Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the l...Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.展开更多
Introduction:Between 50%and 85%of children experience at least one episode of acute otitis media(AOM)by age three.Since the coronavirus disease 2019(COVID-19)pandemic,numerous hospitals across China have integrated te...Introduction:Between 50%and 85%of children experience at least one episode of acute otitis media(AOM)by age three.Since the coronavirus disease 2019(COVID-19)pandemic,numerous hospitals across China have integrated telehealth solutions into their pediatric care services.Our research team introduced the Telehealth and Autonomous Mobile Clinic Framework,which was published in the Bulletin of the World Health Organization in September 2022.Methods:The investigation employed a retrospective cohort design.The study population comprised 200 pediatric patients diagnosed with AOM who received either telehealth follow-up consultation or traditional in-person clinic visits.Bivariate analyses examined relationships between telehealth follow-up consultation and patient outcome in ear pain,hearing loss,and patient satisfaction between telehealth and inperson AOM follow-up groups.Multivariate regression analysis evaluated associations between ear pain(a primary clinical outcome)and telehealth utilization,hearing loss,and patient satisfaction,with adjustments for age and gender.Results:Ear pain relief was reported by 84%of patients in the telehealth follow-up group compared to 81%in the in-person follow-up group,showing no statistically significant difference(P=0.57).Hearing loss was documented in 21 cases from the telehealth group and 23 cases from the in-person follow-up group(P=0.73).Patient satisfaction scores were comparable between groups,with mean scores of 4.47(range:2.47–4.92)for telehealth and 4.49(range:2.72–4.94)for in-person follow-up(P=0.87).Multivariate regression revealed no significant difference in patient satisfaction between telehealth and in-person follow-up(P=0.21).Conclusion:This retrospective study provides compelling evidence for the effectiveness and feasibility of telehealth follow-up consultations in pediatric otitis media management.Our findings demonstrate that telehealth consultations achieve comparable outcomes to in-person follow-up visits for AOM patients.展开更多
1 Introduction Unsupervised Federated Continual Learning(UFCL)is a new learning paradigm that embeds unsupervised representation techniques into the Federated Learning(FL)framework,which enables continuous training of...1 Introduction Unsupervised Federated Continual Learning(UFCL)is a new learning paradigm that embeds unsupervised representation techniques into the Federated Learning(FL)framework,which enables continuous training of a shared representation model without compromising individual participants’data privacy[1,2].However,the continuous learning process may cause catastrophic forgetting in the model,reducing generated representations’performance.展开更多
Website Fingerprinting(WF)attacks enable a local eavesdropper to use metadata of packet fow,such as size,tim-ing,and direction,to infer the websites a user is visiting.This can damage the user privacy provided by anon...Website Fingerprinting(WF)attacks enable a local eavesdropper to use metadata of packet fow,such as size,tim-ing,and direction,to infer the websites a user is visiting.This can damage the user privacy provided by anonymity systems such as Tor.Tor has implemented the WF defense called Circuit Padding Framework,which provides an inter-face for developers to implement their own defenses.However,these defenses in the framework were overcome by the Deep Fingerprinting(DF)attack.In this paper,we propose a novel defense approach called break burst padding(Break-Pad),which injects a random number of padding packets into an incoming burst once the number of consecutive incoming packets exceeds a set number.We integrated Break-Pad into the existing Circuit Padding Framework.In addition,we have implemented two padding machines named August and October in the new framework and conducted experiments to evaluate these machines.In the open-world setting,our results show that August,with 29%bandwidth overhead,reduces Tik-Tok’s TPR by 14.48%and DF’s TPR by 22%.October outper-forms the best padding machine,RBB.With 36%bandwidth overhead,it drops Tik-Tok’s TPR to 74.24%and DF’s TPR to 65.36%.In the one-page setting,October further reduces the bandwidth overhead by 11%while achieving similar performance to RBB.In the information leak analysis,for the burst sequence feature of the trafc,October leaks at 2.453 bits,while the best comparable padding machine Interspace leaks at 2.629 bits.展开更多
基金supported by the National Natural Science Foundation of China(51991351,51827803,52103122,and 22375138)the Institutional Research Fund from Sichuan University(no.2021SCUNL201)the Fundamental Research Funds for the Central Universities,and the 111 project(B20001).
