To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satel...To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is proposed.The process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting factors.The adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more adaptability.The intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency issues.Considering that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed recursively.Stability analysis of the proposed method is also conducted.The effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison experiments.The estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.展开更多
The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting me...The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.展开更多
The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms wit...The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms with a sparse regularization term.In this paper,we propose a variable forgetting factor(VFF)PRLS algorithm with a sparse penalty,e.g.,l_(1)-norm,for sparse identification.To reduce the computation complexity of the proposed algorithm,a fast implementation method based on dichotomous coordinate descent(DCD)algorithm is also derived.Simulation results indicate superior performance of the proposed algorithm.展开更多
In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under n...In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under non-persistent excitation.The proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is received.Theoretical proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite bound.Moreover,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed algorithm.Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.展开更多
Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predict...Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predictive control algorithm(SPC). The new method uses model matching error to calculate the variable forgetting factor, and applies it to constructing Hankel data matrix.This makes the data represent the changes of system information better. For eliminating the steady state error, the derivation of the incremental control is made. Simulation results on a rotary kiln show that this control strategy has achieved a good control effect.展开更多
It is known that the commonly used NaSch cellular automaton （CA） model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of veh...It is known that the commonly used NaSch cellular automaton （CA） model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of vehicle velocity used in these models is assumed to be an exogenous constant or a conditional constant, which cannot reflect the learning and forgetting behaviour of drivers with historical experiences. This paper further modifies the NaSch model by enabling the randomization probability to be adjusted on the bases of drivers＇ memory. The Markov properties of this modified model are discussed. Analytical and simulation results show that the traffic fundamental diagrams can be indeed improved when considering drivers＇ intelligent behaviour. Some new features of traffic are revealed by differently combining the model parameters representing learning and forgetting behaviour.展开更多
Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time ...Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.展开更多
User interest is not static and changes dynamically. In the scenario of a search engine, this paper presents a personalized adaptive user interest prediction framework. It represents user interest as a topic distribut...User interest is not static and changes dynamically. In the scenario of a search engine, this paper presents a personalized adaptive user interest prediction framework. It represents user interest as a topic distribution, captures every change of user interest in the history, and uses the changes to predict future individual user interest dynamically. More specifically, it first uses a personalized user interest representation model to infer user interest from queries in the user＇s history data using a topic model; then it presents a personalized user interest prediction model to capture the dynamic changes of user interest and to predict future user interest by leveraging the query submission time in the history data. Compared with the Interest Degree Multi-Stage Quantization Model, experiment results on an AOL Search Query Log query log show that our framework is more stable and effective in user interest prediction.展开更多
The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they need.At this stage,there are ...The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they need.At this stage,there are a lot of intelligent tutoring systems.In these systems,studentslearning actions are tracked in real⁃time,and there are a lot of available data.From these data,personalized education that suits each student can be mined.To improve the quality of education,some models for predicting studentsnext practice have been produced,such as Bayesian Knowledge Tracing(BKT),Performance Factor Analysis(PFA),and Deep Knowledge Tracing(DKT)with the development of deep learning.However,the model only considers the knowledge component and correctness of the problem,ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system,the lag time of the previous interaction,the number of past attempts to a problem,and situations that students have forgotten the knowledge.Although some studies consider forgetting and rich information when modeling student knowledge,they often ignore student learning sequences.The main contribution of this paper is in two aspects.One is to transform the input into a position feature vector by introducing an auto⁃encoding network layer and to carry out multiple sets of bad political combinations.The other is to consider repeated time intervals,sequence time intervals,and the number of attempts to simulate forgetting behavior.This paper proposes an adaptive algorithm for the original DKT model.By using the stacked auto⁃encoder network,the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of studentslearning.The model proposed in this paper has been experimented on two public data sets to improve the original accuracy.展开更多
