Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Aug...Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.展开更多
Brain oscillations are vital to cognitive functions,while disrupted oscillatory activity is linked to various brain disorders.Although high-frequency neural oscillations(>1 Hz)have been extensively studied in cogni...Brain oscillations are vital to cognitive functions,while disrupted oscillatory activity is linked to various brain disorders.Although high-frequency neural oscillations(>1 Hz)have been extensively studied in cognition,the neural mechanisms underlying low-frequency hemodynamic oscillations(LFHO)<1 Hz have not yet been fully explored.One way to examine oscillatory neural dynamics is to use a facial expression(FE)paradigm to induce steady-state visual evoked potentials(SSVEPs),which has been used in electroencephalography studies of high-frequency brain oscillation activity.In this study,LFHO during SSVEP-inducing periodic flickering stimuli presentation were inspected using functional near-infrared spectroscopy(fNIRS),in which hemodynamic responses in the prefrontal cortex were recorded while participants were passively viewing dynamic FEs flickering at 0.2 Hz.The fast Fourier analysis results demonstrated that the power exhibited monochronic peaks at 0.2 Hz across all channels,indicating that the periodic events successfully elicited LFHO in the prefrontal cortex.More importantly,measurement of LFHO can effectively distinguish the brain activation difference between different cognitive conditions,with happy FE presentation showing greater LFHO power than neutral FE presentation.These results demonstrate that stimuli flashing at a given frequency can induce LFHO in the prefrontal cortex,which provides new insights into the cognitive mechanisms involved in slow oscillation.展开更多
Objective： To describe the revolution and research status of Advances in Psychological Science. Methods： A total of 3060 articles published in Advances in Psychological Science from 1983 to 2014 were analyzed with t...Objective： To describe the revolution and research status of Advances in Psychological Science. Methods： A total of 3060 articles published in Advances in Psychological Science from 1983 to 2014 were analyzed with the information visualization method using Citespace software from the aspects of pub- lications, cited frequency and downloads, funding, organizations, authors and keywords. Results： The results showed that the amount of literature published annually had an upward tendency, and 49.4% of the papers were supported by national or provincial projects. Institutions such as the Chinese Academy of Sciences （CAS） and the normal universities were rated in the forefront of the sci- entific research output. Xiting Huang, Hong Li and Yuejia Luo were at the top of the list of prolific authors. Conclusions： A new pattern of cooperative development of the theory and application in the field of psychological research is forming.展开更多
Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the ...Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.展开更多
Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-o...Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the DAG.However,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban environments.To overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment.HoLens mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral variability.Second,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions.Two real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using HoLens.We also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.展开更多
With the increasing complexity of power systems and the widespread penetration of renewable energy sources(RES),real-time situational awareness for power systems is of great significance for operational scheduling.Con...With the increasing complexity of power systems and the widespread penetration of renewable energy sources(RES),real-time situational awareness for power systems is of great significance for operational scheduling.Considering the impact of RES on power system operations,a situational awareness key performance index(KPI)system for power systems with a high proportion of RES is proposed in this paper,which consists of reserve capacity abundance,ramp resource abundance,center of inertia(COI)frequency deviation,interface power flow margin,synthesized voltage stability,and angle stability margin.Then,the KPIs are synthesized and visualized by the decision tree method and radar chart method,respectively,for monitoring the operation states(i.e,normal,alert,and emergency states)of power systems with a high proportion of RES.Numerical simulations are conducted in a revised New England 16-machine 68-bus power system and an actual CEPRI-RE power system in the northwest region of China with a high proportion of RES.The results show that the proposed KPI-based situational awareness method is able to accurately monitor the real-time state of power systems with a high proportion of RES,and can assist power dispatchers to make effective decisions.展开更多
文摘Medical image classification is crucial in disease diagnosis,treatment planning,and clinical decisionmaking.We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation(BSDA)with a Vision Mamba-based model for medical image classification(MedMamba),enhanced by residual connection blocks,we named the model BSDA-Mamba.BSDA augments medical image data semantically,enhancing the model’s generalization ability and classification performance.MedMamba,a deep learning-based state space model,excels in capturing long-range dependencies in medical images.By incorporating residual connections,BSDA-Mamba further improves feature extraction capabilities.Through comprehensive experiments on eight medical image datasets,we demonstrate that BSDA-Mamba outperforms existing models in accuracy,area under the curve,and F1-score.Our results highlight BSDA-Mamba’s potential as a reliable tool for medical image analysis,particularly in handling diverse imaging modalities from X-rays to MRI.The open-sourcing of our model’s code and datasets,will facilitate the reproduction and extension of our work.
基金University of Macao,Nos.MYRG2019-00082-FHS and MYRG2018-00081-FHSMacao Science and Technology Development Fund,No.FDCT 025/2015/A1 and FDCT 0011/2018/A1.