文摘Organic polymer materials,as the most abundantly produced materials,possess a flammable nature,making them potential hazards to human casualties and property losses.Target polymer design is still hindered due to the lack of a scientific foundation.Herein,we present a robust,generalizable,yet intelligent polymer discovery framework,which synergizes diverse capabilities,including the in situ burning analyzer,virtual reaction generator,and material genomic model,to achieve results that surpass the sum of individual parts.Notably,the high-throughput analyzer created for the first time,grounded in multiple spectroscopic principles,enables in situ capturing of massive combustion intermediates;then,the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information;further,the proposed feature engineering tool,which embedded both polymer hierarchical structures and massive intermediate data,develops the generalizable genomic model with excellent universality(adapting over 20 kinds of polymers)and high accuracy(88.8%),succeeding discovering series of novel polymers.This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.
文摘Introduction:Between 50%and 85%of children experience at least one episode of acute otitis media(AOM)by age three.Since the coronavirus disease 2019(COVID-19)pandemic,numerous hospitals across China have integrated telehealth solutions into their pediatric care services.Our research team introduced the Telehealth and Autonomous Mobile Clinic Framework,which was published in the Bulletin of the World Health Organization in September 2022.Methods:The investigation employed a retrospective cohort design.The study population comprised 200 pediatric patients diagnosed with AOM who received either telehealth follow-up consultation or traditional in-person clinic visits.Bivariate analyses examined relationships between telehealth follow-up consultation and patient outcome in ear pain,hearing loss,and patient satisfaction between telehealth and inperson AOM follow-up groups.Multivariate regression analysis evaluated associations between ear pain(a primary clinical outcome)and telehealth utilization,hearing loss,and patient satisfaction,with adjustments for age and gender.Results:Ear pain relief was reported by 84%of patients in the telehealth follow-up group compared to 81%in the in-person follow-up group,showing no statistically significant difference(P=0.57).Hearing loss was documented in 21 cases from the telehealth group and 23 cases from the in-person follow-up group(P=0.73).Patient satisfaction scores were comparable between groups,with mean scores of 4.47(range:2.47–4.92)for telehealth and 4.49(range:2.72–4.94)for in-person follow-up(P=0.87).Multivariate regression revealed no significant difference in patient satisfaction between telehealth and in-person follow-up(P=0.21).Conclusion:This retrospective study provides compelling evidence for the effectiveness and feasibility of telehealth follow-up consultations in pediatric otitis media management.Our findings demonstrate that telehealth consultations achieve comparable outcomes to in-person follow-up visits for AOM patients.
基金supported by the National Science and Technology Major Project(2022ZD0120203).
文摘1 Introduction Unsupervised Federated Continual Learning(UFCL)is a new learning paradigm that embeds unsupervised representation techniques into the Federated Learning(FL)framework,which enables continuous training of a shared representation model without compromising individual participants’data privacy[1,2].However,the continuous learning process may cause catastrophic forgetting in the model,reducing generated representations’performance.
文摘Website Fingerprinting(WF)attacks enable a local eavesdropper to use metadata of packet fow,such as size,tim-ing,and direction,to infer the websites a user is visiting.This can damage the user privacy provided by anonymity systems such as Tor.Tor has implemented the WF defense called Circuit Padding Framework,which provides an inter-face for developers to implement their own defenses.However,these defenses in the framework were overcome by the Deep Fingerprinting(DF)attack.In this paper,we propose a novel defense approach called break burst padding(Break-Pad),which injects a random number of padding packets into an incoming burst once the number of consecutive incoming packets exceeds a set number.We integrated Break-Pad into the existing Circuit Padding Framework.In addition,we have implemented two padding machines named August and October in the new framework and conducted experiments to evaluate these machines.In the open-world setting,our results show that August,with 29%bandwidth overhead,reduces Tik-Tok’s TPR by 14.48%and DF’s TPR by 22%.October outper-forms the best padding machine,RBB.With 36%bandwidth overhead,it drops Tik-Tok’s TPR to 74.24%and DF’s TPR to 65.36%.In the one-page setting,October further reduces the bandwidth overhead by 11%while achieving similar performance to RBB.In the information leak analysis,for the burst sequence feature of the trafc,October leaks at 2.453 bits,while the best comparable padding machine Interspace leaks at 2.629 bits.
