A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore...A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore,in this paper,a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words.Furthermore,it considers the importance of entity in complaint reports to ensure factual consistency of summary.Experimental results on the customer review datasets(Yelp and Amazon)and complaint report dataset(complaint reports of Shenyang in China)show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation.It unveils the effectiveness of our approach to helping in dealing with complaint reports.展开更多
With the rapid development of computer technology,millions of images are produced everyday by different sources.How to efficiently process these images and accurately discern the scene in them becomes an important but...With the rapid development of computer technology,millions of images are produced everyday by different sources.How to efficiently process these images and accurately discern the scene in them becomes an important but tough task.In this paper,we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification.Specifically,we first extract some high-level features of images under consideration based on available models trained on public datasets.Then,we further design a binary encoding method called one-hot encoding to make the feature representation more efficient.Benefiting from the proposed adaptive binary coding,our method is free of time to train or fine-tune the deep network and can effectively handle different applications.Experimental results on three public datasets,i.e.,UIUC sports event dataset,MIT Indoor dataset,and UC Merced dataset in terms of three different classifiers,demonstrate that our method is superior to the state-of-the-art methods with large margins.展开更多
Purpose This longitudinal study investigated the potential enhancement of one physical and nine performance variables of New Zealand Rugby-Otago Rugby Football Union Academy college-age women student-athletes(NZORFUSA...Purpose This longitudinal study investigated the potential enhancement of one physical and nine performance variables of New Zealand Rugby-Otago Rugby Football Union Academy college-age women student-athletes(NZORFUSA)using in-person-and non-video online-training(online)supervision.Methods Recruited NZORFUSA followed a periodised training protocols over 70-weeks.During weeks 1-35 and weeks 53-70(in-season),the NZORFUSA received 25-h of in-person supervision,participated in 15-h of team practice and game play each week.Over weeks 36-52(off-season),due to the NZORFUSA returning home over the college summer break,NZORFUSA received online supervision.Performance assessments occurred on weeks 1,31,53 and 70.During each testing session,body weight,acceleration,anaerobic endurance,lower-body power,speed,and upper-body strength performance data were collected.Data from weeks 1-70 are presented in this paper;weeks 1-31 and 1-53 data were previously published.Results Over 70-weeks of in-person-,online-,and again in-person supervision,mean data showed a decrease in body weight[effect size,Cohen's d=0.12,trivial;95%confidence interval(CI):29.4-127.7]and showed improvements in per-formance variables(large effect size,d=1.49-4.33),including lower-body power(CI:39.9-47.5),upper-body bench press strength(CI:29.7-132.3)and anaerobic endurance;for the latter performance variable,to complete the 40 m repeat sprints needs less effort(CI:81.3-95.5)with concurrent lower fatigue level being achieved(CI:8.08-9.77).Conclusion Physical and performance enhancement for Academy women student-athletes with in-person,online,and again in-person supervision over 70-weeks is attainable.Future longitudinal research is needed to assist performance enhance-ment for this cohort.展开更多
To the Editor:Urinary incontinence(UI)is a major global health issue for women,affecting nearly 50%of the population and incurring significant costs.[1]Key risk factors,including childbirth and aging,contribute to UI ...To the Editor:Urinary incontinence(UI)is a major global health issue for women,affecting nearly 50%of the population and incurring significant costs.[1]Key risk factors,including childbirth and aging,contribute to UI morbidity rates between 15.9%and 34%.The 2021 National Institute for Health and Care Excellence(NICE)guidelines recommend supervised pelvic floor muscle(PFM)training as the primary conservative treatment for stress and mixed UI,aiming to enhance PFM strength.However,over 30%of patients struggle to voluntarily contract their PFMs during initial consultations,limiting the effectiveness of exercise routines,even when guided by digital therapeutic devices.The Modified Oxford Scale(MOS),[2]the gold standard for assessing PFM strength through palpation,faces challenges in promoting adherence due to a lack of standardized diagnostic tools and limited education for healthcare providers and the public.An ongoing issue is the scarcity of digital devices that match the assessment precision of trained professionals,especially for PFM strength evaluation.In telehealth,an important question is how to accurately diagnose PFM function and develop personalized rehabilitation programs remotely.Electromyography(EMG)has shown potential for home monitoring across rehabilitation settings by measuring electrical signals from muscle fibers,which indicate muscle recruitment and strength;higher EMG signals suggest greater muscle force.展开更多
基金supported by National Natural Science Foundation of China(62276058,61902057,41774063)Fundamental Research Funds for the Central Universities(N2217003)Joint Fund of Science&Technology Department of Liaoning Province and State Key Laboratory of Robotics,China(2020-KF-12-11).
