Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for ...Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.展开更多
Epithelial–mesenchymal transition(EMT)plays a critical role in promoting cancer metastasis.In this study,cancer EMT is considered as an overall structural change in the gene regulatory network(GRN),and its essential ...Epithelial–mesenchymal transition(EMT)plays a critical role in promoting cancer metastasis.In this study,cancer EMT is considered as an overall structural change in the gene regulatory network(GRN),and its essential features are elucidated by the network biology approach.We first defined the state space of GRN as a set of all possible activation patterns of GRN,and then introduced the quasipotential field into this space to show the relative stability distribution of each state.The quasi-potential was determined empirically by collecting gene expression profiles from public databases.Changes of GRN states during the EMT process were traced in the state space,by using time-course data of gene expression profiles of a cell line inducingEMTfromthe database.It wasfound that cancerEMT occurred in three sequential stable stages,each of which formed a potential basin along the EMT trajectory.As confirmation,structural changes of GRN were estimated by applying the ARACNe algorithm to the same time-course data,and then applying master regulator analysis to extract the main regulations.Each group of master regulators was found to be alternatively active in the subsequent three stages to cause overall structural changes of GRN during cancer EMT.展开更多
基金suported by the Fundamental Research Grant Scheme(FRGS)of Universiti Sains Malaysia,Research Number:FRGS/1/2024/ICT02/USM/02/1.
文摘Classroom behavior recognition is a hot research topic,which plays a vital role in assessing and improving the quality of classroom teaching.However,existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures,and inconsistent objects.To address this challenge,we proposed an effective,lightweight object detector method called the RFNet model(YOLO-FR).The YOLO-FR is a lightweight and effective model.Specifically,for efficient multi-scale feature extraction,effective feature pyramid shared convolutional(FPSC)was designed to improve the feature extract performance by leveraging convolutional layers with varying dilation rates from the input image in the backbone.Secondly,to address the problem of multi-scale variability in the scene,we design the Rep Ghost fusion Cross Stage Partial and Efficient Layer Aggregation Network(RGCSPELAN)to improve the network performance further and reduce the amount of computation and the number of parameters.In addition,by conducting experimental valuation on the SCB dataset3 and STBD-08 dataset.Experimental results indicate that,compared to the baseline model,the RFNet model has increased mean accuracy precision(mAP@50)from 69.6%to 71.0%on the SCB dataset3 and from 91.8%to 93.1%on the STBD-08 dataset.The RFNet approach has effectiveness precision at 68.6%,surpassing the baseline method(YOLOv11)at 3.3%and archieve the minimal size(4.9 M)on the SCB dataset3.Finally,comparing it with other algorithms,it accurately detects student behavior in complex classroom environments results confirmed that RFNet is well-suited for real-time and efficiently recognizing classroom behaviors.
基金This work was supported by JSPS KAKENHI Grant-in-Aid for Scientific Research(B)Grant Number 25290070.
文摘Epithelial–mesenchymal transition(EMT)plays a critical role in promoting cancer metastasis.In this study,cancer EMT is considered as an overall structural change in the gene regulatory network(GRN),and its essential features are elucidated by the network biology approach.We first defined the state space of GRN as a set of all possible activation patterns of GRN,and then introduced the quasipotential field into this space to show the relative stability distribution of each state.The quasi-potential was determined empirically by collecting gene expression profiles from public databases.Changes of GRN states during the EMT process were traced in the state space,by using time-course data of gene expression profiles of a cell line inducingEMTfromthe database.It wasfound that cancerEMT occurred in three sequential stable stages,each of which formed a potential basin along the EMT trajectory.As confirmation,structural changes of GRN were estimated by applying the ARACNe algorithm to the same time-course data,and then applying master regulator analysis to extract the main regulations.Each group of master regulators was found to be alternatively active in the subsequent three stages to cause overall structural changes of GRN during cancer EMT.
