We proposed a method using latent regression Bayesian network (LRBN) toextract the shared speech feature for the input of end-to-end speech recognition model.The structure of LRBN is compact and its parameter learning...We proposed a method using latent regression Bayesian network (LRBN) toextract the shared speech feature for the input of end-to-end speech recognition model.The structure of LRBN is compact and its parameter learning is fast. Compared withConvolutional Neural Network, it has a simpler and understood structure and lessparameters to learn. Experimental results show that the advantage of hybridLRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classificationarchitecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN ishelpful to differentiate among multiple language speech sets.展开更多
目的·探究大学生抑郁症状与焦虑症状的共病情况及其与社交网络成瘾之间的关联。方法·通过在上海市5所高等院校进行的针对本科生的横断面问卷调查,收集调查对象的人口学信息,采用患者健康问卷9条目(Patient Health Questionnai...目的·探究大学生抑郁症状与焦虑症状的共病情况及其与社交网络成瘾之间的关联。方法·通过在上海市5所高等院校进行的针对本科生的横断面问卷调查,收集调查对象的人口学信息,采用患者健康问卷9条目(Patient Health Questionnaire-9,PHQ-9)、广泛性焦虑量表(Generalized Anxiety Disorder,GAD-7)和青少年社交网络成瘾评估量表评估抑郁、焦虑及社交网络成瘾情况。采用Logistic回归对大学生社交网络成瘾进行多因素分析。采用潜在剖面分析对大学生的抑郁症状和焦虑症状进行分类,识别大学生抑郁和焦虑情况的异质性,并通过稳健三步法校正分类误差。运用χ^(2)检验和Logistic回归分析明确各剖面的社交网络成瘾率差异是否存在统计学意义。结果·共纳入调查对象1768名,23.53%的调查对象存在抑郁和焦虑共病的情况,66.29%的调查对象有社交网络成瘾。调整人口学因素后,大学生抑郁和焦虑共病组(n=416)发生社交网络成瘾的概率是无抑郁或焦虑组(n=618)的6.093倍(OR=6.093,95%CI 4.426~8.389),仅有抑郁或焦虑组(n=734)发生社交网络成瘾的概率是无抑郁或焦虑组的2.442倍(OR=2.442,95%CI 1.947~3.064)。大学生抑郁和焦虑症状的潜在剖面可划分为中水平组(45.4%)、低水平组(46.3%)和高水平组(8.3%)。3个剖面的社交网络成瘾率差异存在统计学意义,中水平组和高水平组较低水平组更易发生社交网络成瘾(均P<0.001)。结论·大学生抑郁和焦虑情况存在异质性,且易出现抑郁合并焦虑的情况。对于高抑郁、高焦虑情况的大学生,其社交网络成瘾情况也较严重。因此,应高度关注大学生的心理健康状况,针对抑郁合并焦虑症状严重的大学生,就其社交网络成瘾问题开展精准化干预工作。展开更多
The abundance of social networking platforms has increased the frequency and the availability for which individuals communicate with one another. The feasibility and accessibility to go online to find sexual partners ...The abundance of social networking platforms has increased the frequency and the availability for which individuals communicate with one another. The feasibility and accessibility to go online to find sexual partners pose opportunity for contracting sexually transmitted infections (STI) in the absence of safe sexual practices. Low condom use has been reported among young adults who seek sexual partners online. African American young adults have some of the highest rates of infection for certain STIs. In order to mitigate the incidence and prevalence of STIs in at-risk populations, sexually active young adults must use condoms consistently and correctly during sexual activities. The present study sought to uncover the heterogeneity within African American young adults regarding their online networking utilization, STI knowledge, and sexual risk behavior. African American young adults (N = 236), ages 18 - 23, completed private online survey administration. Using latent class analysis, three classes were identified: Social Network Communicators (43%;N = 101), Social Networking Daters (36%;N = 83), and Media Sharers (21%;N = 52). Social Networking Daters exhibited the highest probability of using online dating sites daily, low STI knowledge, and a zero probability of consistent condom use. All three groups exhibited relatively low STI knowledge. Furthermore, having a history of STI increased the likelihood of being classified into the Social Networking Daters class relative to the other classes. Findings highlight the need to capitalize upon online platforms for African American young adults who utilize online dating sites and other online environments.展开更多
文摘We proposed a method using latent regression Bayesian network (LRBN) toextract the shared speech feature for the input of end-to-end speech recognition model.The structure of LRBN is compact and its parameter learning is fast. Compared withConvolutional Neural Network, it has a simpler and understood structure and lessparameters to learn. Experimental results show that the advantage of hybridLRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classificationarchitecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN ishelpful to differentiate among multiple language speech sets.
文摘The abundance of social networking platforms has increased the frequency and the availability for which individuals communicate with one another. The feasibility and accessibility to go online to find sexual partners pose opportunity for contracting sexually transmitted infections (STI) in the absence of safe sexual practices. Low condom use has been reported among young adults who seek sexual partners online. African American young adults have some of the highest rates of infection for certain STIs. In order to mitigate the incidence and prevalence of STIs in at-risk populations, sexually active young adults must use condoms consistently and correctly during sexual activities. The present study sought to uncover the heterogeneity within African American young adults regarding their online networking utilization, STI knowledge, and sexual risk behavior. African American young adults (N = 236), ages 18 - 23, completed private online survey administration. Using latent class analysis, three classes were identified: Social Network Communicators (43%;N = 101), Social Networking Daters (36%;N = 83), and Media Sharers (21%;N = 52). Social Networking Daters exhibited the highest probability of using online dating sites daily, low STI knowledge, and a zero probability of consistent condom use. All three groups exhibited relatively low STI knowledge. Furthermore, having a history of STI increased the likelihood of being classified into the Social Networking Daters class relative to the other classes. Findings highlight the need to capitalize upon online platforms for African American young adults who utilize online dating sites and other online environments.
