Adversarial Momentum-Contrastive Pre-Training for Robust Feature Extraction

Recently proposed adversarial self-supervised learning methods usually require big batches and long training epochs to extract robust features, which is not friendly in practical application. In this paper, we present a novel adversarial momentum-contrastive learning approach that leverages two memory banks to track the invariant features across different mini-batches. These memory banks can be efficiently incorporated into each iteration and help the network to learn more robust feature representations with smaller batches and far fewer epochs. Furthermore, after fine-tuning on the classification tasks, the proposed approach can meet or exceed the performance of some state-of-the-art supervised baselines on real world datasets. Our code is available at \url{https://github.com/MTandHJ/amoc}.Comment: 16 pages;6 figures; preprin

微粒的功能及其与糖尿病的研究进展

MPs are vesicles released by cells when stimulated by physical (e.g. shear force) or chemical (e.g. agonists) factors, as well as cells undergoing apoptosis or exposed to inflammatory conditions . MPs are 100~1000 nm in diameter, have membrane cytoskeletons, express phosphatidylserine (PS) on the surface, and lack of nuclei. Surface molecules, enzymes, RNA and DNA are conveyed via MPs from origin cells to target cells. As mediators of information transfer, MPs have been proposed to impose pro-inflammatory and pro-coagulant effects in many disease states, such as cancer, venous thromboembolism, arteriosclerosis, and diabetes mellitus. The hypercoagulable state associated with diabetes is well recognized. More T2DM patients have died from thrombotic diseases. The endothelium-derived MPs in diabetic patients were elevated. TF-positive MPs concentration was increased and procoagulant activity of MPs was elevated. It is worth to research the role of MPs in the hypercoagulable state of diabetic patients.微粒是由细胞受物理或化学刺激或者凋亡时产生的带细胞膜的囊泡。其大小在100~1000nm之间，表面的膜细胞骨架上含有磷脂酰丝氨酸及多种细胞分子，其包膜内无核，含有细胞分子、RNA、DNA等。在机体中起到信号传递的作用。近年来发现，微粒参与多种疾病的病理生理过程，如糖尿病、冠心病、深静脉血栓、肿瘤等，在以上疾病过程中，微粒主要有促炎、促凝等作用。糖尿病患者存在明显的高凝状态，糖尿病患者合并心血管疾病死亡率近年来有增高趋势，血栓事件较非糖尿病患者增加。糖尿病患者血浆中内皮源性微粒数目增多，组织因子阳性微粒比例升高，微粒促凝活性升高。微粒的改变是否参与糖尿病患者的高凝状态的发生值得研究

Missingness Augmentation: A General Approach for Improving Generative Imputation Models

Missing data imputation is a fundamental problem in data analysis, and many studies have been conducted to improve its performance by exploring model structures and learning procedures. However, data augmentation, as a simple yet effective method, has not received enough attention in this area. In this paper, we propose a novel data augmentation method called Missingness Augmentation (MisA) for generative imputation models. Our approach dynamically produces incomplete samples at each epoch by utilizing the generator's output, constraining the augmented samples using a simple reconstruction loss, and combining this loss with the original loss to form the final optimization objective. As a general augmentation technique, MisA can be easily integrated into generative imputation frameworks, providing a simple yet effective way to enhance their performance. Experimental results demonstrate that MisA significantly improves the performance of many recently proposed generative imputation models on a variety of tabular and image datasets. The code is available at \url{https://github.com/WYu-Feng/Missingness-Augmentation}.Comment: 20 page

Fuzzy ARTMAP Ensemble Based Decision Making and Application

Because the performance of single FAM is affected by the sequence of sample presentation for the offline mode of training, a fuzzy ARTMAP (FAM) ensemble approach based on the improved Bayesian belief method is supposed to improve the classification accuracy. The training samples are input into a committee of FAMs in different sequence, the output from these FAMs is combined, and the final decision is derived by the improved Bayesian belief method. The experiment results show that the proposed FAMs’ ensemble can classify the different category reliably and has a better classification performance compared with single FAM

Fuzzy ARTMAP Ensemble Based Decision Making and Application

Because the performance of single FAM is affected by the sequence of sample presentation for the offline mode of training, a fuzzy ARTMAP (FAM) ensemble approach based on the improved Bayesian belief method is supposed to improve the classification accuracy. The training samples are input into a committee of FAMs in different sequence, the output from these FAMs is combined, and the final decision is derived by the improved Bayesian belief method. The experiment results show that the proposed FAMs' ensemble can classify the different category reliably and has a better classification performance compared with single FAM