文摘A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary.Therefore,in this paper,a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words.Furthermore,it considers the importance of entity in complaint reports to ensure factual consistency of summary.Experimental results on the customer review datasets(Yelp and Amazon)and complaint report dataset(complaint reports of Shenyang in China)show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation.It unveils the effectiveness of our approach to helping in dealing with complaint reports.
基金supported by the National Key R&D Program of China 2018YFB1003205by the National Natural Science Foundation of China U1836208,U1536206,U1836110,61972207+2 种基金by the Engineering Research Center of Digital Forensics,Ministry of Educationby the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China。
文摘With the rapid development of computer technology,millions of images are produced everyday by different sources.How to efficiently process these images and accurately discern the scene in them becomes an important but tough task.In this paper,we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification.Specifically,we first extract some high-level features of images under consideration based on available models trained on public datasets.Then,we further design a binary encoding method called one-hot encoding to make the feature representation more efficient.Benefiting from the proposed adaptive binary coding,our method is free of time to train or fine-tune the deep network and can effectively handle different applications.Experimental results on three public datasets,i.e.,UIUC sports event dataset,MIT Indoor dataset,and UC Merced dataset in terms of three different classifiers,demonstrate that our method is superior to the state-of-the-art methods with large margins.
文摘Purpose This longitudinal study investigated the potential enhancement of one physical and nine performance variables of New Zealand Rugby-Otago Rugby Football Union Academy college-age women student-athletes(NZORFUSA)using in-person-and non-video online-training(online)supervision.Methods Recruited NZORFUSA followed a periodised training protocols over 70-weeks.During weeks 1-35 and weeks 53-70(in-season),the NZORFUSA received 25-h of in-person supervision,participated in 15-h of team practice and game play each week.Over weeks 36-52(off-season),due to the NZORFUSA returning home over the college summer break,NZORFUSA received online supervision.Performance assessments occurred on weeks 1,31,53 and 70.During each testing session,body weight,acceleration,anaerobic endurance,lower-body power,speed,and upper-body strength performance data were collected.Data from weeks 1-70 are presented in this paper;weeks 1-31 and 1-53 data were previously published.Results Over 70-weeks of in-person-,online-,and again in-person supervision,mean data showed a decrease in body weight[effect size,Cohen's d=0.12,trivial;95%confidence interval(CI):29.4-127.7]and showed improvements in per-formance variables(large effect size,d=1.49-4.33),including lower-body power(CI:39.9-47.5),upper-body bench press strength(CI:29.7-132.3)and anaerobic endurance;for the latter performance variable,to complete the 40 m repeat sprints needs less effort(CI:81.3-95.5)with concurrent lower fatigue level being achieved(CI:8.08-9.77).Conclusion Physical and performance enhancement for Academy women student-athletes with in-person,online,and again in-person supervision over 70-weeks is attainable.Future longitudinal research is needed to assist performance enhance-ment for this cohort.
基金supported by National Key R&D Program of China(Nos.2021YFC2701300,2021YFC2701303)National High Level Hospital Clinical Research Funding(Nos.BJ-2018-204 and BJ-2023-112).
文摘To the Editor:Urinary incontinence(UI)is a major global health issue for women,affecting nearly 50%of the population and incurring significant costs.[1]Key risk factors,including childbirth and aging,contribute to UI morbidity rates between 15.9%and 34%.The 2021 National Institute for Health and Care Excellence(NICE)guidelines recommend supervised pelvic floor muscle(PFM)training as the primary conservative treatment for stress and mixed UI,aiming to enhance PFM strength.However,over 30%of patients struggle to voluntarily contract their PFMs during initial consultations,limiting the effectiveness of exercise routines,even when guided by digital therapeutic devices.The Modified Oxford Scale(MOS),[2]the gold standard for assessing PFM strength through palpation,faces challenges in promoting adherence due to a lack of standardized diagnostic tools and limited education for healthcare providers and the public.An ongoing issue is the scarcity of digital devices that match the assessment precision of trained professionals,especially for PFM strength evaluation.In telehealth,an important question is how to accurately diagnose PFM function and develop personalized rehabilitation programs remotely.Electromyography(EMG)has shown potential for home monitoring across rehabilitation settings by measuring electrical signals from muscle fibers,which indicate muscle recruitment and strength;higher EMG signals suggest greater muscle force.
