Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders.However,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia diagnosis.In this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven models.Specifically,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation robust.Also,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.The typical topological properties and discriminative features can be preserved entirely.Furthermore,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive diseases.Attempts on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional networks.The classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related augmentedmodels.Overall,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks.

A multi-modal clustering method for traditonal Chinese medicine clinical data via media convergence

Media convergence is a media change led by technological innovation.Applying media convergence technology to the study of clustering in Chinese medicine can significantly exploit the advantages of media fusion.Obtaining consistent and complementary information among multiple modalities through media convergence can provide technical support for clustering.This article presents an approach based on Media Convergence and Graph convolution Encoder Clustering(MCGEC)for traditonal Chinese medicine(TCM)clinical data.It feeds modal information and graph structure from media information into a multi-modal graph convolution encoder to obtain the media feature representation learnt from multiple modalities.MCGEC captures latent information from various modalities by fusion and optimises the feature representations and network architecture with learnt clustering labels.The experiment is conducted on real-world multimodal TCM clinical data,including information like images and text.MCGEC has improved clustering results compared to the generic single-modal clustering methods and the current more advanced multi-modal clustering methods.MCGEC applied to TCM clinical datasets can achieve better results.Integrating multimedia features into clustering algorithms offers significant benefits compared to single-modal clustering approaches that simply concatenate features from different modalities.It provides practical technical support for multi-modal clustering in the TCM field incorporating multimedia features.

An Amorphous 2-Dimensional Barcode

Most existing 2-dimensional barcodes are designed with a fixed shape and clear area.Having a fixed shape and clear area makes the barcode difficult to lay out with other text and pictures.To solve this problem,an amorphous 2-dimensional barcode is presented in this paper.The barcode uses encoding graph units to encode data.There are two key points in a 2-dimensional barcode:One is the encoding graph unit,the other is its encoding rules.Because encoding graph units of a 2-dimensional barcode are surrounded by other graphics,the units should be self-positioned and distinguished from other units.This paper presents an encoding graph unit generation algorithm,which is based on genetic algorithms.Encoding rules of the barcode are also given in this paper.Those rules include data encoding rules and encoding graph unit sequence arrangement rules.Data encoding rules are used to encode data to an encoding graph unit sequence.Encoding graph unit sequence arrangement rules are used to embed the unit sequence in the target picture.In addition to those rules,it also discussed the steps to restore encoding graph unit sequence from a picture.In the experiments section of this paper,an example is provided to encode a string and embed it in a car ad picture by the barcode.According to encoding rules of the barcode,those encoding graphic units can be scattered and embedded in a picture with other graphics,so amorphous 2-dimensional barcode has no fixed shape.Take advantage of this,designer can present a more elegant design to lay out barcodes with other graphic elements.