As more and more benefits of forgetting have been found in recent studies,whether forgetting could promote individuals ability of creative problem solving remains a controversial debate.This article discusses the eff...As more and more benefits of forgetting have been found in recent studies,whether forgetting could promote individuals ability of creative problem solving remains a controversial debate.This article discusses the effect of two types of forgetting,the retrieval-induced forgetting(RIF)and the forgetting during incubation,in benefiting creative problem solving by introducing and analysing the relevant experiments.The results reveal that retrieval-induced forgetting only works when previous mental fixations occurred and the promotion varies when solving different types of problems.The level of RIF is irrelevant to the performance in solving closed-ended creative problems and high level of RIF even impairs the creativity when solving open-ended problems.And forgetting during incubation cannot explain the incubation effect.The spreading activation of relevant information or the unconscious work is more likely to be the possible reasons.In conclusion,the current article brings about the discussions about the work conditions and effects of forgetting in creative problem solving.展开更多
This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabu...This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.展开更多
This article examines the problem of individual and collective attempts at forgetting the traumatic past in Toni Morrison’s sixth novel Jazz(1992).More specifically,it emphasizes by selected examples psychological an...This article examines the problem of individual and collective attempts at forgetting the traumatic past in Toni Morrison’s sixth novel Jazz(1992).More specifically,it emphasizes by selected examples psychological and social aspects of willful amnesia which can lend itself useful in helping traumatized(country)individuals to repress painful remembrances,heal mental wounds and build a new identity in a memory-free modern city.Analyzing Jazz’s narrative featuring Joe and Violet Trace,with a particular focus put on the expectations and experiences connected with their migration to and life in the City,the article explores via Paul Connerton’s ruminations on cultural forgetting in modern times-delineated in his book How Modernity Forgets(2009)-the mechanisms of intentional amnesia used in the process of recovering from personal and social traumas resulting from more recent(migration and urban life)and more time-distant(slavery and racism)ordeals.展开更多
The Buried Giant by Kazuo Ishiguro begins with an elderly couple who start a quest for the past memory which disap pears under the spell of the she-dragon Querig.During,individual confrontations and collective revenge...The Buried Giant by Kazuo Ishiguro begins with an elderly couple who start a quest for the past memory which disap pears under the spell of the she-dragon Querig.During,individual confrontations and collective revenges work together to disclose the dark secrets that have been withheld.At the same time,it probes into the problem of forgiveness:can forced forgetting enable individuals or collectives forget their dark history for either love or peace?Based on the analysis of the individual and collective memories embodied in the novel,the present paper by virtue of Paul Ricoeur’s theory of abuses of memory,especially forced forget ting exhumes Ishiguro’s critical attitude towards forced forgetting,which ignores the threatening elements of love and peace like vi olent revenge and betrayal.展开更多
This paper reviews the theory of language attrition,which refers to the loss or degradation of second language skills due to lack of using the second language for a certain period of time.Although our country attaches...This paper reviews the theory of language attrition,which refers to the loss or degradation of second language skills due to lack of using the second language for a certain period of time.Although our country attaches great importance to English language teaching,most of college English majors use English far less frequently than that of Chinese in real life,which makes them easily influenced by language attrition.Therefore,it is of great significance for college English majors to improve the efficiency of English vocabulary memory from the perspective of language attrition combined with Forgetting.This thesis consists of three parts.Chapter one is an analysis the concept of language attrition and Forgetting.Chapter two describes and analyzes the existing problems in current vocabulary memory among the college English majors via a questionnaire survey.The final chapter puts forward some corresponding countermeasures to help college English majors get rid of the influence of language attrition on vocabulary learning.展开更多
1 Introduction Unsupervised Federated Continual Learning(UFCL)is a new learning paradigm that embeds unsupervised representation techniques into the Federated Learning(FL)framework,which enables continuous training of...1 Introduction Unsupervised Federated Continual Learning(UFCL)is a new learning paradigm that embeds unsupervised representation techniques into the Federated Learning(FL)framework,which enables continuous training of a shared representation model without compromising individual participants’data privacy[1,2].However,the continuous learning process may cause catastrophic forgetting in the model,reducing generated representations’performance.展开更多
