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Characterizing Discourse about COVID-19 Vaccines:A Reddit Version of the Pandemic Story

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Health Data Science 2021年第1期2021卷 146-156页

作者： Wei Wu Hanjia Lyu jiebo luo Goergen Institute for Data Science University of RochesterRochesterUSA Department of Computer Science University of RochesterRochesterUSA

It has been one year since the outbreak of the COVID-19 *** good news is that vaccines developed by several manufacturers are being actively distributed ***,as more and more vaccines become available to the public,various concerns related to vaccines become the primary barriers that may hinder the public from getting *** the complexities of these concerns and their potential hazards,this study is aimed at offering a clear understanding about different population groups’underlying concerns when they talk about COVID-19 vaccines—particularly those active on *** goal is achieved by applying LDA and LIWC to characterize the pertaining discourse with insights generated through a combination of quantitative and qualitative *** include the following:(1)during the pandemic,the proportion of Reddit comments predominated by conspiracy theories outweighed that of any other topics;(2)each subreddit has its own user bases,so information posted in one subreddit may not reach that from other subreddits;and(3)since users’concerns vary across time and subreddits,communication strategies must be adjusted according to specific *** results of this study manifest challenges as well as opportunities in the process of designing effective communication and immunization programs.

关键词： offering discourse qualitative

Misinformation versus Facts:Understanding the Influence of News regarding COVID-19 Vaccines on Vaccine Uptake

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Health Data Science 2022年第1期2022卷 136-143页

作者： Hanjia Lyu Zihe Zheng jiebo luo Department of Computer Science University of RochesterRochesterUSA Goergen Institute for Data Science University of RochesterRochesterUSA

Background. There is a lot of fact-based information and misinformation in the online discourses and discussions about theCOVID-19 vaccines. Method. Using a sample of nearly four million geotagged English tweets and the data from the CDCCOVID Data Tracker, we conducted the Fama-MacBeth regression with the Newey-West adjustment to understand theinfluence of both misinformation and fact-based news on Twitter on the COVID-19 vaccine uptake in the US from April 19when US adults were vaccine eligible to June 30, 2021, after controlling state-level factors such as demographics, education,and the pandemic severity. We identified the tweets related to either misinformation or fact-based news by analyzing theURLs. Results. One percent increase in fact-related Twitter users is associated with an approximately 0.87 decrease (B = −0:87,SE = 0:25, and p < :001) in the number of daily new vaccinated people per hundred. No significant relationship was foundbetween the percentage of fake-news-related users and the vaccination rate. Conclusion. The negative association between thepercentage of fact-related users and the vaccination rate might be due to a combination of a larger user-level influence and thenegative impact of online social endorsement on vaccination intent.

关键词： vaccination discussions education

Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders

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Biomedical Engineering Frontiers 2022年第1期3卷 116-125页

作者： Wei Xiong Neil Yeung Shubo Wang Haofu Liao Liyun Wang jiebo luo Department of Computer Science University of RochesterRochesterUSA Department of Mechanical Engineering University of DelawareUSA Amazon Web Services USA

Objective and Impact *** adopt a deep learning model for bone osteolysis prediction on computed tomography(CT)images of murine breast cancer bone *** the bone CT scans at previous time steps,the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT *** ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone *** cancer often metastasizes to bone,causes osteolytic lesions,and results in skeletal-related events(SREs)including severe pain and even fatal *** current imaging techniques can detect macroscopic bone lesions,predicting the occurrence and progression of bone lesions remains a *** adopt a temporal variational autoencoder(T-VAE)model that utilizes a combination of variational autoencoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine *** the CT scans of murine tibiae at early weeks,our model can learn the distribution of their future states from *** test our model against other deep learning-based prediction models on the bone lesion progression prediction *** model produces much more accurate predictions than existing models under various evaluation *** develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone *** will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.

