Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction

Dimensionality reduction is an essential technique for multi-way large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ability and flexibility. However, the traditional TR decomposition algorithms suffer from high computational cost when facing large-scale data. In this paper, taking advantages of the recently proposed tensor random projection method, we propose two TR decomposition algorithms. By employing random projection on every mode of the large-scale tensor, the TR decomposition can be processed at a much smaller scale. The simulation experiment shows that the proposed algorithms are $4-25$ times faster than traditional algorithms without loss of accuracy, and our algorithms show superior performance in deep learning dataset compression and hyperspectral image reconstruction experiments compared to other randomized algorithms.Comment: ICASSP submissio

Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particular, for tensor ring (TR) decomposition, the number of model possibilities grows exponentially with the tensor order, which makes it rather challenging to find the optimal TR decomposition. In this paper, by exploiting the low-rank structure of the TR latent space, we propose a novel tensor completion method which is robust to model selection. In contrast to imposing the low-rank constraint on the data space, we introduce nuclear norm regularization on the latent TR factors, resulting in the optimization step using singular value decomposition (SVD) being performed at a much smaller scale. By leveraging the alternating direction method of multipliers (ADMM) scheme, the latent TR factors with optimal rank and the recovered tensor can be obtained simultaneously. Our proposed algorithm is shown to effectively alleviate the burden of TR-rank selection, thereby greatly reducing the computational cost. The extensive experimental results on both synthetic and real-world data demonstrate the superior performance and efficiency of the proposed approach against the state-of-the-art algorithms

Longitudinal study on blood and biochemical indexes of Tibetan and Han in high altitude area

ObjectiveThis study aims to review the blood routine and biochemical indicators of the plateau population for three consecutive years, and analyze the impact of the plateau on these blood indicators of the Tibetan population and the Han immigrant population.MethodThese parameters were extracted from the Laboratory Department of Ali District People’s Hospital in Tibet from January 2019 to December 2021, including blood routine, liver and kidney function, blood lipids, myocardial enzyme spectrum, and rheumatic factor indicators. Changes in these parameters were analyzed over 3 consecutive years according to inclusion and exclusion criteria.ResultA total of 114 Tibetans and 93 Hans participated in the study. These parameters were significantly different between Tibetan and Han populations. Red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), mean hemoglobin content (MCH), mean corpuscular hemoglobin concentration (MCHC), white blood cells (WBC), lymphocytes (LYMPH) and monocytes (MONO) were significantly higher in Hans than Tibetans (p < 0.05). Biochemically, total bilirubin (TBIL), direct bilirubin (DBIL), albumin (ALB), urea nitrogen (Urea), creatinine (Cr), uric acid (UA), glucose (GLU), triglycerides (TG) and creatine kinase isoenzyme (CKMB) were significantly higher in Hans than Tibetans; aspartate aminotransferase (AST), glutamyl transpeptidase (GGT), alkaline phosphatase (ALP), antistreptolysin (ASO), and C-reactive protein (CRP) were significantly higher in Tibetans than Hans (p < 0.05). There were no obvious continuous upward or downward trend of the parameters for 3 consecutive years.ConclusionIn high-altitude areas, Han immigrants have long-term stress changes compared with Tibetans. The main differences are reflected in the blood system, liver and kidney functions, etc., which provide basic data for further research on the health status of plateau populations

Deep learning in classifying depth of anesthesia (DoA)

© Springer Nature Switzerland AG 2019. This present study is what we think is one of the first studies to apply Deep Learning to learn depth of anesthesia (DoA) levels based solely on the raw EEG signal from a single channel (electrode) originated from many subjects under full anesthesia. The application of Deep Neural Networks to detect levels of Anesthesia from Electroencephalogram (EEG) is relatively new field and has not been addressed extensively in current researches as done with other fields. The peculiarities of the study emerges from not using any type of pre-processing at all which is usually done to the EEG signal in order to filter it or have it in better shape, but rather accept the signal in its raw nature. This could make the study a peculiar, especially with using new development tool that seldom has been used in deep learning which is the DeepLEarning4J (DL4J), the java programming environment platform made easy and tailored for deep neural network learning purposes. Results up to 97% in detecting two levels of Anesthesia have been reported successfully

Immunogenomic-Based Analysis of Hierarchical Clustering of Diffuse Large Cell Lymphoma

Diffuse large B cell lymphoma (DLBCL) is one of the most usual types of adult lymphoma with heterogeneousness in histological morphology, prognosis, and clinical indications. Prior to this, several studies were carried out to determine the DLBCL subtype based on the analysis of the genome profile. However, classification based on assessment of genes related to the immune system has limited clinical significance for DLBCL. We systematically explored the DLBCL gene expression dataset and provided publicly available clinical information on patients with GEO. In this research, 928 DLBCL samples were applied, and we calculated 29 immune-related genomes’ enrichment levels in each sample and stratified them into high immunity (Immunity_H, n=135, 28.7%), moderate immunity (Immunity_M, n=135, 28.7%), and low immunity (Immunity_L, n=12, 2.6%) that was based on ssGSEA score. The ESTIMATE algorithm was used to calculate stromal scores (range 586.88 to 1982.43), immune scores, estimated scores (range 2,618.2 to 8,098.14), and tumor purity (range 0.216 to 0.976). All of them were significantly correlated with immune subtypes (Kruskal-Wallis test, p<0.001). At the same time, the correlation of related genes was analyzed by immunohistochemistry staining. In addition, DLBCL cells were cultured in transfected and in vitro with siRNA to verify correlation analysis and gene expression. Finally, human peripheral blood lymphocytes were incubated with DLBCL cells and stained. Flow cytometry was applied to analyze genes’ influence on immune function. By analysis, immune checkpoint and HLA gene expression levels were higher in the Immunity_H group (Kruskal-Wallis test, p<0.05). The levels of Tfhs (follicular helper T cells), monocytes, CD8+ T cells, M1 macrophages, M2 macrophages, and CD4+ memory-activated T cells were the most excellent in Immunity_H, and the total survival rate was higher in the Immunity_L. Through analysis, IRF4 (MUM1) was identified by us as immunotherapeutic target and a potential prognostic marker for DLBCL, which was made sure by using molecular biology experimentations. To conclude, immunosignature made a connection between DLBCL subtypes playing a position in DLBCL prognostic stratification. Immunocharacteristics-related DLBCL subtypes’ construction predicts expected patient results and supplies conceivable immunotherapy candida