Vaccination is critical for controlling infectious diseases,but negative vaccination information can lead to vaccine hesitancy.To study how the interplay between information diffusion and disease transmission impacts ...Vaccination is critical for controlling infectious diseases,but negative vaccination information can lead to vaccine hesitancy.To study how the interplay between information diffusion and disease transmission impacts vaccination and epidemic spread,we propose a novel two-layer multiplex network model that integrates an unaware-acceptant-negative-unaware(UANU)information diffusion model with a susceptible-vaccinated-exposed-infected-susceptible(SVEIS)epidemiological framework.This model includes individual exposure and vaccination statuses,time-varying forgetting probabilities,and information conversion thresholds.Through the microscopic Markov chain approach(MMCA),we derive dynamic transition equations and the epidemic threshold expression,validated by Monte Carlo simulations.Using MMCA equations,we predict vaccination densities and analyze parameter effects on vaccination,disease transmission,and the epidemic threshold.Our findings suggest that promoting positive information,curbing the spread of negative information,enhancing vaccine effectiveness,and promptly identifying asymptomatic carriers can significantly increase vaccination rates,reduce epidemic spread,and raise the epidemic threshold.展开更多
The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of d...The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.展开更多
Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increa...Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.展开更多
Accelerated forgetting has been identified as a feature of Alzheimer's disease(AD),but the therapeutic efficacy of the manipulation of biological mechanisms of forgetting has not been assessed in AD animal models....Accelerated forgetting has been identified as a feature of Alzheimer's disease(AD),but the therapeutic efficacy of the manipulation of biological mechanisms of forgetting has not been assessed in AD animal models.Ras-re-lated C3 botulinum toxin substrate 1(Rac1),a small GTPase,has been shown to regulate active forgetting in Drosophila and mice?Here,we showed that Rac1 activity is aberrantly elevated in the hippocampal tissues of AD patients and AD animal models.Moreover,amyloid-beta 42 could induce Rac1 activation in cultured cells.The elevation of Rac1 activity not only accelerated 6-hour spatial memory decay in 3-month-old APP/PS1 mice,but also significantly contributed to severe memory loss in aged APP/PS1 mice.A similar age-dependent Rac1 activity-based memory loss was also observed in an AD fly model.Moreover,inhibition of Rac1 activity could ameliorate cognitive defects and synaptic plasticity in AD animal models.Finally,two novel compounds,identified through behavioral screening of a randomly selected pool of brain permeable small molecules for their positive effect in rescuing memory loss in both fly and mouse models,were found to be capable of inhibiting Rac1 activity.Thus,multiple lines of evidence corroborate in supporting the idea that inhibition of Rac1 activity is effective for treating AD-related memory loss.展开更多
基金Associate Professor Hongzhuan Qiu for his valuable comments and suggestions in formula derivation and proofreading of this paper.
文摘To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is proposed.The process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting factors.The adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more adaptability.The intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency issues.Considering that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed recursively.Stability analysis of the proposed method is also conducted.The effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison experiments.The estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.
基金supported by the National Natural Science Foundation of China (No.U1960202).
文摘The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.
基金supported by National Key Research and Development Program of China(2020YFB0505803)National Key Research and Development Program of China(2016YFB0501700)。
文摘The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms with a sparse regularization term.In this paper,we propose a variable forgetting factor(VFF)PRLS algorithm with a sparse penalty,e.g.,l_(1)-norm,for sparse identification.To reduce the computation complexity of the proposed algorithm,a fast implementation method based on dichotomous coordinate descent(DCD)algorithm is also derived.Simulation results indicate superior performance of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(61803163,61991414,61873301)。
文摘In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output systems.The objective is to enhance parameter estimation performance under non-persistent excitation.The proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is received.Theoretical proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite bound.Moreover,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed algorithm.Numerical simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.
文摘Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predictive control algorithm(SPC). The new method uses model matching error to calculate the variable forgetting factor, and applies it to constructing Hankel data matrix.This makes the data represent the changes of system information better. For eliminating the steady state error, the derivation of the incremental control is made. Simulation results on a rotary kiln show that this control strategy has achieved a good control effect.
基金supported by the National Natural Science Foundation of China (Grant No. 70821061)the National Basic Research Program of China (Grant No. 2006CB705503)
文摘It is known that the commonly used NaSch cellular automaton （CA） model and its modifications can help explain the internal causes of the macro phenomena of traffic flow. However, the randomization probability of vehicle velocity used in these models is assumed to be an exogenous constant or a conditional constant, which cannot reflect the learning and forgetting behaviour of drivers with historical experiences. This paper further modifies the NaSch model by enabling the randomization probability to be adjusted on the bases of drivers＇ memory. The Markov properties of this modified model are discussed. Analytical and simulation results show that the traffic fundamental diagrams can be indeed improved when considering drivers＇ intelligent behaviour. Some new features of traffic are revealed by differently combining the model parameters representing learning and forgetting behaviour.