文摘Brain oscillations are vital to cognitive functions,while disrupted oscillatory activity is linked to various brain disorders.Although high-frequency neural oscillations(>1 Hz)have been extensively studied in cognition,the neural mechanisms underlying low-frequency hemodynamic oscillations(LFHO)<1 Hz have not yet been fully explored.One way to examine oscillatory neural dynamics is to use a facial expression(FE)paradigm to induce steady-state visual evoked potentials(SSVEPs),which has been used in electroencephalography studies of high-frequency brain oscillation activity.In this study,LFHO during SSVEP-inducing periodic flickering stimuli presentation were inspected using functional near-infrared spectroscopy(fNIRS),in which hemodynamic responses in the prefrontal cortex were recorded while participants were passively viewing dynamic FEs flickering at 0.2 Hz.The fast Fourier analysis results demonstrated that the power exhibited monochronic peaks at 0.2 Hz across all channels,indicating that the periodic events successfully elicited LFHO in the prefrontal cortex.More importantly,measurement of LFHO can effectively distinguish the brain activation difference between different cognitive conditions,with happy FE presentation showing greater LFHO power than neutral FE presentation.These results demonstrate that stimuli flashing at a given frequency can induce LFHO in the prefrontal cortex,which provides new insights into the cognitive mechanisms involved in slow oscillation.
基金supported by MOE(Ministry of Education of China)the research projects of Humanities and Social Sciences(No.13YJCZH239)Project of innovation and entrepreneurship for undergraduates in Shanxi Medical University(No.20160311)
文摘Objective： To describe the revolution and research status of Advances in Psychological Science. Methods： A total of 3060 articles published in Advances in Psychological Science from 1983 to 2014 were analyzed with the information visualization method using Citespace software from the aspects of pub- lications, cited frequency and downloads, funding, organizations, authors and keywords. Results： The results showed that the amount of literature published annually had an upward tendency, and 49.4% of the papers were supported by national or provincial projects. Institutions such as the Chinese Academy of Sciences （CAS） and the normal universities were rated in the forefront of the sci- entific research output. Xiting Huang, Hong Li and Yuejia Luo were at the top of the list of prolific authors. Conclusions： A new pattern of cooperative development of the theory and application in the field of psychological research is forming.
文摘Brain-computer interfaces(BCI)based on steady-state visual evoked potentials(SSVEP)have attracted great interest because of their higher signal-to-noise ratio,less training,and faster information transfer.However,the existing signal recognition methods for SSVEP do not fully pay attention to the important role of signal phase characteristics in the recognition process.Therefore,an improved method based on extended Canonical Correlation Analysis(eCCA)is proposed.The phase parameters are added from the stimulus paradigm encoded by joint frequency phase modulation to the reference signal constructed from the training data of the subjects to achieve phase constraints on eCCA,thereby improving the recognition performance of the eCCA method for SSVEP signals,and transmit the collected signals to the robotic arm system to achieve control of the robotic arm.In order to verify the effectiveness and advantages of the proposed method,this paper evaluated the method using SSVEP signals from 35 subjects.The research shows that the proposed algorithm improves the average recognition rate of SSVEP signals to 82.76%,and the information transmission rate to 116.18 bits/min,which is superior to TRCA and traditional eCAA-based methods in terms of information transmission speed and accuracy,and has better stability.
基金supported in part by the Shenzhen Science and Technology Program(No.ZDSYS20210623092007023)in part by the National Natural Science Foundation of China(No.62172398)the Guangdong Basic and Applied Basic Research Foundation(No.2021A1515011700).
文摘Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement patterns.Conventional methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the DAG.However,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban environments.To overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment.HoLens mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral variability.Second,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions.Two real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using HoLens.We also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.
基金supported in part by the National Key R&D Program of China(2016YFB0900100)the National Natural Science Foundation of China(52077195).
文摘With the increasing complexity of power systems and the widespread penetration of renewable energy sources(RES),real-time situational awareness for power systems is of great significance for operational scheduling.Considering the impact of RES on power system operations,a situational awareness key performance index(KPI)system for power systems with a high proportion of RES is proposed in this paper,which consists of reserve capacity abundance,ramp resource abundance,center of inertia(COI)frequency deviation,interface power flow margin,synthesized voltage stability,and angle stability margin.Then,the KPIs are synthesized and visualized by the decision tree method and radar chart method,respectively,for monitoring the operation states(i.e,normal,alert,and emergency states)of power systems with a high proportion of RES.Numerical simulations are conducted in a revised New England 16-machine 68-bus power system and an actual CEPRI-RE power system in the northwest region of China with a high proportion of RES.The results show that the proposed KPI-based situational awareness method is able to accurately monitor the real-time state of power systems with a high proportion of RES,and can assist power dispatchers to make effective decisions.
