This study investigated the effects of xu-argument-based continuation writing on learners’processing of source texts.Seventy-five participants were randomly assigned to three conditions:(1)continuation writing,(2)sum...This study investigated the effects of xu-argument-based continuation writing on learners’processing of source texts.Seventy-five participants were randomly assigned to three conditions:(1)continuation writing,(2)summary writing,or(3)reading comprehension.Eye-tracking data were collected during reading,measuring early(first fixation duration,first pass duration)and late(go-past time,total fixation duration)eye movements.During writing,source-text rereading was tracked via fixation counts and durations.Results showed that task type did not affect initial lexical access,as first fixation duration showed no group differences.However,both production groups exhibited significantly longer first pass durations than the reading comprehension group.Late measures revealed a gradient pattern:the continuation writing group spent significantly longer gopast time and total fixation duration than the summary writing group,which exceeded the reading comprehension group.This indicates that continuation tasks promoted deeper cognitive engagement during reading.During writing,the continuation writing group spent more time rereading the source text with higher fixation counts than the summary writing group.These findings suggest that continuation writing triggers more intensive reader-text interaction during pre-writing and enhances comprehension-production coupling through sustained attention to input during writing.This study sheds light on the cognitive mechanisms underlying the theoretical and pedagogical value of xu-argument.展开更多
This study investigated the impact of the xu-argument-based continuation on Chinese high school students’English syntactic complexity captured using verb-argument constructions(VACs)over an 8-week period.Participants...This study investigated the impact of the xu-argument-based continuation on Chinese high school students’English syntactic complexity captured using verb-argument constructions(VACs)over an 8-week period.Participants were two comparable groups of students:one group worked with English input texts(i.e.,E-E),while the other worked with Chinese input texts with the same content(i.e.,C-E).The results showed that over time,the E-E group exhibited a greater tendency to use a wider range of VACs,such as caused-motion constructions,attributives,passives,and phrasal verbs.At the same time,they reduced their use of simpler VACs like intransitive-motion and simple transitive constructions,especially when compared to the C-E group.This pattern was also evident in the topic-based writing during the posttest.These findings strongly support the effectiveness of xu-argument-based continuation tasks in promoting the development of L2 VAC knowledge.They suggest that tasks combining language input with output can significantly enhance learners’ability to use more sophisticated VACs.展开更多
The analytic continuation serves as a crucial bridge between quantum Monte Carlo calculations in imaginary-time formalism,specifically the Green's functions,and physical measurements(the spectral functions)in real...The analytic continuation serves as a crucial bridge between quantum Monte Carlo calculations in imaginary-time formalism,specifically the Green's functions,and physical measurements(the spectral functions)in real time.Various approaches have been developed to enhance the accuracy of analytic continuation,including the Padéapproximation,the maximum entropy method,and stochastic analytic continuation.In this study,we employ different deep learning techniques to investigate the analytic continuation for the quantum impurity model.A significant challenge in this context is that the sharp Abrikosov-Suhl resonance peak may be either underestimated or overestimated.We fit both the imaginary-time Green's function and the spectral function using Chebyshev polynomials in logarithmic coordinates.We utilize Full-Connected Networks(FCN),Convolutional Neural Networks(CNNs),and Residual Networks(ResNet)to address this issue.Our findings indicate that introducing noise during the training phase significantly improves the accuracy of the learning process.The typical absolute error achieved is less than 10-4.These investigations pave the way for machine learning to optimize the analytic continuation problem in many-body systems,thereby reducing the need for prior expertise in physics.展开更多
文摘This study investigated the effects of xu-argument-based continuation writing on learners’processing of source texts.Seventy-five participants were randomly assigned to three conditions:(1)continuation writing,(2)summary writing,or(3)reading comprehension.Eye-tracking data were collected during reading,measuring early(first fixation duration,first pass duration)and late(go-past time,total fixation duration)eye movements.During writing,source-text rereading was tracked via fixation counts and durations.Results showed that task type did not affect initial lexical access,as first fixation duration showed no group differences.However,both production groups exhibited significantly longer first pass durations than the reading comprehension group.Late measures revealed a gradient pattern:the continuation writing group spent significantly longer gopast time and total fixation duration than the summary writing group,which exceeded the reading comprehension group.This indicates that continuation tasks promoted deeper cognitive engagement during reading.During writing,the continuation writing group spent more time rereading the source text with higher fixation counts than the summary writing group.These findings suggest that continuation writing triggers more intensive reader-text interaction during pre-writing and enhances comprehension-production coupling through sustained attention to input during writing.This study sheds light on the cognitive mechanisms underlying the theoretical and pedagogical value of xu-argument.
文摘This study investigated the impact of the xu-argument-based continuation on Chinese high school students’English syntactic complexity captured using verb-argument constructions(VACs)over an 8-week period.Participants were two comparable groups of students:one group worked with English input texts(i.e.,E-E),while the other worked with Chinese input texts with the same content(i.e.,C-E).The results showed that over time,the E-E group exhibited a greater tendency to use a wider range of VACs,such as caused-motion constructions,attributives,passives,and phrasal verbs.At the same time,they reduced their use of simpler VACs like intransitive-motion and simple transitive constructions,especially when compared to the C-E group.This pattern was also evident in the topic-based writing during the posttest.These findings strongly support the effectiveness of xu-argument-based continuation tasks in promoting the development of L2 VAC knowledge.They suggest that tasks combining language input with output can significantly enhance learners’ability to use more sophisticated VACs.
基金Sponsored by National Natural Science Foundation of China(Grant No.12174101)Fundamental Research Funds for the Central Universities(Grant No.2022MS051).
文摘The analytic continuation serves as a crucial bridge between quantum Monte Carlo calculations in imaginary-time formalism,specifically the Green's functions,and physical measurements(the spectral functions)in real time.Various approaches have been developed to enhance the accuracy of analytic continuation,including the Padéapproximation,the maximum entropy method,and stochastic analytic continuation.In this study,we employ different deep learning techniques to investigate the analytic continuation for the quantum impurity model.A significant challenge in this context is that the sharp Abrikosov-Suhl resonance peak may be either underestimated or overestimated.We fit both the imaginary-time Green's function and the spectral function using Chebyshev polynomials in logarithmic coordinates.We utilize Full-Connected Networks(FCN),Convolutional Neural Networks(CNNs),and Residual Networks(ResNet)to address this issue.Our findings indicate that introducing noise during the training phase significantly improves the accuracy of the learning process.The typical absolute error achieved is less than 10-4.These investigations pave the way for machine learning to optimize the analytic continuation problem in many-body systems,thereby reducing the need for prior expertise in physics.