基金Project supported by the National Natural Science Foundation of China (No. 60272079), and the Hi-Tech Research and Development Program (863) of China (No. 2003AA123310)
文摘Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.
基金Supported by the National Natural Science Foundation of China(71473183,71503188)
文摘User interest is not static and changes dynamically. In the scenario of a search engine, this paper presents a personalized adaptive user interest prediction framework. It represents user interest as a topic distribution, captures every change of user interest in the history, and uses the changes to predict future individual user interest dynamically. More specifically, it first uses a personalized user interest representation model to infer user interest from queries in the user＇s history data using a topic model; then it presents a personalized user interest prediction model to capture the dynamic changes of user interest and to predict future user interest by leveraging the query submission time in the history data. Compared with the Interest Degree Multi-Stage Quantization Model, experiment results on an AOL Search Query Log query log show that our framework is more stable and effective in user interest prediction.
基金Sponsored by the China Association of Higher Education(Grant No.2018GCJZD11).
文摘The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they need.At this stage,there are a lot of intelligent tutoring systems.In these systems,studentslearning actions are tracked in real⁃time,and there are a lot of available data.From these data,personalized education that suits each student can be mined.To improve the quality of education,some models for predicting studentsnext practice have been produced,such as Bayesian Knowledge Tracing(BKT),Performance Factor Analysis(PFA),and Deep Knowledge Tracing(DKT)with the development of deep learning.However,the model only considers the knowledge component and correctness of the problem,ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system,the lag time of the previous interaction,the number of past attempts to a problem,and situations that students have forgotten the knowledge.Although some studies consider forgetting and rich information when modeling student knowledge,they often ignore student learning sequences.The main contribution of this paper is in two aspects.One is to transform the input into a position feature vector by introducing an auto⁃encoding network layer and to carry out multiple sets of bad political combinations.The other is to consider repeated time intervals,sequence time intervals,and the number of attempts to simulate forgetting behavior.This paper proposes an adaptive algorithm for the original DKT model.By using the stacked auto⁃encoder network,the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of studentslearning.The model proposed in this paper has been experimented on two public data sets to improve the original accuracy.
文摘As more and more benefits of forgetting have been found in recent studies,whether forgetting could promote individuals ability of creative problem solving remains a controversial debate.This article discusses the effect of two types of forgetting,the retrieval-induced forgetting(RIF)and the forgetting during incubation,in benefiting creative problem solving by introducing and analysing the relevant experiments.The results reveal that retrieval-induced forgetting only works when previous mental fixations occurred and the promotion varies when solving different types of problems.The level of RIF is irrelevant to the performance in solving closed-ended creative problems and high level of RIF even impairs the creativity when solving open-ended problems.And forgetting during incubation cannot explain the incubation effect.The spreading activation of relevant information or the unconscious work is more likely to be the possible reasons.In conclusion,the current article brings about the discussions about the work conditions and effects of forgetting in creative problem solving.
文摘This paper reports the outcomes of three vocabulary tests taken by 71 second-year undergraduates, discusses the possible effects of contextualized explanation of new words and Ebbinghaus Forgetting Curve on the vocabulary teaching and learning. The authors find that in a short duration there is a significant difference between the effect of bilingual （English ＆ Chinese） explanation and that of monolingual （Chinese） explanation on the students＇ recognition of English new words.
文摘This article examines the problem of individual and collective attempts at forgetting the traumatic past in Toni Morrison’s sixth novel Jazz(1992).More specifically,it emphasizes by selected examples psychological and social aspects of willful amnesia which can lend itself useful in helping traumatized(country)individuals to repress painful remembrances,heal mental wounds and build a new identity in a memory-free modern city.Analyzing Jazz’s narrative featuring Joe and Violet Trace,with a particular focus put on the expectations and experiences connected with their migration to and life in the City,the article explores via Paul Connerton’s ruminations on cultural forgetting in modern times-delineated in his book How Modernity Forgets(2009)-the mechanisms of intentional amnesia used in the process of recovering from personal and social traumas resulting from more recent(migration and urban life)and more time-distant(slavery and racism)ordeals.