关键词： Breast prediction utilize

Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination

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Intelligent Medicine 2022年第1期2卷 1-12页

作者： Hanjia Lyu Junda Wang Wei Wu Viet Duong Xiyang Zhang Timothy DDye jiebo luo Goergen Institute for Data Science University of RochesterRochesterNew York 14627United States Department of Computer Science University of RochesterRochesterNew York 14627United States Department of Psychology University of AkronAkronOhio 44325United States Department of Obstetrics and Gynecology University of Rochester School of Medicine and DentistryRochesterNew York 14642United States

Background The current development of vaccines for severe acute respiratory syndrome coronavirus 2(SARSCoV-2)is *** is known,however,about the nuanced public opinions on the vaccines on social *** We adopted a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2,classifying them into three groups:pro-vaccine,vaccine-hesitant,and *** feature inference and opinion mining,10,945 unique Twitter users were included in the study *** logistic regression and counterfactual analysis were *** Socioeconomically disadvantaged groups were more likely to hold polarized opinions on coronavirus disease 2019(COVID-19)vaccines,either pro-vaccine(B=0.40,SE=0.08,P<0.001,OR=1.49;95%CI=1.26-1.75)or anti-vaccine(B=0.52,SE=0.06,P<0.001,OR=1.69;95%CI=1.49-1.91).People who have the worst personal pandemic experience were more likely to hold the anti-vaccine opinion(B=−0.18,SE=0.04,P<0.001,OR=0.84;95%CI=0.77-0.90).The United States public is most concerned about the safety,effectiveness,and political issues regarding vaccines for COVID-19,and improving personal pandemic experience increases the vaccine acceptance level.

关键词： Vaccine hesitancy COVID-19 Public opinion Social Media Twitter

Recent advances in artificial intelligence generated content

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Frontiers of Information Technology & Electronic Engineering 2024年第1期25卷 1-5页

作者： Junping ZHANG Lingyun SUN Cong JIN Junbin GAO Xiaobing LI jiebo luo Zhigeng PAN Ying TANG Jingdong WANG School of Computer Science Fudan UniversityShanghai 200433China International Design Institute Zhejiang UniversityHangzhou 310058China School of Information and Communication Engineering Communication University of ChinaBeijing 100024China The University of Sydney Business School The University of SydneyNSW 2006Australia Department of AI Music and Music Information Technology Central Conservatory of MusicBeijing 100032China Department of Computer Science University of RochesterNY 14627USA School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing 210044China Electrical and Computer Engineering Department Rowan UniversityGlassboroNJ 08028USA Baidu Beijing 100085China

Artificial intelligence generated content(AIGC)has been a research hotspot in the field of artificial intelligence in recent *** is expected to replace humans in performing some of the work of content generation at a low cost and a high volume,such as music,painting,multimodal content generation,news articles,summary reports,stock commentary summaries,and even content and digital people generated in the *** provides a new technical path for the development and implementation of AI in the future.

关键词： artificial replace content

一种由粗至精的RGB-D室内场景语义分割方法

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东南大学学报（自然科学版） 2016年第4期46卷 681-687页

作者：刘天亮冯希龙顾雁秋戴修斌罗杰波南京邮电大学江苏省图像处理与图像通信重点实验室南京210003 罗彻斯特大学计算机科学系美国罗彻斯特14627

为了标注室内场景中可见物体,提出一种基于RGB-D数据由粗至精的室内场景语义分割方法.首先,利用分层显著度导引的简单线性迭代聚类过分割和鲁棒多模态区域特征,构建面向语义类别的超像素区域池,基于随机决策森林分类器判决各个超像素区... 详细信息

为了标注室内场景中可见物体,提出一种基于RGB-D数据由粗至精的室内场景语义分割方法.首先,利用分层显著度导引的简单线性迭代聚类过分割和鲁棒多模态区域特征,构建面向语义类别的超像素区域池,基于随机决策森林分类器判决各个超像素区域的语义类别,实现粗粒度区域级语义标签推断.然后,为了改善粗粒度级的语义标签,利用几何深度导引和内部反馈机制改进像素级稠密全连接条件随机场模型,以求精细粒度像素级语义标注.最后,在粗、细粒度语义标注之间引入全局递归式反馈,渐进式迭代更新室内场景的语义类别标签.2个公开的RGBD室内场景数据集上的实验结果表明,与其他方法相比,所提出的语义分割方法无论在主观还是客观评估上,均具有较好的效果.

