavelet domain based on wavelet domain and the fingerprint image enhancement algorithm based on Gabor function. Contourlet transform has good texture sensitivity and can be used for the segmentation enforcement of the fingerprint image. The method proposed in this paper has attained the final fingerprint segmentation image through utilizing a modified denoising for a high-frequency coefficient after Contourlet decomposition, highlighting the fingerprint ridge line through modulus maxima detection and finally connecting the broken fingerprint line using a value filter in direction. It can attain richer direction information than the method based on wavelet transform and Gabor function and can make the positioning of detailed features more accurate. However, its ridge should be more coherent. Experiments have shown that this algorithm is obviously superior in fingerprint features detection

Association between perceived life stress and subjective well-being among Chinese perimenopausal women: a moderated mediation analysis

Background The impact of perceived life stress on subjective well-being has been well-established; while few studies have explored the mediating and moderating mechanisms of the association between perceived life stress and subjective well-being among perimenopausal women. This study is aimed at exploring the mediating effect of depressive symptoms and the role of interests/hobbies as a moderator in the association between perceived life stress and subjective well-being among perimenopausal women. Methods The participants were 1,104 perimenopausal women at the age of 40 to 60, who were asked to complete a paper-based questionnaire. A single item was used to measure self-perceived life stress and interests/hobbies. The Zung Self-rating Depression Scale (SDS) and Subjective Well-being Scale for Chinese Citizens (SWBS-CC) were applied to assess both depressive symptoms and subjective well-being. Multiple linear regression analysis and the PROCESS macro were adopted to analyse not only the mediating effect of depressive symptoms but also the moderating role of interests/hobbies. Results Perceived life stress was negatively associated with subjective well-being (B =  − 1.424, β =  − 0.101, P < 0.001). The impact of perceived life stress on subjective well-being was partially mediated by depressive symptoms (mediation effect = −0.760, 95% confidence intervals (CI) [−1.129, −0.415]). In addition, the interaction term between depressive symptoms and interests/hobbies was significantly related to subjective well-being (β =  − 0.060, P < 0.05), indicating moderating effect. Moderated mediation had a significant index (Index = −0.220, SE = 0.099, 95% CI [−0.460, −0.060]). Conclusions Perceived life stress was negatively related to subjective well-being. The impact of perceived life stress on subjective well-being was mediated by depressive symptoms. Besides, interests/hobbies moderated the indirect effect of depressive symptoms on the relationship between perceived life stress and subjective well-being

Novel Reversible-Binding PET Ligands for Imaging Monoacylglycerol Lipase Based on the Piperazinyl Azetidine Scaffold

Monoacylglycerol lipase (MAGL) is a 33 kDa serine protease primarily responsible for hydrolyzing 2-arachidonoylglycerol into the proinflammatory eicosanoid precursor arachidonic acid in the central nervous system. Inhibition of MAGL constitutes an attractive therapeutic concept for treating psychiatric disorders and neurodegenerative diseases. Herein, we present the design and synthesis of multiple reversible MAGL inhibitor candidates based on a piperazinyl azetidine scaffold. Compounds 10 and 15 were identified as the best-performing reversible MAGL inhibitors by pharmacological evaluations, thus channeling their radiolabeling with fluorine-18 in high radiochemical yields and favorable molar activity. Furthermore, evaluation of [18F]10 and [18F]15 ([18F]MAGL-2102) by autoradiography and positron emission tomography (PET) imaging in rodents and nonhuman primates demonstrated favorable brain uptakes, heterogeneous radioactivity distribution, good specific binding, and adequate brain kinetics, and [18F]15 demonstrated a better performance. In conclusion, [18F]15 was found to be a suitable PET radioligand for the visualization of MAGL, harboring potential for the successful translation into humans