文摘The Buried Giant by Kazuo Ishiguro begins with an elderly couple who start a quest for the past memory which disap pears under the spell of the she-dragon Querig.During,individual confrontations and collective revenges work together to disclose the dark secrets that have been withheld.At the same time,it probes into the problem of forgiveness:can forced forgetting enable individuals or collectives forget their dark history for either love or peace?Based on the analysis of the individual and collective memories embodied in the novel,the present paper by virtue of Paul Ricoeur’s theory of abuses of memory,especially forced forget ting exhumes Ishiguro’s critical attitude towards forced forgetting,which ignores the threatening elements of love and peace like vi olent revenge and betrayal.
文摘This paper reviews the theory of language attrition,which refers to the loss or degradation of second language skills due to lack of using the second language for a certain period of time.Although our country attaches great importance to English language teaching,most of college English majors use English far less frequently than that of Chinese in real life,which makes them easily influenced by language attrition.Therefore,it is of great significance for college English majors to improve the efficiency of English vocabulary memory from the perspective of language attrition combined with Forgetting.This thesis consists of three parts.Chapter one is an analysis the concept of language attrition and Forgetting.Chapter two describes and analyzes the existing problems in current vocabulary memory among the college English majors via a questionnaire survey.The final chapter puts forward some corresponding countermeasures to help college English majors get rid of the influence of language attrition on vocabulary learning.
基金supported by the National Science and Technology Major Project(2022ZD0120203).
文摘1 Introduction Unsupervised Federated Continual Learning(UFCL)is a new learning paradigm that embeds unsupervised representation techniques into the Federated Learning(FL)framework,which enables continuous training of a shared representation model without compromising individual participants’data privacy[1,2].However,the continuous learning process may cause catastrophic forgetting in the model,reducing generated representations’performance.
基金supported by the National Social Science Foundation of China(Grant Nos.21BGL217 and 22CGL050)the Philosophy and Social Science Fund of Education Department of Jiangsu Province(Grant No.2020SJA2346).
文摘Vaccination is critical for controlling infectious diseases,but negative vaccination information can lead to vaccine hesitancy.To study how the interplay between information diffusion and disease transmission impacts vaccination and epidemic spread,we propose a novel two-layer multiplex network model that integrates an unaware-acceptant-negative-unaware(UANU)information diffusion model with a susceptible-vaccinated-exposed-infected-susceptible(SVEIS)epidemiological framework.This model includes individual exposure and vaccination statuses,time-varying forgetting probabilities,and information conversion thresholds.Through the microscopic Markov chain approach(MMCA),we derive dynamic transition equations and the epidemic threshold expression,validated by Monte Carlo simulations.Using MMCA equations,we predict vaccination densities and analyze parameter effects on vaccination,disease transmission,and the epidemic threshold.Our findings suggest that promoting positive information,curbing the spread of negative information,enhancing vaccine effectiveness,and promptly identifying asymptomatic carriers can significantly increase vaccination rates,reduce epidemic spread,and raise the epidemic threshold.
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through project number RI-44-0833.
文摘The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R196)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.
文摘Accelerated forgetting has been identified as a feature of Alzheimer's disease(AD),but the therapeutic efficacy of the manipulation of biological mechanisms of forgetting has not been assessed in AD animal models.Ras-re-lated C3 botulinum toxin substrate 1(Rac1),a small GTPase,has been shown to regulate active forgetting in Drosophila and mice?Here,we showed that Rac1 activity is aberrantly elevated in the hippocampal tissues of AD patients and AD animal models.Moreover,amyloid-beta 42 could induce Rac1 activation in cultured cells.The elevation of Rac1 activity not only accelerated 6-hour spatial memory decay in 3-month-old APP/PS1 mice,but also significantly contributed to severe memory loss in aged APP/PS1 mice.A similar age-dependent Rac1 activity-based memory loss was also observed in an AD fly model.Moreover,inhibition of Rac1 activity could ameliorate cognitive defects and synaptic plasticity in AD animal models.Finally,two novel compounds,identified through behavioral screening of a randomly selected pool of brain permeable small molecules for their positive effect in rescuing memory loss in both fly and mouse models,were found to be capable of inhibiting Rac1 activity.Thus,multiple lines of evidence corroborate in supporting the idea that inhibition of Rac1 activity is effective for treating AD-related memory loss.