关键词： RGB-D室内场景语义分割 SLIC过分割稠密CRFs 递归式反馈

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一种由粗至精的室内场景的空间布局估计方法

机器人 2019年第1期41卷 58-64页

作者：刘天亮顾雁秋曹旦旦戴修斌罗杰波南京邮电大学江苏省图像处理与图像通信重点实验室江苏南京210003 罗彻斯特大学计算机科学系美国罗彻斯特14627

为有效标注室内场景的布局关系,提出一种由粗至精的空间布局估计方法.首先,采用局部不连续自适应阈值检测场景的长直线段,根据直线段的方向将其分为竖直和水平直线段;基于投票机制和正交准则估计垂直与水平消失点,由这两个消失点等角度... 详细信息

为有效标注室内场景的布局关系,提出一种由粗至精的空间布局估计方法.首先,采用局部不连续自适应阈值检测场景的长直线段,根据直线段的方向将其分为竖直和水平直线段;基于投票机制和正交准则估计垂直与水平消失点,由这两个消失点等角度间隔地引出成对射线生成场景候选布局.其次,采用VGG-16全卷积神经网络估计相应场景的几何上下文和信息化边界,采用softmax分类器决策其fc7层特征以获取布局类别,融合信息化边界和布局类别生成全局特征以粗选取场景候选布局.接着,基于VGG空间多尺度卷积神经网络估计相应场景的法向图和深度图以提取法向特征和深度特征.然后,利用消失点射线夹角参数化3D盒式布局模型,利用几何积分图聚集候选布局中的直线段成员、几何上下文、法向量和深度等区域级特征,采用割平面法学习结构化模型参数.最后,对候选布局的结构化预测得分进行排序,将得分最高者选取为最终空间布局.Hedau和LSUN数据集实验表明,该方法能获得空间布局的精准区域面划分个数和精确边界位置.

关键词：室内场景布局估计卷积神经网络场景布局类别

Exploring practical methods for detecting k-nonseparability in multipartite quantum states

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 255-256页

作者： Shunlong luo Academy of Mathematics and Systems Science Chinese Academy of Sciences

Quantum entanglement, referred to as “spooky action at a distance” by Einstein, is not only an intriguing and fascinating feature of quantum theory challenging our classical intuitions about locality and correlations, but also has practical and profound implications for quantum technologies. Initially described by Einstein, Podolsky and Rosen, as well as by Schro¨dinger, entanglement now has become a basic and crucial ingredient for both foundational and experimental investigations of the quantum world.

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基于多层次深度卷积神经网络的图像情感分类

华南理工大学学报（自然科学版） 2019年第6期47卷 39-50页

作者：王伟凝李乐敏黄杰雄罗杰波徐向民华南理工大学电子与信息学院广东广州510640 罗切斯特大学计算机科学学院纽约罗彻斯特14627

由于图像的复杂性和人类情感的主观性,图像情感分类是一项非常具有挑战性的任务.针对深度学习方法没有充分考虑图像先验信息的问题,提出一个新的多层次深度卷积神经网络框架.该框架综合考虑全局和局部视角,引入显著主体、颜色和局部等... 详细信息

由于图像的复杂性和人类情感的主观性,图像情感分类是一项非常具有挑战性的任务.针对深度学习方法没有充分考虑图像先验信息的问题,提出一个新的多层次深度卷积神经网络框架.该框架综合考虑全局和局部视角,引入显著主体、颜色和局部等先验信息,从多个层次学习图像的情感表达.实验结果表明,在公开的大数量级和小数量级情感图库上,该框架的分类准确率均高于现有的图像情感分类方法,其平均分类准确率比最优方法提高了2.8%,特别在情感类别“厌恶”上提高了15%.

关键词：图像情感分类卷积神经网络先验信息多层次