Risk Factors and Prevalence of Helicobacter pylori

Aim. The aim of this study was to investigate the prevalence and risk factors of H. pylori infection in areas with high prevalence of gastric cancer in Jiangsu Province, China. Methods. A prospective epidemiologic survey of H. pylori infection was accomplished in a natural population of 5417 individuals in Yangzhong city. Questionnaires and 13C-urea breath test for H. pylori infection were performed. Results. Among 5417 subjects who completed questionnaires and 13C-urea breath test, 3435 (63.41%) were H. pylori positive. The prevalence reached a peak at the age of 30–39 years (90.82%). There was significant difference between sexes and women had a higher infection rate than men. The prevalence of H. pylori infection was also associated with eating kipper food and fried food. No association between H. pylori prevalence and smoking or drinking was found. Compared to healthy individuals, people with dyspeptic diseases (peptic ulcer, gastroenteritis) presented a high prevalence of H. pylori infection. Using multivariate logistic regression analysis, age and history of peptic ulcer and gastroenteritis were the independent predictors for H. pylori infection. Conclusions. Yangzhong city had a high prevalence of H. pylori infection and was related to several risk factors. The underlying mechanisms are needed to be further investigated

Satisfaction with the Overseas Education in China: A Survey on 44 Institutions of Higher Learning in Jiangsu Province

In order to fully grasp the service level, management quality and effectiveness of the overseas education in China, a satisfaction survey was carried out on 44 institutions of higher learning in Jiangsu province from 5 dimensions of school learning, school life, school administration, surrounding environment, and urban civility and environment. Findings include 1. Students’ nationality: students from Asia and Africa are the most while students from Oceania are the least; 2. Student category: more than 1/3 of overseas students are Chinese language students, but overseas students in Jiangsu province have a higher level of matriculate quality and educational pursuit, for those pursuing a higher education degree at the bachelor’s, the master’s and the doctoral level have reached an accumulative 60% of all; 3. The overall student satisfaction is above average, with a significant difference between different colleges and universities within Jiangsu province. Satisfaction with surrounding environment gets the highest score, but dissatisfaction of a certain degree occurs with library, website construction, hostel accommodation, other school facilities and services, and urban civility and environment. As is revealed by the investigation, 70.45% of students respond that their college or university does not have a canteen exclusive for overseas students; 4. There is a significant difference in overall satisfaction and satisfaction with each of the 5 assessment dimensions between different categories of students; 5. A multiple linear regression model shows that the overall accountability is 98.8% on the 11 assessment parameters in the dimensions of school learning and school life (curriculum, teacher, school system, teaching facilities, library, website construction, hostel accommodation, canteen catering, entertainment & sports activities, student affairs and services, and other school facilities and services). Suggestions are as follows: 1. “Foreign language prompts and guidance” are the topmost demand of overseas students in China; 2. The basic living conditions for overseas students should be improved; 3. The international service level of educational administrative employees should be raised; 4. The construction of school library and a portal website should be made to better meet the practical needs of overseas students; 5. The disparity in the overseas education between different colleges and universities in Jiangsu province should be narrowed and a standardized management highlighted. Keywords: Overseas education in China; overall satisfaction; multiple linear regression model; international service level

Analyzing the noise robustness of deep neural networks

Adversarial examples, generated by adding small but intentionally imperceptible perturbations to normal examples, can mislead deep neural networks (DNNs) to make incorrect predictions. Although much work has been done on both adversarial attack and defense, a fine-grained understanding of adversarial examples is still lacking. To address this issue, we present a visual analysis method to explain why adversarial examples are misclassified. The key is to compare and analyze the datapaths of both the adversarial and normal examples. A datapath is a group of critical neurons along with their connections. We formulate the datapath extraction as a subset selection problem and solve it by constructing and training a neural network. A multi-level visualization consisting of a network-level visualization of data flows, a layer-level visualization of feature maps, and a neuron-level visualization of learned features, has been designed to help investigate how datapaths of adversarial and normal examples diverge and merge in the prediction process. A quantitative evaluation and a case study were conducted to demonstrate the promise of our method to explain the misclassification of adversarial examples

Photonic Memristor for Future Computing: A Perspective

Photonic computing and neuromorphic computing could address the inherent limitations of traditional von Neumann architecture and gradually invalidate Moore’s law. As photonics applications are capable of storing and processing data in an optical manner with unprecedented bandwidth and high speed, twoâ terminal photonic memristors with a remote optical control of resistive switching behaviors at defined wavelengths ensure the benefit of onâ chip integration, low power consumption, multilevel data storage, and a large variation margin, suggesting promising advantages for both photonic and neuromorphic computing. Herein, the development of photonic memristors is reviewed, as well as their application in photonic computing and emulation on optogeneticsâ modulated artificial synapses. Different photoactive materials acting as both photosensing and storage media are discussed in terms of their opticalâ tunable memory behaviors and underlying resistive switching mechanism with consideration of photogating and photovoltaic effects. Moreover, lightâ involved logic operations, systemâ level integration, and lightâ controlled artificial synaptic memristors along with improved learning tasks performance are presented. Furthermore, the challenges in the field are discussed, such as the lack of a comprehensive understanding of microscopic mechanisms under light illumination and a general constraint of inferior nearâ infrared (NIR) sensitivity.The development of photonic memristors and their application in photonic computing and emulation on optogeneticsâ modulated artificial synapses are reviewed. Photoactive materials as photosensing and storage media are discussed, considering their opticalâ tunable memory behavior and resistive switching mechanism including photogating and photovoltaic effect. Lightâ involved logic operations, system level integration, and artificial synaptic memristors along with improved learning tasks performance are presented.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153103/1/adom201900766.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153103/2/adom201900766_am.pd

Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation

Continual learning entails learning a sequence of tasks and balancing their knowledge appropriately. With limited access to old training samples, much of the current work in deep neural networks has focused on overcoming catastrophic forgetting of old tasks in gradient-based optimization. However, the normalization layers provide an exception, as they are updated interdependently by the gradient and statistics of currently observed training samples, which require specialized strategies to mitigate recency bias. In this work, we focus on the most popular Batch Normalization (BN) and provide an in-depth theoretical analysis of its sub-optimality in continual learning. Our analysis demonstrates the dilemma between balance and adaptation of BN statistics for incremental tasks, which potentially affects training stability and generalization. Targeting on these particular challenges, we propose Adaptive Balance of BN (AdaB$^2$N), which incorporates appropriately a Bayesian-based strategy to adapt task-wise contributions and a modified momentum to balance BN statistics, corresponding to the training and testing stages. By implementing BN in a continual learning fashion, our approach achieves significant performance gains across a wide range of benchmarks, particularly for the challenging yet realistic online scenarios (e.g., up to 7.68%, 6.86% and 4.26% on Split CIFAR-10, Split CIFAR-100 and Split Mini-ImageNet, respectively). Our code is available at https://github.com/lvyilin/AdaB2N.Comment: Accepted by NeurIPS 202